Bat diversity boosts ecosystem services: Evidence from pine processionary moth predation

Coniferous forests contribute to the European economy; however, they have experienced a decline since the late 1990s due to an invasive pest known as the pine processionary moth, Thaumetopoea pityocampa. The impacts of this pest are increasingly exacerbated by climate change. Traditional control strategies involving pesticides have had negative effects on public health and the environment. Instead, forest managers seek a more ecological and sustainable approach to management that promotes the natural actions of pest control agents. This study aims to evaluate the role of bats in suppressing pine processionary moths in pine forests and examine how the bat community composition and abundance influence pest consumption. Bats were sampled in the mountainous environment of the Serra da Estrela in central Portugal to collect faecal samples for DNA meta-barcoding analysis. We assessed the relationship between a) bat richness, b) bat relative abundance, c) bat diet richness, and the frequency of pine processionary moth consumption. Our findings indicate that sites with the highest bat species richness and abundance exhibit the highest levels of pine processionary moth consumption. The intensity of pine processionary moth consumption is independent of insect diversity within the site. The highest occurrence of pine processionary moth presence in bat diets is primarily observed in species that forage in cluttered habitats. A typical predator of pine processionary moths among bats is likely to be a forest-dwelling species that specialises in consuming Lepidoptera. These species primarily use short-range echolocation calls, which are relatively inaudible to tympanate moths, suitable for locating prey in cluttered environments, employing a gleaning hunting strategy. Examples include species from the genera Plecotus, Myotis, and Rhinolophus. This study enhances our understanding of the potential pest consumption services provided by bats in pine forests. The insights gained from this research can inform integrated pest management practices in forestry.

Comparative assessment of synthetic time series generation approaches in healthcare: leveraging patient metadata for accurate data synthesis

BACKGROUND

Synthetic data is an emerging approach for addressing legal and regulatory concerns in biomedical research that deals with personal and clinical data, whether as a single tool or through its combination with other privacy enhancing technologies. Generating uncompromised synthetic data could significantly benefit external researchers performing secondary analyses by providing unlimited access to information while fulfilling pertinent regulations. However, the original data to be synthesized (e.g., data acquired in Living Labs) may consist of subjects' metadata (static) and a longitudinal component (set of time-dependent measurements), making it challenging to produce coherent synthetic counterparts.

METHODS

Three synthetic time series generation approaches were defined and compared in this work: only generating the metadata and coupling it with the real time series from the original data (A1), generating both metadata and time series separately to join them afterwards (A2), and jointly generating both metadata and time series (A3). The comparative assessment of the three approaches was carried out using two different synthetic data generation models: the Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). The experiments were performed with three different healthcare-related longitudinal datasets: Treadmill Maximal Effort Test (TMET) measurements from the University of Malaga (1), a hypotension subset derived from the MIMIC-III v1.4 database (2), and a lifelogging dataset named PMData (3).

RESULTS

Three pivotal dimensions were assessed on the generated synthetic data: resemblance to the original data (1), utility (2), and privacy level (3). The optimal approach fluctuates based on the assessed dimension and metric.

CONCLUSION

The initial characteristics of the datasets to be synthesized play a crucial role in determining the best approach. Coupling synthetic metadata with real time series (A1), as well as jointly generating synthetic time series and metadata (A3), are both competitive methods, while separately generating time series and metadata (A2) appears to perform more poorly overall.

Synthetic Tabular Data Evaluation in the Health Domain Covering Resemblance, Utility, and Privacy Dimensions

BACKGROUND

 Synthetic tabular data generation is a potentially valuable technology with great promise for data augmentation and privacy preservation. However, prior to adoption, an empirical assessment of generated synthetic tabular data is required across dimensions relevant to the target application to determine its efficacy. A lack of standardized and objective evaluation and benchmarking strategy for synthetic tabular data in the health domain has been found in the literature.

OBJECTIVE

 The aim of this paper is to identify key dimensions, per dimension metrics, and methods for evaluating synthetic tabular data generated with different techniques and configurations for health domain application development and to provide a strategy to orchestrate them.

