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Raj A, Sipes BS, Verma P, Mathalon DH, Biswal B, Nagarajan S. Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.22.586305. [PMID: 38586057 PMCID: PMC10996488 DOI: 10.1101/2024.03.22.586305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, and Graduate Program in Bio-engineering, University of California, San Francisco, San Francisco, CA 94143
| | - Benjamin S Sipes
- Department of Radiology and Biomedical Imaging, and Graduate Program in Bio-engineering, University of California, San Francisco, San Francisco, CA 94143
| | - Parul Verma
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, UCSF, University of California, San Francisco, and Veterans Affairs San Francisco Health Care System, San Francisco, CA 94121
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ 07102
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, and Graduate Program in Bio-engineering, University of California, San Francisco, San Francisco, CA 94143
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Gupta P, Shukla DP. Implications of Russia-Ukraine war on land surface temperature and air quality: long-term and short-term analysis. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024:10.1007/s11356-024-32800-5. [PMID: 38503957 DOI: 10.1007/s11356-024-32800-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 03/03/2024] [Indexed: 03/21/2024]
Abstract
The world is currently witnessing the military operations of Russia invading Ukraine, leading to missile bombing and shelling on various parts. Although the economic ill effects are more conspicuous and much talked about, the environmental impacts are grimmer and more devastating but ironically are less in the news. Hence, in this work, we focused on the environmental impact of the Russia-Ukraine war by quantifying the long-term (2001 to 2023) and short-term temperature changes using land surface temperature (LST) and air temperature (AT) as proxies and monitoring changes in air quality, mainly methane (CH4), carbon monoxide (CO) and carbon dioxide (CO2), between 2021 and 2022. We used NASA MODIS FIRMS fire points from 24th February 2022 to 08th September 2023 to prepare the heat map for identifying the regions heavily devastated by bombing. Thus, parts of Kiev, Lviv, Luhansk, Odesa, Donetsk, Kherson, etc., in Ukraine were chosen for assessing the LST, AT variations during the peak season of war along with analysis of long-term and short-term changes. We used MODIS Terra LST and Emissivity, ERA 5 AT, CH4, CO2 from AIRS and CO from Sentinel 5P. The results of the LST showed an average increase of around 2.32 °C (2022-2023), 3.44 °C (2021 and 2022) in parts of Ukraine and an increase of about 2 °C from COVID time, whilst a decrease of about 1 °C during COVID. This increase in LST will cause enhanced warming, thus changing the regional climate in a shorter time frame. A consistent upward trend in CH4, CO and CO2 is seen from 2019 to 2023. These heat waves and pollution will grip Ukraine and cause menace due to the cumulative effect of heat waves, changing climate and the aftermaths of war. This would be catastrophic as it might lead to a widespread impact on human health, agricultural yield and infrastructure, to name a few.
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Affiliation(s)
- Priyanka Gupta
- DExtER Lab, School of Civil and Environmental Engineering, North Campus, IIT Mandi, A-11 Building, Mandi, 175005, India
| | - Dericks Praise Shukla
- DExtER Lab, School of Civil and Environmental Engineering, North Campus, IIT Mandi, A-11 Building, Mandi, 175005, India.
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Tenorio FA, Rattalino Edreira JI, Monzon JP, Aramburu-Merlos F, Dobermann A, Gruere A, Brihet JM, Gayo S, Conley S, Mourtzinis S, Mashingaidze N, Sananka A, Aston S, Ojeda JJ, Grassini P. Filling the agronomic data gap through a minimum data collection approach. FIELD CROPS RESEARCH 2024; 308:109278. [PMID: 38495465 PMCID: PMC10933791 DOI: 10.1016/j.fcr.2024.109278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 01/12/2024] [Accepted: 01/24/2024] [Indexed: 03/19/2024]
Abstract
Context Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist. Objectives Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas. Methods The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country. Results Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations. Conclusions The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems. Significance The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.
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Affiliation(s)
- Fatima A.M. Tenorio
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583-0915, USA
| | - Juan I. Rattalino Edreira
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583-0915, USA
| | - Juan Pablo Monzon
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583-0915, USA
| | - Fernando Aramburu-Merlos
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583-0915, USA
| | - Achim Dobermann
- International Fertilizer Association, 49 Avenue d′lena, 75116 Paris, France
| | - Armelle Gruere
- International Fertilizer Association, 49 Avenue d′lena, 75116 Paris, France
| | - Juan Martin Brihet
- Department of Technological Prospective and Research, Buenos Aires Grain Exchange, Buenos Aires, Argentina
| | - Sofia Gayo
- Department of Technological Prospective and Research, Buenos Aires Grain Exchange, Buenos Aires, Argentina
| | - Shawn Conley
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Spyridon Mourtzinis
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | | | | | | | - Patricio Grassini
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE 68583-0915, USA
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Park JS, Fadnavis S, Garyfallidis E. Multi-scale V-net architecture with deep feature CRF layers for brain extraction. COMMUNICATIONS MEDICINE 2024; 4:29. [PMID: 38396078 PMCID: PMC10891085 DOI: 10.1038/s43856-024-00452-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. METHODS We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. RESULTS Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. CONCLUSIONS Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
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Affiliation(s)
- Jong Sung Park
- Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA.
| | - Shreyas Fadnavis
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Zurqani HA. The first generation of a regional-scale 1-m forest canopy cover dataset using machine learning and google earth engine cloud computing platform: A case study of Arkansas, USA. Data Brief 2024; 52:109986. [PMID: 38293581 PMCID: PMC10827392 DOI: 10.1016/j.dib.2023.109986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/26/2023] [Accepted: 12/14/2023] [Indexed: 02/01/2024] Open
Abstract
Forest canopy cover (FCC) is essential in forest assessment and management, affecting ecosystem services such as carbon sequestration, wildlife habitat, and water regulation. Ongoing advancements in techniques for accurately and efficiently mapping and extracting FCC information require a thorough evaluation of their validity and reliability. The primary objectives of this study are to: (1) create a large-scale forest FCC dataset with a 1-meter spatial resolution, (2) assess the regional spatial distribution of FCC at a regional scale, and (3) investigate differences in FCC areas among the Global Forest Change (Hansen et al., 2013) and U.S. Forest Service Tree Canopy Cover products at various spatial scales in Arkansas (i.e., county and city levels). This study utilized high-resolution aerial imagery and a machine learning algorithm processed and analyzed using the Google Earth Engine cloud computing platform to produce the FCC dataset. The accuracy of this dataset was validated using one-third of the reference locations obtained from the Global Forest Change (Hansen et al., 2013) dataset and the National Agriculture Imagery Program (NAIP) aerial imagery with a 0.6-m spatial resolution. The results showed that the dataset successfully identified FCC at a 1-m resolution in the study area, with overall accuracy ranging between 83.31% and 94.35% per county. Spatial comparison results between the produced FCC dataset and the Hansen et al., 2013 and USFS products indicated a strong positive correlation, with R2 values ranging between 0.94 and 0.98 for county and city levels. This dataset provides valuable information for monitoring, forecasting, and managing forest resources in Arkansas and beyond. The methodology followed in this study enhances efficiency, cost-effectiveness, and scalability, as it enables the processing of large-scale datasets with high computational demands in a cloud-based environment. It also demonstrates that machine learning and cloud computing technologies can generate high-resolution forest cover datasets, which might be helpful in other regions of the world.
