Clustering of the self-organizing map

Big data and machine learning to tackle diabetes management

BACKGROUND

Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity.

METHODS

In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D.

RESULTS

To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology.

CONCLUSION

Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

Forced vital capacity trajectories in patients with idiopathic pulmonary fibrosis: a secondary analysis of a multicentre, prospective, observational cohort

BACKGROUND

Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in forced vital capacity (FVC) is the main indicator of progression; however, missingness prevents long-term analysis of patterns in lung function. We aimed to identify distinct clusters of lung function trajectory among patients with idiopathic pulmonary fibrosis using machine learning techniques.

METHODS

We did a secondary analysis of longitudinal data on FVC collected from a cohort of patients with idiopathic pulmonary fibrosis from the PROFILE study; a multicentre, prospective, observational cohort study. We evaluated the imputation performance of conventional and machine learning techniques to impute missing data and then analysed the fully imputed dataset by unsupervised clustering using self-organising maps. We compared anthropometric features, genomic associations, serum biomarkers, and clinical outcomes between clusters. We also performed a replication of the analysis on data from a cohort of patients with idiopathic pulmonary fibrosis from an independent dataset, obtained from the Chicago Consortium.

FINDINGS

415 (71%) of 581 participants recruited into the PROFILE study were eligible for further analysis. An unsupervised machine learning algorithm had the lowest imputation error among tested methods, and self-organising maps identified four distinct clusters (1-4), which was confirmed by sensitivity analysis. Cluster 1 comprised 140 (34%) participants and was associated with a disease trajectory showing a linear decline in FVC over 3 years. Cluster 2 comprised 100 (24%) participants and was associated with a trajectory showing an initial improvement in FVC before subsequently decreasing. Cluster 3 comprised 113 (27%) participants and was associated with a trajectory showing an initial decline in FVC before subsequent stabilisation. Cluster 4 comprised 62 (15%) participants and was associated with a trajectory showing stable lung function. Median survival was shortest in cluster 1 (2·87 years [IQR 2·29-3·40]) and cluster 3 (2·23 years [1·75-3·84]), followed by cluster 2 (4·74 years [3·96-5·73]), and was longest in cluster 4 (5·56 years [5·18-6·62]). Baseline FEV1 to FVC ratio and concentrations of the biomarker SP-D were significantly higher in clusters 1 and 3. Similar lung function clusters with some shared anthropometric features were identified in the replication cohort.

INTERPRETATION

Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters might optimise evaluation of intervention efficacy during clinical trials and patient management.

FUNDING

National Institute for Health and Care Research, Medical Research Council, and GlaxoSmithKline.

The spatial and temporal reconstruction of a medieval moat ecosystem

Moats and other historical water features had great importance for past societies. The functioning of these ecosystems can now only be retrieved through palaeoecological studies. Here we aimed to reconstruct the history of a stronghold's moat during its period of operation. Our spatio-temporal approach allowed mapping of the habitat changes within a medieval moat for the first time. Using data from four cores of organic deposits taken within the moat system, we describe ecological states of the moat based on subfossil Chironomidae and Ceratopogonidae assemblages. We found that over half (57%) of the identified dipteran taxa were indicative of one of the following ecological states: limnetic conditions with or without periodic water inflow, or marshy conditions. Samples representing conditions unfavourable for aquatic insects were grouped in a separate cluster. Analyses revealed that the spatio-temporal distribution of midge assemblages depended mostly on depth differences and freshwater supply from an artificial channel. Paludification and terrestrialization did not happen simultaneously across the moat system, being greatly influenced by human activity. The results presented here demonstrate the importance of a multi-aspect approach in environmental archaeology, focusing not only on the human environment, but also on the complex ecology of the past ecosystems.

A data-driven spatially-specific vaccine allocation framework for COVID-19

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies.

Multi-omic Characterization of Pancreatic Ductal Adenocarcinoma Relates CXCR4 mRNA Expression Levels to Potential Clinical Targets

PURPOSE

Chemokines are essential for immune cell trafficking and are considered to have a major impact on the composition of the tumor microenvironment. CX-chemokine receptor 4 (CXCR4) is associated with poor differentiation, metastasis, and prognosis in pancreatic ductal adenocarcinoma (PDAC). This study provides a comprehensive molecular portrait of PDAC according to CXCR4 mRNA expression levels.