METHODS

 Based on the literature, the resemblance, utility, and privacy dimensions have been prioritized, and a collection of metrics and methods for their evaluation are orchestrated into a complete evaluation pipeline. This way, a guided and comparative assessment of generated synthetic tabular data can be done, categorizing its quality into three categories ("Excellent," "Good," and "Poor"). Six health care-related datasets and four synthetic tabular data generation approaches have been chosen to conduct an analysis and evaluation to verify the utility of the proposed evaluation pipeline.

RESULTS

 The synthetic tabular data generated with the four selected approaches has maintained resemblance, utility, and privacy for most datasets and synthetic tabular data generation approach combination. In several datasets, some approaches have outperformed others, while in other datasets, more than one approach has yielded the same performance.

CONCLUSION

 The results have shown that the proposed pipeline can effectively be used to evaluate and benchmark the synthetic tabular data generated by various synthetic tabular data generation approaches. Therefore, this pipeline can support the scientific community in selecting the most suitable synthetic tabular data generation approaches for their data and application of interest.

Spatial characterization of the effect of age and sex on macular layer thicknesses and foveal pit morphology

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21-88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

INTRODUCTION

Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.

METHODS

Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia.

RESULTS

Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms.

CONCLUSIONS

The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

INTRODUCTION

Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.

METHODS

Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019. Validated random forest models were used to identify separately depressive and psychotic clusters according to their presence in the electronic health records of all patients diagnosed with dementia.

RESULTS

Among the 631,949 individuals over 60 years registered, 28,563 were diagnosed with dementia, of whom 15,828 (55.4%) showed psychotic symptoms and 19,461 (68.1%) depressive symptoms. The incidence of dementia in 2019 was 6.8/1000 person-years. Most incident cases of depressive (72.3%) and psychotic (51.9%) NPS occurred in cases of incident dementia. The risk of depressive-type NPS grows with years since dementia diagnosis, living in a nursing home, and female sex, but falls with older age. In the psychotic cluster model, the effects of male sex, and older age are inverted, both increasing the probability of this type of symptoms.

CONCLUSIONS

The stigmatization factor conditions the social and attitudinal environment, delaying the diagnosis of dementia, preventing patients from receiving adequate care and exacerbating families' suffering. This study evidences the synergy between big data and real-world data for psychiatric epidemiological research.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data

BACKGROUND

Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

OBJECTIVE

The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

METHODS

First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models.

RESULTS

Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age.

CONCLUSION

Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.

Aging Modulates the Resting Brain after a Memory Task: A Validation Study from Multivariate Models

Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140-160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.

A dust-enshrouded tidal disruption event with a resolved radio jet in a galaxy merger

Tidal disruption events (TDEs) are transient flares produced when a star is ripped apart by the gravitational field of a supermassive black hole (SMBH). We have observed a transient source in the western nucleus of the merging galaxy pair Arp 299 that radiated >1.5 × 10⁵² erg at infrared and radio wavelengths but was not luminous at optical or x-ray wavelengths. We interpret this as a TDE with much of its emission reradiated at infrared wavelengths by dust. Efficient reprocessing by dense gas and dust may explain the difference between theoretical predictions and observed luminosities of TDEs. The radio observations resolve an expanding and decelerating jet, probing the jet formation and evolution around a SMBH.

Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease

As members of an increasingly aging society, one of our major priorities is to develop tools to detect the earliest stage of age-related disorders such as Alzheimer's Disease (AD). The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavior data to detect the multimodal symptoms that are often found to be impaired in AD. After gathering longitudinal smart home data for 29 older adults over an average duration of 2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing ten behavioral features. Mobility, cognition, and mood were evaluated every six months. Using these data, we created regression models to predict symptoms as measured by the tests and a feature selection analysis was performed. Classification models were built to detect reliable absolute changes in the scores predicting symptoms and SmoteBOOST and wRACOG algorithms were used to overcome class imbalance where needed. Results show that all mobility, cognition, and depression symptoms can be predicted from activity-aware smart home data. Similarly, these data can be effectively used to predict reliable changes in mobility and memory skills. Results also suggest that not all behavioral features contribute equally to the prediction of every symptom. Future work therefore can improve model sensitivity by including additional longitudinal data and by further improving strategies to extract relevant features and address class imbalance. The results presented herein contribute toward the development of an early change detection system based on smart home technology.