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Affiliation(s)
- Hamdi A. Zurqani
- University of Arkansas Division of Agriculture, Arkansas Forest Resources Center, University of Arkansas System, 110 University Court, Monticello, AR 71656, USA
- College of Forestry, Agriculture, and Natural Resources, University of Arkansas at Monticello, 110 University Court, Monticello, AR 71656, USA
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Barbosa JMG, Shokry E, Caetano David L, Pereira NZ, da Silva AR, de Oliveira VF, Fioravanti MCS, da Cunha PHJ, de Oliveira AE, Antoniosi Filho NR. Cancer evaluation in dogs using cerumen as a source for volatile biomarker prospection. Mol Omics 2024; 20:27-36. [PMID: 37751172 DOI: 10.1039/d3mo00147d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
Cancer is one of the deadliest diseases in humans and dogs. Nevertheless, most tumor types spread faster in canines, and early cancer detection methods are necessary to enhance animal survival. Here, cerumen (earwax) was tested as a source of potential biomarkers for cancer evaluation in dogs. Earwax samples from dogs were collected from tumor-bearing and clinically healthy dogs, followed by Headspace/Gas Chromatography-Mass Spectrometry (HS/GC-MS) analyses and multivariate statistical workflow. An evolutionary-based multivariate algorithm selected 18 out of 128 volatile metabolites as a potential cancer biomarker panel in dogs. The candidate biomarkers showed a full discrimination pattern between tumor-bearing dogs and cancer-free canines with high accuracy in the test dataset: an accuracy of 95.0% (75.1-99.9), and sensitivity and specificity of 100.0% and 92.9%, respectively. In summary, this work raises a new perspective on cancer diagnosis in dogs, being carried out painlessly and non-invasive, facilitating sample collection and periodic application in a veterinary routine.
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Affiliation(s)
- João Marcos G Barbosa
- Laboratório de Métodos de Extração e Separação, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil.
| | - Engy Shokry
- Laboratório de Métodos de Extração e Separação, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil.
| | - Lurian Caetano David
- Laboratório de Métodos de Extração e Separação, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil.
| | - Naiara Z Pereira
- Laboratório de Métodos de Extração e Separação, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil.
| | - Adriana R da Silva
- Hospital Veterinário - Escola de Veterinária e Zootecnia da UFG, Rodovia Goiânia - Nova Veneza, km 8 Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil
| | - Vilma F de Oliveira
- Hospital Veterinário - Escola de Veterinária e Zootecnia da UFG, Rodovia Goiânia - Nova Veneza, km 8 Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil
| | - Maria Clorinda S Fioravanti
- Hospital Veterinário - Escola de Veterinária e Zootecnia da UFG, Rodovia Goiânia - Nova Veneza, km 8 Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil
| | - Paulo H Jorge da Cunha
- Hospital Veterinário - Escola de Veterinária e Zootecnia da UFG, Rodovia Goiânia - Nova Veneza, km 8 Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil
| | - Anselmo E de Oliveira
- Laboratório de Química Teórica e Computacional, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil
| | - Nelson Roberto Antoniosi Filho
- Laboratório de Métodos de Extração e Separação, Instituto de Química, Universidade Federal de Goiás (UFG), Campus II - Samambaia, 74690-900, Goiânia, GO, Brazil.
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Jin L, Shi HY, Li T, Zhao N, Xu Y, Xiao TW, Song F, Ma CX, Li QM, Lin LX, Shao XN, Li BH, Mi XC, Ren HB, Qiao XJ, Lian JY, Du H, Ge XJ. A DNA barcode library for woody plants in tropical and subtropical China. Sci Data 2023; 10:819. [PMID: 37993453 PMCID: PMC10665436 DOI: 10.1038/s41597-023-02742-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/10/2023] [Indexed: 11/24/2023] Open
Abstract
The application of DNA barcoding has been significantly limited by the scarcity of reliable specimens and inadequate coverage and replication across all species. The deficiency of DNA barcode reference coverage is particularly striking for highly biodiverse subtropical and tropical regions. In this study, we present a comprehensive barcode library for woody plants in tropical and subtropical China. Our dataset includes a standard barcode library comprising the four most widely used barcodes (rbcL, matK, ITS, and ITS2) for 2,520 species from 4,654 samples across 49 orders, 144 families, and 693 genera, along with 79 samples identified at the genus level. This dataset also provides a super-barcode library consisting of 1,239 samples from 1,139 species, 411 genera, 113 families, and 40 orders. This newly developed library will serve as a valuable resource for DNA barcoding research in tropical and subtropical China and bordering countries, enable more accurate species identification, and contribute to the conservation and management of tropical and subtropical forests.
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Affiliation(s)
- Lu Jin
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Hao-You Shi
- Central South Academy of Inventory and Planning of NFGA, Changsha, 410014, China
| | - Ting Li
- Yiyang Forestry Bureau, Yiyang, 413000, China
| | - Nan Zhao
- Hunan Police Academy, Changsha, 410138, China
| | - Yong Xu
- Conghua Middle School, Guangzhou, 510900, China
| | - Tian-Wen Xiao
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Feng Song
- College of Forestry, Central South University of Forestry & Technology, Changsha, 410004, China
| | - Chen-Xin Ma
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Qiao-Ming Li
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650201, China
| | - Lu-Xiang Lin
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650201, China
| | - Xiao-Na Shao
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, 650201, China
| | - Bu-Hang Li
- School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275, China
| | - Xiang-Cheng Mi
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Hai-Bao Ren
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China
| | - Xiu-Juan Qiao
- Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, China
- Center of Conservation Biology, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan, 430074, China
| | - Ju-Yu Lian
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China
- Center of Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou, 510650, China
| | - Hu Du
- Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, Hunan, 410125, China
| | - Xue-Jun Ge
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China.