EXPERIMENTAL DESIGN

The Cancer Genome Atlas database was used to explore molecular and immunologic features associated with CXCR4 mRNA expression in PDAC. A large real-word dataset (n = 3,647) served for validation and further exploratory analyses. Single-cell RNA analyses on a publicly available dataset and in-house multiplex immunofluorescence (mIF) experiments were performed to elaborate cellular localization of CXCR4.

RESULTS

High CXCR4 mRNA expression (CXCR4high) was associated with increased infiltration of regulatory T cells, CD8+ T cells, and macrophages, and upregulation of several immune-related genes, including immune checkpoint transcripts (e.g., TIGIT, CD274, PDCD1). Analysis of the validation cohort confirmed the CXCR4-dependent immunologic TME composition in PDAC irrespective of microsatellite instability-high/mismatch repair-deficient or tumor mutational burden. Single-cell RNA analysis and mIF revealed that CXCR4 was mainly expressed by macrophages and T-cell subsets. Clinical relevance of our finding is supported by an improved survival of CXCR4high PDAC.

CONCLUSIONS

High intratumoral CXCR4 mRNA expression is linked to a T cell- and macrophage-rich PDAC phenotype with high expression of inhibitory immune checkpoints. Thus, our findings might serve as a rationale to investigate CXCR4 as a predictive biomarker in patients with PDAC undergoing immune checkpoint inhibition.

Self-organizing map improves understanding on the hydrochemical processes in aquifer systems

The holistic understanding of hydrochemical features is a crucial task for management and protection of water resources. However, it is challenging for a complex region, where multiple factors can cause hydrochemical changes in studied catchment. We collected 208 groundwater samples from such region in Kumamoto, southern Japan to explicitly characterize these processes by applying machine learning technique. The analyzed groundwater chemistry data like major cations and anions were fed to the self-organizing map (SOM) and the results were compared with classical classification approaches like Stiff diagram, standalone cluster analysis and score plots of principal component analysis (PCA). The SOM with integrated application of clustering divided the data into 11 clusters in this complex region. We confirmed that the results provide much greater details for the associated hydrochemical and contamination processes than the traditional approaches, which show quite good correspondence with the recent high resolution hydrological simulation model and aspects from geochemical modeling. However, the careful application of the SOM is necessary for obtaining accurate results. This study tested different normalization approaches for selecting the best SOM map and found that the topographic error (TE) was more important over the quantization error (QE). For instance, the lower QE obtained from min-max and log normalizations showed problems after clustering the SOM map, since the QE did not confirm the topological preservation. In contrast, the lowest TE obtained from Z-transformation data showed better spatial matching of the clusters with relevant hydrochemical characteristics. The results from this study clearly demonstrated that the SOM is a helpful approach for explicit understanding of the hydrochemical processes on reginal scale that may capably facilitate better groundwater resource management.

Unsupervised pattern-recognition and radiological risk assessment applied to the evaluation of behavior of rare earth elements, Th, and U in monazite sand

The Brazilian coast is rich in monazite which is found in beach sand deposits. In this study, the composition of the monazite sands from beaches of State of Espírito Santo, Brazil, was investigated. The concentrations of rare earth elements (REEs), Th, and U were determined by inductively coupled plasma mass spectrometry (ICP-MS). In the studied region, the mean concentration of investigated elements increased in the following order: Tm < Yb < Ho < Lu < Eu < Er < Tb < Dy < U < Y < Th < Gd < Sm < Pr < Nd < La < Ce. The sampling sites were classified into three clusters and discriminated by the concentrations of REEs, Th, and U found. In general, the radiological risk indices were higher than the established limits, and the risk of developing cancer was estimated to be higher than the world average.

Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

Source tracing of heavy metals in agricultural soils is of critical importance for effective pollution control and targeting policies. It is a great challenge to identify and apportion the complex sources of soil heavy metal pollution. In this study, a traditional analysis method, positive matrix fraction (PMF), and three machine learning methodologies, including self-organizing map (SOM), conditional inference tree (CIT) and random forest (RF), were used to identify and apportion the sources of heavy metals in agricultural soils from Lianzhou, Guangdong Province, China. Based on PMF, the contribution of the total loadings of heavy metals in soil were 19.3% for atmospheric deposition, 65.5% for anthropogenic and geogenic sources, and 15.2% for soil parent materials. Based on SOM model, As, Cd, Hg, Pb and Zn were attributed to mining and geogenic sources; Cr, Cu and Ni were derived from geogenic sources. Based on CIT results, the influence of altitude on soil Cr, Cu, Hg, Ni and Zn, as well as soil pH on Cd indicated their primary origin from natural processes. Whereas As and Pb were related to agricultural practices and traffic emissions, respectively. RF model further quantified the importance of variables and identified potential control factors (altitude, soil pH, soil organic carbon) in heavy metal accumulation in soil. This study provides an integrated approach for heavy metals source apportionment with a clear potential for future application in other similar regions, as well as to provide the theoretical basis for undertaking management and assessment of soil heavy metal pollution.

Use of neural network and multivariate statistics in the assessment of pellets produced from the exploitation of agro-industrial residues

The production of pellets from residual biomass generated monocropping by Brazilian agribusiness is an environmentally and economically interesting alternative in view of the growing demand for clean, low-cost, and efficient energy. In this way, pellets were produced with sugarcane bagasse and coffee processing residues, in different proportions with charcoal fines, aiming to improve the energy properties and add value to the residual biomass. The pellets had their properties compared to the commercial quality standard. Artificial neural networks and multivariate statistical models were used to validate the best treatments for biofuel production. The obtained pellets presented the minimum characteristics required by DIN EN 14961-6. However, the sugarcane bagasse biomass distinguished itself for use in energy pellets, more specifically, the treatment with 20% of fine charcoal because of its higher net calorific value (17.85 MJ·kg^-1) and energy density (13.30 GJ·m^-3), achieving the characteristics required for type A pellets in commercial standards. The statistical techniques were efficient and grouped the treatments with similar properties, as well as validated the sugarcane biomass mixed with charcoal fines for pellet production. Thus, these results demonstrate that waste charcoal fines mixed with agro-industrial biomass have great potential to integrate the production chain for energy generation.

Self-Organizing Maps to Multidimensionally Characterize Physical Profiles in Older Adults

The aim of this study is to automatically analyze, characterize and classify physical performance and body composition data of a cohort of Mexican community-dwelling older adults. Self-organizing maps (SOM) were used to identify similar profiles in 562 older adults living in Mexico City that participated in this study. Data regarding demographics, geriatric syndromes, comorbidities, physical performance, and body composition were obtained. The sample was divided by sex, and the multidimensional analysis included age, gait speed over height, grip strength over body mass index, one-legged stance, lean appendicular mass percentage, and fat percentage. Using the SOM neural network, seven profile types for older men and women were identified. This analysis provided maps depicting a set of clusters qualitatively characterizing groups of older adults that share similar profiles of body composition and physical performance. The SOM neural network proved to be a useful tool for analyzing multidimensional health care data and facilitating its interpretability. It provided a visual representation of the non-linear relationship between physical performance and body composition variables, as well as the identification of seven characteristic profiles in this cohort.

A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks

Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.

Fires, vegetation, and human-The history of critical transitions during the last 1000 years in Northeastern Mongolia

Fires are natural phenomena that impact human behaviors, vegetation, and landscape functions. However, the long-term history of fire, especially in the permafrost marginal zone of Central Asia (Mongolia), is poorly understood. This paper presents the results of radiocarbon and short-lived radionuclides (²¹⁰Pb and ¹³⁷Cs) dating, pollen, geochemical, charcoal, and statistical analyses (Kohonen's artificial neural network) of sediment core obtained from Northern Mongolia (the Khentii Mountains region). Therefore, we present the first high-resolution fire history from Northern Mongolia covering the last 1000 years, based on a multiproxy analysis of peat archive data. The results revealed that most of the fires in the region were likely initiated by natural factors, which were probably related to heatwaves causing prolonged droughts. We have demonstrated the link between enhanced fires and "dzud", a local climatic phenomenon. The number of livestock, which has been increasing for several decades, and the observed climatic changes are superimposed to cause "dzud", a deadly combination of droughts and snowy winter, which affects fire intensity. We observed that the study area has a sensitive ecosystem that reacts quickly to climate change. In terms of changes in the vegetation, the reconstruction reflected climate variations during the last millennium, the degradation of permafrost and occurrence of fires. However, more sites with good chronologies are needed to thoroughly understand the spatial relationships between changing climate, permafrost degradation, and vegetation change, which ultimately affect the nomadic societies in the region of Central and Northern Mongolia.