Copy-number analysis identified new prognostic marker in acute myeloid leukemia

Recent advances in genomic technologies have revolutionized acute myeloid leukemia (AML) understanding by identifying potential novel actionable genomic alterations. Consequently, current risk stratification at diagnosis not only relies on cytogenetics, but also on the inclusion of several of these abnormalities. Despite this progress, AML remains a heterogeneous and complex malignancy with variable response to current therapy. Although copy-number alterations (CNAs) are accepted prognostic markers in cancers, large-scale genomic studies aiming at identifying specific prognostic CNA-based markers in AML are still lacking. Using 367 AML, we identified four recurrent CNA on chromosomes 11 and 21 that predicted outcome even after adjusting for standard prognostic risk factors and potentially delineated two new subclasses of AML with poor prognosis. ERG amplification, the most frequent CNA, was related to cytarabine resistance, a cornerstone drug of AML therapy. These findings were further validated in The Cancer Genome Atlas data. Our results demonstrate that specific CNA are of independent prognostic relevance, and provide new molecular information into the genomic basis of AML and cytarabine response. Finally, these CNA identified two potential novel risk groups of AML, which when confirmed prospectively, may improve the clinical risk stratification and potentially the AML outcome.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.

The mitogenome of a 35,000-year-old Homo sapiens from Europe supports a Palaeolithic back-migration to Africa

After the dispersal of modern humans (Homo sapiens) Out of Africa, hominins with a similar morphology to that of present-day humans initiated the gradual demographic expansion into Eurasia. The mitogenome (33-fold coverage) of the Peştera Muierii 1 individual (PM1) from Romania (35 ky cal BP) we present in this article corresponds fully to Homo sapiens, whilst exhibiting a mosaic of morphological features related to both modern humans and Neandertals. We have identified the PM1 mitogenome as a basal haplogroup U6*, not previously found in any ancient or present-day humans. The derived U6 haplotypes are predominantly found in present-day North-Western African populations. Concomitantly, those found in Europe have been attributed to recent gene-flow from North Africa. The presence of the basal haplogroup U6* in South East Europe (Romania) at 35 ky BP confirms a Eurasian origin of the U6 mitochondrial lineage. Consequently, we propose that the PM1 lineage is an offshoot to South East Europe that can be traced to the Early Upper Paleolithic back migration from Western Asia to North Africa, during which the U6 lineage diversified, until the emergence of the present-day U6 African lineages.

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.

Binding of AP-2 adaptor complex to brain membrane is regulated by phosphorylation of proteins

Phosphorylation of proteins appears as a key process in early steps of clathrin coated vesicle formation. Here, we report that treatment of post-nuclear fraction with alkaline phosphatase induced redistribution of alpha subunits of AP-2 adaptor complex to cytosol and this effect was higher in the alpha2 subunit. A high serine phosphorylation status of alpha subunits correlated with the higher affinity of AP-2 to membranes. Using a simple binding assay, where membranes were incubated with either purified adaptors or cytosols, we observed an inhibitory effect of tyrphostin, a tyrosine kinase inhibitor, on the binding of AP-2 to membranes, but also an unexpected decrease induced by the phosphatase inhibitor cyclosporine. We also show an inhibitory effect of ATP mediated by cytosolic proteins, although it could not be related to the phosphorylation of AP-2, suggesting an action upstream a cascade of phosphorylations that participate in the regulation of the assembly of AP-2 to membranes.

The calcium chelator BAPTA affects the binding of assembly protein AP-2 to membranes

Clathrin coated vesicles are involved in receptor-mediated transport. The coat of these vesicles is constituted mostly of clathrin and the assembly proteins AP-1 or AP-2. In the present study using an in vitro binding system, we found that the interaction of AP-2 but not AP-1 with membranes diminished when the calcium chelating agent BAPTA was added. The maximal inhibitory effect was observed with 10 mM of the chelating agent. Binding of AP-2 to membranes was recovered by adding calcium in a concentration-dependent fashion. Binding was also affected when the membranes were previously treated with BAPTA and then washed. However, other chelating agents such as EDTA or EGTA, as well as the zinc chelating TPEN, did not have any effect on the binding. From these results we postulate a role for calcium in regulating the assembly-disassembly cycle of adaptors in the formation of clathrin coated vesicles.