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Knab F, Koch SP, Major S, Farr TD, Mueller S, Euskirchen P, Eggers M, Kuffner MT, Walter J, Berchtold D, Knauss S, Dreier JP, Meisel A, Endres M, Dirnagl U, Wenger N, Hoffmann CJ, Boehm-Sturm P, Harms C. Prediction of Stroke Outcome in Mice Based on Noninvasive MRI and Behavioral Testing. Stroke 2023; 54:2895-2905. [PMID: 37746704 PMCID: PMC10589430 DOI: 10.1161/strokeaha.123.043897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/06/2023] [Accepted: 08/02/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Prediction of poststroke outcome using the degree of subacute deficit or magnetic resonance imaging is well studied in humans. While mice are the most commonly used animals in preclinical stroke research, systematic analysis of outcome predictors is lacking. METHODS We intended to incorporate heterogeneity into our retrospective study to broaden the applicability of our findings and prediction tools. We therefore analyzed the effect of 30, 45, and 60 minutes of arterial occlusion on the variance of stroke volumes. Next, we built a heterogeneous cohort of 215 mice using data from 15 studies that included 45 minutes of middle cerebral artery occlusion and various genotypes. Motor function was measured using a modified protocol for the staircase test of skilled reaching. Phases of subacute and residual deficit were defined. Magnetic resonance images of stroke lesions were coregistered on the Allen Mouse Brain Atlas to characterize stroke topology. Different random forest prediction models that either used motor-functional deficit or imaging parameters were generated for the subacute and residual deficits. RESULTS Variance of stroke volumes was increased by 45 minutes of arterial occlusion compared with 60 minutes. The inclusion of various genotypes enhanced heterogeneity further. We detected both a subacute and residual motor-functional deficit after stroke in mice and different recovery trajectories could be observed. In mice with small cortical lesions, lesion volume was the best predictor of the subacute deficit. The residual deficit could be predicted most accurately by the degree of the subacute deficit. When using imaging parameters for the prediction of the residual deficit, including information about the lesion topology increased prediction accuracy. A subset of anatomic regions within the ischemic lesion had particular impact on the prediction of long-term outcomes. Prediction accuracy depended on the degree of functional impairment. CONCLUSIONS For the first time, we developed and validated a robust tool for the prediction of functional outcomes after experimental stroke in mice using a large and genetically heterogeneous cohort. These results are discussed in light of study design and imaging limitations. In the future, using outcome prediction can improve the design of preclinical studies and guide intervention decisions.
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Affiliation(s)
- Felix Knab
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Stefan Paul Koch
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Sebastian Major
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Tracy D. Farr
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
- School of Life Sciences, University of Nottingham, United Kingdom (T.D.F.)
| | - Susanne Mueller
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Philipp Euskirchen
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Moritz Eggers
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Melanie T.C. Kuffner
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Josefine Walter
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, QUEST Center for Transforming Biomedical Research, Germany (J.W.)
| | - Daniel Berchtold
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Samuel Knauss
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
| | - Jens P. Dreier
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- Bernstein Center for Computational Neuroscience (J.P.D.)
| | - Andreas Meisel
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
| | - Matthias Endres
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany (M. Endres., U.D.)
- German Center for Neurodegenerative Diseases (DZNE) (M. Endres, U.D.)
| | - Ulrich Dirnagl
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
- NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany (M. Endres., U.D.)
- German Center for Neurodegenerative Diseases (DZNE) (M. Endres, U.D.)
| | - Nikolaus Wenger
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
| | - Christian J. Hoffmann
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
| | - Philipp Boehm-Sturm
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Cluster of Excellence and Charité Core Facility, 7T Experimental MRIs, Germany (S.P.K., T.D.F., S. Mueller, P.B.-S.)
| | - Christoph Harms
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Germany (F.K., S.P.K., S. Major, T.D.F., S. Mueller, P.E., M. Eggers, M.T.C.K., J.W., D.B., S.K., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Charité Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, Germany (F.K., S.P.K., S. Major, S. Mueller., M. Eggers, M.T.C.K., J.W., D.B., J.P.D., A.M., M. Endres, U.D., N.W., C.J.H., P.B.-S., C.H.)
- Berlin Institute of Health (BIH), Germany (S.K., N.W., C.J.H., C.H.)
- Einstein Center for Neuroscience, Berlin, Germany (J.P.D., M. Endres, U.D., N.W., C.H.)
- German Center for Cardiovascular Research (DZHK), partner site Berlin (M. Endres, U.D., C.H.)
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9
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Royer J, Larivière S, Rodriguez-Cruces R, Cabalo DG, Tavakol S, Auer H, Ngo A, Park BY, Paquola C, Smallwood J, Jefferies E, Caciagli L, Bernasconi A, Bernasconi N, Frauscher B, Bernhardt BC. Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy. Brain 2023; 146:3923-3937. [PMID: 37082950 PMCID: PMC10473569 DOI: 10.1093/brain/awad125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/21/2023] [Accepted: 03/23/2023] [Indexed: 04/22/2023] Open
Abstract
Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a reorganization of this gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post-mortem dataset, we observed that in vivo findings reflected topographical variations in cortical cytoarchitecture. We indeed found that macroscale changes in microstructural differentiation in TLE reflected increased similarity of paralimbic and primary sensory/motor regions. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm and correlated with interindividual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a structurally-grounded explanation for large-scale functional network reorganization and cognitive dysfunction characteristic of TLE.
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Affiliation(s)
- Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Donna Gift Cabalo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Hans Auer
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Alexander Ngo
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Bo-yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Data Science, Inha University, Incheon 22212, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
| | - Casey Paquola
- Multiscale Neuroanatomy Lab, INM-1, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Jonathan Smallwood
- Department of Psychology, Queen’s University, Kingston, ON, K7L 3N6, Canada
| | | | - Lorenzo Caciagli
- Department of Bioengineering, University of Pennsylvania, Philadelphia, MA 19104, USA
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Birgit Frauscher
- Analytical Neurophysiology Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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10
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García-de-Villa S, Neira GGV, Álvarez MN, Huertas-Hoyas E, Ruiz LR, Del-Ama AJ, Sánchez MCR, Jiménez AR. A database with frailty, functional and inertial gait metrics for the research of fall causes in older adults. Sci Data 2023; 10:566. [PMID: 37626053 PMCID: PMC10457385 DOI: 10.1038/s41597-023-02428-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 07/28/2023] [Indexed: 08/27/2023] Open
Abstract
The GSTRIDE database contains information of the health status assessment of 163 elderly adults. We provide socio-demographic data, functional and frailty variables, and the outcomes from tests commonly performed for the evaluation of elder people. The database contains gait parameters estimated from the measurements of an Inertial Measurement Unit (IMU) placed on the foot of volunteers. These parameters include the total walking distance, the number of strides and multiple spatio-temporal gait parameters, such as stride length, stride time, speed, foot angles and clearance, among others. The main processed database is stored, apart from MS Excel, in CSV format to ensure their usability. The database is complemented with the raw IMU recordings in TXT format, in order to let researchers test other algorithms of gait analysis. We include the Python programming codes as a base to reproduce or modify them. The database stores data to study the frailty-related parameters that distinguish faller and non-faller populations, and analyze the gait-related parameters in the frail subjects, which are essential topics for the elderly.
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Grants
- FUNDACIÓN MAPFRE "Ayudas a la investigación de Ignacio H. de Larramendi, 2020"
- Spanish Ministry of Science, Grant No. MICROCEBUS RTI2018-095168-B-C55 (MCIU/AEI/FEDER, UE) and NEXTPERCEPTION European Union project funded by ECSEL Joint Undertaking (JU), under grant agreement No.876487 (ECSEL-2019-2-RIA), which receives support from the European Union’s Horizon 2020 research and innovation programm and Finland, Spain (MCIN/AEI PCI2020-112040), Italy, Germany, Czech Republic, Belgium, Netherlands.
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Affiliation(s)
- Sara García-de-Villa
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain.
- Universidad Rey Juan Carlos, Department of Signal Theory and Communications, Madrid, 28942, Spain.
| | | | - Marta Neira Álvarez
- Foundation for Research and Biomedical Innovation of the Infanta Sofía Hospital (HUIS), Department of Geriatrics, Madrid, 28702, Spain
| | - Elisabet Huertas-Hoyas
- Universidad Rey Juan Carlos, Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Madrid, 28922, Spain
| | - Luisa Ruiz Ruiz
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain
- Universidad de Alcalá, PhD Student, Alcalá de Henares, 28801, Madrid, Spain
| | - Antonio J Del-Ama
- Universidad Rey Juan Carlos, Electronic Technology Area, Madrid, 28933, Spain
| | | | - Antonio R Jiménez
- Spanish National Research Council, Centre for Automation and Robotics (CSIC-UPM), 28500, Arganda del Rey, Spain
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11
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Zhang Z, Liu Q, Lee CK, Hsieh CY, Chen E. An equivariant generative framework for molecular graph-structure Co-design. Chem Sci 2023; 14:8380-8392. [PMID: 37564414 PMCID: PMC10411624 DOI: 10.1039/d3sc02538a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/05/2023] [Indexed: 08/12/2023] Open
Abstract
Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% validity) and diverse (98.75% uniqueness) molecular graphs/structures with desirable properties, but also generates drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode's potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.
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Affiliation(s)
- Zaixi Zhang
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
| | - Qi Liu
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
| | | | - Chang-Yu Hsieh
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University Hangzhou Zhejiang 310058 China
| | - Enhong Chen
- Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China Hefei Anhui 230026 China
- State Key Laboratory of Cognitive Intelligence Hefei Anhui 230088 China
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12
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Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning. Biomed Signal Process Control 2023; 85:104855. [PMID: 36987448 PMCID: PMC10036214 DOI: 10.1016/j.bspc.2023.104855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 03/04/2023] [Accepted: 03/11/2023] [Indexed: 03/26/2023]
Abstract
Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification.
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13
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Chen X, Affourtit J, Ryskin R, Regev TI, Norman-Haignere S, Jouravlev O, Malik-Moraleda S, Kean H, Varley R, Fedorenko E. The human language system, including its inferior frontal component in "Broca's area," does not support music perception. Cereb Cortex 2023; 33:7904-7929. [PMID: 37005063 PMCID: PMC10505454 DOI: 10.1093/cercor/bhad087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 04/04/2023] Open
Abstract
Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within "Broca's area." However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across 4 experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions' responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by nonmusic auditory conditions, like animal sounds. Furthermore, the language regions are not sensitive to music structure: they show low responses to both intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia, who cannot judge sentence grammaticality, perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to process music, including music syntax.
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Affiliation(s)
- Xuanyi Chen
- Department of Cognitive Sciences, Rice University, TX 77005, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rachel Ryskin
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive & Information Sciences, University of California, Merced, Merced, CA 95343, United States
| | - Tamar I Regev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Samuel Norman-Haignere
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Hope Kean
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rosemary Varley
- Psychology & Language Sciences, UCL, London, WCN1 1PF, United Kingdom
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
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14
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Tang J, Fan K, Xie W, Zeng L, Han F, Huang G, Wang T, Liu A, Zhang S. A Semi-supervised Sensing Rate Learning based CMAB scheme to combat COVID-19 by trustful data collection in the crowd. COMPUTER COMMUNICATIONS 2023; 206:85-100. [PMID: 37197296 PMCID: PMC10171893 DOI: 10.1016/j.comcom.2023.04.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/02/2023] [Accepted: 04/22/2023] [Indexed: 05/19/2023]
Abstract
The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, or assume that the platform knows the qualities of workers once it receives their collected data. In reality, to reduce costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform, which is called False data attacks. And it is very hard for the platform to evaluate the authenticity of the received data In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem and design an UCB-based algorithm to separate the exploration and exploitation, regarding the Sensing Rates (SRs) of recruited workers as the gain of the bandit Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We theoretically prove that our SCMABA achieves truthfulness and individual rationality and exhibits outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.