Expression of interferon-alpha subtypes in peripheral mononuclear cells from patients with chronic hepatitis C: a role for interferon-alpha5

Interferon (IFN)-alpha is a family of antiviral proteins encoded by different genes. The biological significance of the existence of various IFN-alpha subtypes is not clear. We have investigated the interferon system in chronic hepatitis C virus (HCV) infection, a disease that responds to interferon-alpha2 therapy in only a limited proportion of cases. We analysed the expression of interferon regulatory factor (IRF)-1, IRF-2, and IFN-alpha subtypes in nonstimulated and Sendai virus-stimulated peripheral blood mononuclear cells (PBMC) from HCV infected patients and healthy controls. We observed that the IRF-1 mRNA and IRF-1/IRF-2 ratios were increased in PBMC from hepatitis C patients with respect to normal subjects. Sendai virus stimulation of PBMC led to a significant increase in the levels of IRF-1, IRF-2 and IFN-alpha mRNAs and in the production of IFN-alpha protein with respect to basal values in healthy controls as well as in patients with HCV infection. In addition, we found that while natural HCV infection induced increased IFN-alpha5 expression in PBMC, in vitro infection of these cells with Sendai virus caused a raise in the expression of IFN-alpha8 in both patients and normal controls. In summary, our results indicate that virus-induced activation of the IFN system in human PBMC is associated with selective expression of individual IFN-alpha subtypes, IFN-alpha5 being the specific subtype induced in PBMC from patients with chronic HCV infection.

Flashing superluminal components in the jet of the radio galaxy 3C120

A 16-month sequence of radio images of the active galaxy 3C120 with the Very Long Baseline Array reveals a region in the relativistic jet where superluminal components flash on and off over time scales of months, while the polarization angle rotates. This can be explained by interaction between the jet and an interstellar cloud located about 8 parsecs from the center of the galaxy. The cloud, which rotates the polarization direction and possibly eclipses a section of the jet, represents a "missing link" between the ultradense broad-emission-line clouds closer to the center and the lower density narrow-emission-line clouds seen on kiloparsec scales.

[Parasitism in fishes and human riverside communities of the Huillinco and Natri lakes (Great Island of Chiloé), Chile]

In April and December 1989, 35 fish from Lake Huillinco (42 degrees 48'S, 74 degrees 02'W) and 36 fish from Lake Natri (42 degrees 48'S, 73 degrees 50'W), in the Great Island of Chiloé (Chile) were examined. Coprological samples from 159 persons, 17 dogs, 19 pigs and 4 cats from around both lakes were examined for Diphyllobothrium spp. infection. In the Lake Huillinco the following helminths of fishes were determined: Contracaecum sp. and Hysterothylacium sp. in Salmo trutta, Cauque mauleanum and Eleginops maclovinus; Dichelyne (Cucullanellus) dichelyneformis in S. trutta and E. maclovinus and Scolex pleuronectis in S. trutta. One specimen of Mugil cephalus did not show helminth parasites. Prevalence of infection were greater for Contracaecum sp. in S. trutta (75.0%) and C. mauleanum (76.0%); and Hysterothylacium sp. in E. maclovinus (75.0%). Mean intensity was higher for D. (C.) dichelyneformis in E. maclovinus. Contracaecum sp. in S. trutta, Oncorhynchus mykiss, Oncorhynchus kisutch and Galaxias maculatus; Acanthocephalus sp. in S. trutta and G. maculatus, S. pleuronectis in O. mykiss and Cystidicoloides sp. in G. maculatus were determined at Lake Natri. Prevalence and intensity of infection were higher for Contracaecum sp. in S. trutta and O. kisutch. Infection by Diphyllobothrium sp. was determined in one domestic cat. Prevalence of infection by intestinal protozoan and helminths in human population only showed significative differences for Ascaris lumbricoides and Trichuris trichiura that were higher in the Lake Huillinco. Importance of natural infection by helminth parasites for fish in cultured condition and possible mechanisms of infections in relation to the diet of fishes are discussed.