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Affiliation(s)
- Jianheng Tang
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Kejia Fan
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Wenxuan Xie
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Luomin Zeng
- School of Civil Engineering, Central South University, Changsha, 410083, China
| | - Feijiang Han
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Guosheng Huang
- School of computer Science and Engineering, Hunan First Normal University, Changsha, 410205, China
| | - Tian Wang
- Department of Artificial Intelligence and Future Networks, Beijing Normal University & UIC, Zhuhai, Guangdong, China
| | - Anfeng Liu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Shaobo Zhang
- School of Computer Science and Engineering of the Hunan University of Science and Technology, Xiangtan, 411201, China
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15
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Fallah B, Russo E, Menz C, Hoffmann P, Didovets I, Hattermann FF. Anthropogenic influence on extreme temperature and precipitation in Central Asia. Sci Rep 2023; 13:6854. [PMID: 37100878 PMCID: PMC10133278 DOI: 10.1038/s41598-023-33921-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 04/20/2023] [Indexed: 04/28/2023] Open
Abstract
We investigate the contribution of anthropogenic forcing to the extreme temperature and precipitation events in Central Asia (CA) during the last 60 years. We bias-adjust and downscale two Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) ensemble outputs, with natural (labelled as hist-nat, driven only by solar and volcanic forcing) and natural plus anthropogenic forcing (labelled as hist, driven by all-forcings), to [Formula: see text] spatial resolution. Each ensemble contains six models from ISIMIP, based on the Coupled Model Inter-comparison Project phase 6 (CMIP6). The presented downscaling methodology is necessary to create a reliable climate state for regional climate impact studies. Our analysis shows a higher risk of extreme heat events (factor 4 in signal-to-noise ratio) over large parts of CA due to anthropogenic influence. Furthermore, a higher likelihood of extreme precipitation over CA, especially over Kyrgyzstan and Tajikistan, can be attributed to anthropogenic forcing (over 100[Formula: see text] changes in intensity and 20[Formula: see text] in frequency). Given that these regions show a high risk of rainfall-triggered landslides and floods during historical times, we report that human-induced climate warming can contribute to extreme precipitation events over vulnerable areas of CA. Our high-resolution data set can be used in impact studies focusing on the attribution of extreme events in CA and is freely available to the scientific community.
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Affiliation(s)
- Bijan Fallah
- Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, 14473, Potsdam, Brandenburg, Germany.
| | - Emmanuele Russo
- Institute for atmospheric and climate science, ETH Zürich, Universitätstrasse 16, 8092, Zürich, Switzerland
| | - Christoph Menz
- Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, 14473, Potsdam, Brandenburg, Germany
| | - Peter Hoffmann
- Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, 14473, Potsdam, Brandenburg, Germany
| | - Iulii Didovets
- Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, 14473, Potsdam, Brandenburg, Germany
| | - Fred F Hattermann
- Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A62, 14473, Potsdam, Brandenburg, Germany
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16
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Zhang Z, Chen M, Zhong T, Zhu R, Qian Z, Zhang F, Yang Y, Zhang K, Santi P, Wang K, Pu Y, Tian L, Lü G, Yan J. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nat Commun 2023; 14:2347. [PMID: 37095101 PMCID: PMC10126133 DOI: 10.1038/s41467-023-38079-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 04/14/2023] [Indexed: 04/26/2023] Open
Abstract
Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.
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Affiliation(s)
- Zhixin Zhang
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Min Chen
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
- International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094, China.
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China.
- Jiangsu Provincial Key Laboratory for NSLSCS, School of Mathematical Science, Nanjing Normal University, Nanjing, 210023, China.
| | - Teng Zhong
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
| | - Rui Zhu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, Singapore, 138632, Republic of Singapore
| | - Zhen Qian
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Fan Zhang
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yue Yang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Kai Zhang
- School of Geography, Nanjing Normal University, Nanjing, 210023, China
| | - Paolo Santi
- Senseable City Laboratory, Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Kaicun Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Yingxia Pu
- School of Geography and Ocean Science, Nanjing University, Nanjing, 210023, China
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
- Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, 210023, China
| | - Lixin Tian
- Research Institute of Carbon Neutralization Development, School of Mathematical Sciences, Jiangsu University, Zhenjiang, 212013, China
- Key Laboratory for NSLSCS, Ministry of Education, School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, China
| | - Guonian Lü
- Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, 210023, China.
- School of Geography, Nanjing Normal University, Nanjing, 210023, China.
- Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China.
| | - Jinyue Yan
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Future Energy Center, Mälardalen University, Västerås, 72123, Sweden.
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17
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Leihy RI, Peake L, Clarke DA, Chown SL, McGeoch MA. Introduced and invasive alien species of Antarctica and the Southern Ocean Islands. Sci Data 2023; 10:200. [PMID: 37041141 PMCID: PMC10090047 DOI: 10.1038/s41597-023-02113-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/28/2023] [Indexed: 04/13/2023] Open
Abstract
Open data on biological invasions are particularly critical in regions that are co-governed and/or where multiple independent parties have responsibility for preventing and controlling invasive alien species. The Antarctic is one such region where, in spite of multiple examples of invasion policy and management success, open, centralised data are not yet available. This dataset provides current and comprehensive information available on the identity, localities, establishment, eradication status, dates of introduction, habitat, and evidence of impact of known introduced and invasive alien species for the terrestrial and freshwater Antarctic and Southern Ocean region. It includes 3066 records for 1204 taxa and 36 individual localities. The evidence indicates that close to half of these species are not having an invasive impact, and that ~ 13% of records are of species considered locally invasive. The data are provided using current biodiversity and invasive alien species data and terminology standards. They provide a baseline for updating and maintaining the foundational knowledge needed to halt the rapidly growing risk of biological invasion in the region.
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Affiliation(s)
- Rachel I Leihy
- Securing Antarctica's Environmental Future, School of Biological Sciences, Monash University, Victoria, 3800, Australia.
- Arthur Rylah Institute for Environmental Research, Department of Energy, Environment, and Climate Action, Heidelberg, Victoria, 3084, Australia.
| | - Lou Peake
- Securing Antarctica's Environmental Future, Department of Environment and Genetics, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - David A Clarke
- Securing Antarctica's Environmental Future, Department of Environment and Genetics, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Steven L Chown
- Securing Antarctica's Environmental Future, School of Biological Sciences, Monash University, Victoria, 3800, Australia
| | - Melodie A McGeoch
- Securing Antarctica's Environmental Future, Department of Environment and Genetics, La Trobe University, Melbourne, Victoria, 3086, Australia
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18
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Tian J, Cheng Q, Xue R, Han Y, Shan Y. A dataset on corporate sustainability disclosure. Sci Data 2023; 10:182. [PMID: 37002227 PMCID: PMC10064614 DOI: 10.1038/s41597-023-02093-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
Enterprises, as key emitters, play a vital role in promoting sustainable development. Corporate sustainability disclosure provides a key channel for stakeholders to gain insights into a company's sustainability progress. However, few studies have been conducted to measure sustainability disclosure at the firm level. In this study, we apply the machine learning techniques to listed companies' management discussion and analysis (MD&A) documents and construct a dataset on corporate sustainability disclosure, including the Corporate Sustainability Disclosure Index (CSDI), CSDI_Economic Dimension (CSDI_ECO), CSDI_Environmental Dimension (CSDI_ENV), and CSDI_Social Dimension (CSDI_SOCI). The dataset will be updated annually. To the best of our knowledge, this is the first sustainability disclosure dataset constructed at the firm level. Our dataset reflects corporate managements' sustainability attitudes and promotes the implementation of corporate sustainability strategies and subsequent sustainable economic and social outcomes.
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Affiliation(s)
- Jinfang Tian
- Research Center for Statistics and Interdisciplinary Sciences | School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, 250014, China
| | - Qian Cheng
- Research Center for Statistics and Interdisciplinary Sciences | School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, 250014, China
| | - Rui Xue
- Centre for Corporate Sustainability and Environmental Finance, Department of Applied Finance, Macquarie University, Sydney, NSW, 2109, Australia.
| | - Yilong Han
- School of Economics and Management, Tongji University, Shanghai, 200092, China
| | - Yuli Shan
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
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19
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Käser S, Vazquez-Salazar LI, Meuwly M, Töpfer K. Neural network potentials for chemistry: concepts, applications and prospects. DIGITAL DISCOVERY 2023; 2:28-58. [PMID: 36798879 PMCID: PMC9923808 DOI: 10.1039/d2dd00102k] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.
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Affiliation(s)
- Silvan Käser
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | | | - Markus Meuwly
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
| | - Kai Töpfer
- Department of Chemistry, University of Basel Klingelbergstrasse 80 CH-4056 Basel Switzerland
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20
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Tong XY, Xia GS, Zhu XX. Enabling country-scale land cover mapping with meter-resolution satellite imagery. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING : OFFICIAL PUBLICATION OF THE INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING (ISPRS) 2023; 196:178-196. [PMID: 36824311 PMCID: PMC9939933 DOI: 10.1016/j.isprsjprs.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 06/18/2023]
Abstract
High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the complex land cover patterns, the costly training sample collections, and the severe distribution shifts of satellite imageries caused by, e.g., geographical differences or acquisition conditions, few studies have applied high-resolution images to land cover mapping in detailed categories at large scale. To fill this gap, we present a large-scale land cover dataset, Five-Billion-Pixels. It contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. In addition, we propose a deep-learning-based unsupervised domain adaptation approach that can transfer classification models trained on labeled dataset (referred to as the source domain) to unlabeled data (referred to as the target domain) for large-scale land cover mapping. Specifically, we introduce an end-to-end Siamese network employing dynamic pseudo-label assignment and class balancing strategy to perform adaptive domain joint learning. To validate the generalizability of our dataset and the proposed approach across different sensors and different geographical regions, we carry out land cover mapping on five megacities in China and six cities in other five Asian countries severally using: PlanetScope (3 m), Gaofen-1 (8 m), and Sentinel-2 (10 m) satellite images. Over a total study area of 60,000 km2, the experiments show promising results even though the input images are entirely unlabeled. The proposed approach, trained with the Five-Billion-Pixels dataset, enables high-quality and detailed land cover mapping across the whole country of China and some other Asian countries at meter-resolution.
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Affiliation(s)
- Xin-Yi Tong
- Remote Sensing Technology Institute, German Aerospace Center, Münchener Straße 20, Weßling 82234, Germany
| | - Gui-Song Xia
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
- National Engineering Research Center for Multi-media Software, School of Computer Science and Institute of Artificial Intelligence, Wuhan University, Wuhan 430072, China
| | - Xiao Xiang Zhu
- Chair of Data Science in Earth Observation, Technical University of Munich, Arcisstraße 21, Munich 80333, Germany
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21
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Henniges MC, Leitch AR, Leitch IJ. "BIFloraExplorer": A Taxonomic, Genetic, and Ecological Data Resource for the Vascular Plants of Britain and Ireland. Methods Mol Biol 2023; 2703:83-90. [PMID: 37646939 DOI: 10.1007/978-1-0716-3389-2_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
The vascular flora of Britain and Ireland is a historically well-documented and clearly delimited study system that offers itself to large-scale analyses of ecology and species assemblages. However, such analyses require clean, curated, and taxonomically resolved data, which are often unavailable. In this chapter, we describe how to access and use a key data resource that combines a taxonomically stable species list with genetic data (genome size, chromosome counts, and DNA barcode information), ecological information (such as life-form, realized niche description, and geographic origin) and distribution records. The data resource enables and encourages the study of natural ecological and evolutionary patterns and processes within the vascular flora of Britain and Ireland.
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Affiliation(s)
- Marie C Henniges
- Royal Botanic Gardens, Kew, Richmond, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Andrew R Leitch
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
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22
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Simon J, Bosch M, Blanché C. CromoCat: Chromosome Database of the Vascular Flora of the Catalan Countries-25 years. Methods Mol Biol 2023; 2703:131-160. [PMID: 37646943 DOI: 10.1007/978-1-0716-3389-2_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
CromoCat is a plant chromosome database that evolved from previous versions, as a repository of karyological information on the vascular flora of the Catalan Countries. CromoCat is designed as an independent database, managed by a team based at the University of Barcelona directed by J. Simon, available from its own webpage ( http://www.cromo.cat/ ) and from the Flora section of the Catalan Biodiversity Database - BDBC ( http://biodiver.bio.ub.es ). CromoCat contains at present (mid 2022) more than 68,000 records of karyological data belonging to more than 5000 taxa. A synthesis of the development of CromoCat, its functional system, achievements, limitations, and adopted solutions, during 25 years (1996-2021) and updated 2022, as well as the application to biodiversity conservation and management are outlined.
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Affiliation(s)
- Joan Simon
- BioC (GReB, IRBio) - Laboratori de Botànica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona, Barcelona, Catalonia, Spain.
| | - Maria Bosch
- BioC (GReB, IRBio) - Laboratori de Botànica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Cèsar Blanché
- BioC (GReB, IRBio) - Laboratori de Botànica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona, Barcelona, Catalonia, Spain
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23
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Ong SQ, Hamid SA. Next generation insect taxonomic classification by comparing different deep learning algorithms. PLoS One 2022; 17:e0279094. [PMID: 36584101 PMCID: PMC9803097 DOI: 10.1371/journal.pone.0279094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 11/30/2022] [Indexed: 12/31/2022] Open
Abstract
Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures. The results show that different taxonomic ranks require different deep learning (DL) algorithms to generate high-performance models, which indicates that the design of an automated systematic classification pipeline requires the integration of different algorithms. The InceptionV3 model has advantages over other models due to its high performance in distinguishing insect order and family, which is having F1-score of 0.75 and 0.79, respectively. Referring to the performance per class, Hemiptera (order), Rhiniidae (family), and Lucilia (genus) had the lowest performance, and we discuss the possible rationale and suggest future works to improve the generalization of a DL model for taxonomic rank classification.
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Affiliation(s)
- Song-Quan Ong
- Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
- * E-mail:
| | - Suhaila Ab. Hamid
- School of Biological Sciences, Universiti Sains Malaysia, Gelugor, Pulau Pinang, Malaysia
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24
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Udrescu M, Ardelean SM, Udrescu L. The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis. Gigascience 2022; 12:giad011. [PMID: 36892110 PMCID: PMC10023830 DOI: 10.1093/gigascience/giad011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/18/2022] [Accepted: 02/07/2023] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications. RESULTS We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves. CONCLUSIONS Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.
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Affiliation(s)
- Mihai Udrescu
- Department of Computer and Information Technology, Politehnica University of Timişoara, Timişoara 300223, Romania
| | - Sebastian Mihai Ardelean
- Department of Computer and Information Technology, Politehnica University of Timişoara, Timişoara 300223, Romania
| | - Lucreţia Udrescu
- Department I—Drug Analysis, “Victor Babeş” University of Medicine and Pharmacy Timişoara, Timişoara 300041, Romania
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25
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Lino Ferreira da Silva Barros MH, Santos GL, de Almeida Rodrigues MG, Sampaio V, Lynn T, Endo PT. A Brazilian classified data set for prognosis of tuberculosis, between January 2001 and April 2020. Sci Data 2022; 9:771. [PMID: 36522386 PMCID: PMC9753864 DOI: 10.1038/s41597-022-01892-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022] Open
Abstract
After COVID-19, tuberculosis (TB) is the leading cause of death by an infectious disease in the world. This work presents a data set based on data collected from the Brazilian Information System for Notifiable Diseases (SINAN) for the period from January 2001 to April 2020 relating to patients diagnosed with tuberculosis in Brazil. The data from SINAN was pre-processed to generate a new data set with two distinct treatment outcome classes: CURED and DIED. The data set comprises 37 categorical attributes (including socio-demographic, clinical, and laboratory data) as well as the target class. There are 927,909 records of patients classified as CURED and 36,190 classified as DIED, totaling 964,099 records.
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Affiliation(s)
| | - Guto Leoni Santos
- Universidade Federal de Pernambuco (UFPE), Centro de Informática (CIn), Recife, 50740-560, Brazil
| | | | | | - Theo Lynn
- Dublin City University (DCU), Dublin, Ireland
| | - Patricia Takako Endo
- Universidade de Pernambuco (UPE), Programa de Pós-graduação em Engenharia de Computação (PPGEC), Recife, 50720-001, Brazil.
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26
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Kougioumoutzis K, Trigas P, Tsakiri M, Kokkoris IP, Koumoutsou E, Dimopoulos P, Tzanoudakis D, Iatrou G, Panitsa M. Climate and Land-Cover Change Impacts and Extinction Risk Assessment of Rare and Threatened Endemic Taxa of Chelmos-Vouraikos National Park (Peloponnese, Greece). PLANTS (BASEL, SWITZERLAND) 2022; 11:3548. [PMID: 36559660 PMCID: PMC9784511 DOI: 10.3390/plants11243548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 12/04/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
Chelmos-Vouraikos National Park is a floristic diversity and endemism hotspot in Greece and one of the main areas where Greek endemic taxa, preliminary assessed as critically endangered and threatened under the IUCN Criteria A and B, are mainly concentrated. The climate and land-cover change impacts on rare and endemic species distributions is more prominent in regional biodiversity hotspots. The main aims of the current study were: (a) to investigate how climate and land-cover change may alter the distribution of four single mountain endemics and three very rare Peloponnesian endemic taxa of the National Park via a species distribution modelling approach, and (b) to estimate the current and future extinction risk of the aforementioned taxa based on the IUCN Criteria A and B, in order to investigate the need for designing an effective plant micro-reserve network and to support decision making on spatial planning efforts and conservation research for a sustainable, integrated management. Most of the taxa analyzed are expected to continue to be considered as critically endangered based on both Criteria A and B under all land-cover/land-use scenarios, GCM/RCP and time-period combinations, while two, namely Alchemilla aroanica and Silene conglomeratica, are projected to become extinct in most future climate change scenarios. When land-cover/land-use data were included in the analyses, these negative effects were less pronounced. However, Silene conglomeratica, the rarest mountain endemic found in the study area, is still expected to face substantial range decline. Our results highlight the urgent need for the establishment of micro-reserves for these taxa.
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Affiliation(s)
| | - Panayiotis Trigas
- Laboratory of Systematic Botany, Department of Crop Science, Agricultural University of Athens, 11855 Athens, Greece
| | - Maria Tsakiri
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Ioannis P. Kokkoris
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Eleni Koumoutsou
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Panayotis Dimopoulos
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Dimitris Tzanoudakis
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Gregoris Iatrou
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
| | - Maria Panitsa
- Laboratory of Botany, Department of Biology, University of Patras, 26504 Patras, Greece
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27
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Timalsina H, Gyawali T, Ghimire S, Paudel SR. Potential application of enhanced phytoremediation for heavy metals treatment in Nepal. CHEMOSPHERE 2022; 306:135581. [PMID: 35798158 DOI: 10.1016/j.chemosphere.2022.135581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/20/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Heavy metals contamination in soil and water resources is a great threat to developing countries because of the lack of waste treatment facilities. A majority of wastewater treatment methods are known to be expensive and out of reach for municipalities and small pollution treatment enterprises. Phytotechnology is a promising, sustainable, environment-friendly, and cost-effective technique for domestic and industrial wastewater treatment in places where land is available. However, interest in conventional remediation methods and the lack of information on recent advances in a significant portion of the society in developing countries have restrained the applications of phytoremediation. This review discusses the concept of phytoremediation, mechanisms of heavy metals removal by plants, and the potential application of enhanced phytoremediation technologies in developing countries like Nepal. The authors also review the commercially viable hyperaccumulator species with their native distribution, heavy metals intake capacity, and their availability in Nepal. Those native plants can be utilized locally or introduced strategically in other parts/countries as well. Thus, for a flora-rich country like Nepal, this study holds great potential and presents enhanced phytoremediation as an effective and sustainable strategy for the future.
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Affiliation(s)
- Haribansha Timalsina
- Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Lalitpur, 44700, Nepal; Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, USA
| | - Tunisha Gyawali
- Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Lalitpur, 44700, Nepal
| | - Swastik Ghimire
- Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Lalitpur, 44700, Nepal
| | - Shukra Raj Paudel
- Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Lalitpur, 44700, Nepal; Department of Environmental Engineering, College of Science and Technology, Korea University, Sejong, Republic of Korea.
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28
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Dehnen-Schmutz K, Pescott OL, Booy O, Walker KJ. Integrating expert knowledge at regional and national scales improves impact assessments of non-native species. NEOBIOTA 2022. [DOI: 10.3897/neobiota.77.89448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Knowledge of the impacts of invasive species is important for their management, prioritisation of control efforts and policy decisions. We investigated how British and Irish botanical experts assessed impacts at smaller scales in areas where they were familiar with the flora. Experts were asked to select the 10 plants that they considered were having the largest impacts in their areas. They also scored the local impacts of 10 plant species that had been previously scored to have the highest impacts at the scale of Great Britain. Impacts were scored using the modified classification scheme of the EICAT framework (Environmental Impact Classification for Alien Taxa). A total of 782 species/score combinations were received, of which 123 were non-native plants in 86 recording areas. Impatiens glandulifera, Reynoutria japonica and Rhododendron ponticum were the three species considered to have the highest impacts across all regions. Four of the species included in the list of the 10 highest impact species in Great Britain were also in the top 10 of species reported in our study. Species in the higher impact categories had, on average, a wider distribution than species with impacts categorised at lower levels. The main habitat types affected were woodlands, followed by linear/boundary features and freshwater habitats. Thirty-nine native plant species were reported to be negatively affected. In comparison to the overall non-native flora of Britain and Ireland, the lifeform spectrum of the species reported was significantly different, with higher percentages of aquatic plants and trees, but a lower proportion of annuals. The study demonstrates the value of local knowledge and expertise in identifying invasive species with negative impacts on the environment. Local knowledge is useful to both confirm national assessments and to identify species and impacts on native species and habitats that may not have gained national attention.
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29
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Witek J, Heindel JP, Guan X, Leven I, Hao H, Naullage P, LaCour A, Sami S, Menger MFSJ, Cofer-Shabica DV, Berquist E, Faraji S, Epifanovsky E, Head-Gordon T. M-Chem: a Modular Software Package for Molecular Simulation that Spans Scientific Domains. Mol Phys 2022; 121:e2129500. [PMID: 37470065 PMCID: PMC10353727 DOI: 10.1080/00268976.2022.2129500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/06/2022] [Indexed: 10/10/2022]
Abstract
We present a new software package called M-Chem that is designed from scratch in C++ and parallelized on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine that supports the evaluation of energies and forces from two-body to many-body all-atom potentials, reactive force fields, coarse-grained models, combined quantum mechanics molecular mechanics (QM/MM) models, and external force drivers from machine learning, augmented by algorithms that are focused on gains in computational simulation times. M-Chem also includes a range of standard simulation capabilities including thermostats, barostats, multi-timestepping, and periodic cells, as well as newer methods such as fast extended Lagrangians and high quality electrostatic potential generation. At present M-Chem is a developer friendly environment in which we encourage new software contributors from diverse fields to build their algorithms, models, and methods in our modular framework. The long-term objective of M-Chem is to create an interdisciplinary platform for computational methods with applications ranging from biomolecular simulations, reactive chemistry, to materials research.
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Affiliation(s)
- Jagna Witek
- Kenneth S. Pitzer Theory Center and Department of Chemistry
| | - Joseph P Heindel
- Kenneth S. Pitzer Theory Center and Department of Chemistry
- Chemical Sciences Division, Lawrence Berkeley National Laboratory
| | - Xingyi Guan
- Kenneth S. Pitzer Theory Center and Department of Chemistry
- Chemical Sciences Division, Lawrence Berkeley National Laboratory
| | - Itai Leven
- Kenneth S. Pitzer Theory Center and Department of Chemistry
| | - Hongxia Hao
- Kenneth S. Pitzer Theory Center and Department of Chemistry
| | | | - Allen LaCour
- Kenneth S. Pitzer Theory Center and Department of Chemistry
- Chemical Sciences Division, Lawrence Berkeley National Laboratory
| | - Selim Sami
- Kenneth S. Pitzer Theory Center and Department of Chemistry
| | - M F S J Menger
- Stratingh Institute for Chemistry, University of Groningen, 9747 AG Groningen, The Netherlands
| | - D Vale Cofer-Shabica
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA, 19128 USA
| | - Eric Berquist
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Shirin Faraji
- Stratingh Institute for Chemistry, University of Groningen, 9747 AG Groningen, The Netherlands
| | - Evgeny Epifanovsky
- Q-Chem, Inc., 6601 Owens Drive, Suite 105, Pleasanton, California 94588, USA
| | - Teresa Head-Gordon
- Kenneth S. Pitzer Theory Center and Department of Chemistry
- Chemical Sciences Division, Lawrence Berkeley National Laboratory
- Department of Bioengineering and Chemical and Biomolecular Engineering University of California, Berkeley, CA, USA
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30
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Baibakova V, Elzouka M, Lubner S, Prasher R, Jain A. Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction. Sci Data 2022; 9:589. [PMID: 36175557 PMCID: PMC9522672 DOI: 10.1038/s41597-022-01699-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/14/2022] [Indexed: 11/10/2022] Open
Abstract
Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile. Measurement(s) | optical emissivity • device design | Technology Type(s) | Fourier transform infrared microscopy • text mining |
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Affiliation(s)
- Viktoriia Baibakova
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA
| | - Mahmoud Elzouka
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA
| | - Sean Lubner
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA
| | - Ravi Prasher
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA
| | - Anubhav Jain
- Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA.
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31
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Jeanselme V, De-Arteaga M, Zhang Z, Barrett J, Tom B. Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2022; 193:12-34. [PMID: 36601036 PMCID: PMC7614014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Biases have marked medical history, leading to unequal care affecting marginalised groups. The patterns of missingness in observational data often reflect these group discrepancies, but the algorithmic fairness implications of group-specific missingness are not well understood. Despite its potential impact, imputation is too often an overlooked preprocessing step. When explicitly considered, attention is placed on overall performance, ignoring how this preprocessing can reinforce groupspecific inequities. Our work questions this choice by studying how imputation affects downstream algorithmic fairness. First, we provide a structured view of the relationship between clinical presence mechanisms and groupspecific missingness patterns. Then, through simulations and real-world experiments, we demonstrate that the imputation choice influences marginalised group performance and that no imputation strategy consistently reduces disparities. Importantly, our results show that current practices may endanger health equity as similarly performing imputation strategies at the population level can affect marginalised groups differently. Finally, we propose recommendations for mitigating inequities that may stem from a neglected step of the machine learning pipeline.
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Affiliation(s)
| | - Maria De-Arteaga
- McCombs School of Business, University of Texas at Austin, Austin, USA
| | - Zhe Zhang
- Rady School of Management, University of California, San Diego, USA
| | - Jessica Barrett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Brian Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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