Impacts of Land Use on Surface Water Quality Using Self-Organizing Map in Middle Region of the Yellow River Basin, China

The Yellow River is one of the most important water sources in China, and its surrounding land use affected by human activities is an important factor in water quality pollution. To understand the impact of land use types on water quality in the Sanmenxia section of the Yellow River, the water quality index (WQI) was used to evaluate the water quality. A self-organizing map (SOM) was used for clustering analysis of water quality indicators, and the relationship between surface water quality and land use types was further analyzed by redundancy analysis (RDA). The results showed that WQI values ranged from 82.60 to 507.27, and the highest value was the sampling site S3, whose water quality grade was "Likely not suitable for drinking", mainly polluted by agricultural non-point sources ammonia nitrogen pollution. SOM clustered the sampling sites into 4 groups according to the water quality indicators, the main influencing factors for different groups were analyzed and explored in more depth in relation to land use types, suggesting that surface water quality was significantly connected with the proportion of land use types at the watershed scale in the interpretation of water quality change. The negative impact of cropland on surface water quality was greater than that of other land use types, and vegetation showed a greater positive impact on surface water quality than other land uses. The results provide evidence for water environment conservation based on land use in the watershed.

Optimization of multi-mode classification for process monitoring

Process monitoring seeks to identify anomalous plant operating states so that operators can take the appropriate actions for recovery. Instrumental to process monitoring is the labeling of known operating states in historical data, so that departures from these states can be identified. This task can be challenging and time consuming as plant data is typically high dimensional and extensive. Moreover, automation of this procedure is not trivial since ground truth labels are often unavailable. In this contribution, this problem is approached as a multi-mode classification one, and an automatic framework for labeling using unsupervised Machine Learning (ML) methods is presented. The implementation was tested using data from the Tennessee Eastman Process and an industrial pyrolysis process. A total of 9 ML ensembles were included. Hyperparameters were optimized using a multi-objective evolutionary optimization algorithm. Unsupervised clustering metrics (silhouette score, Davies-Bouldin index, and Calinski-Harabasz Index) were investigated as candidates for objective functions in the optimization implementation. Results show that ensembles and hyperparameter selection can be aided by multi-objective optimization. It was found that Silhouette score and Davies-Bouldin index are strong predictions of the ensemble’s performance and can then be used to obtain good initial results for subsequent fault detection and fault diagnosis procedures.

A Mask-Shaped Respiration Sensor Using Triboelectricity and a Machine Learning Approach toward Smart Sleep Monitoring Systems

Daily sleep monitoring is limited by the needs for specialized equipment and experts. This study combines a mask-shaped triboelectric nanogenerator (M-TENG) and machine learning for facile daily sleep monitoring without the specialized equipment or experts. The fabricated M-TENG demonstrates its excellent ability to detect respiration, even distinguishing oral and nasal breath. To increase the pressure sensitivity of the M-TENG, the reactive ion etching is conducted with different tilted angles. By investigating each surface morphology of the polytetrafluoroethylene films according to the reactive ion etching with different tilted angles, the tilted angle is optimized with the angle of 60° and the pressure sensitivity is increased by 5.8 times. The M-TENG can also detect changes in the angle of head and snoring. Various sleep stages can be classified by their distinctive electrical outputs, with the aid of a machine learning approach. As a result, a high averaged-classification accuracy of 87.17% is achieved for each sleep stage. Experimental results demonstrate that the proposed combination can be utilized to monitor the sleep stage in order to provide an aid for self-awareness of sleep disorders. Considering these results, the M-TENG and machine learning approach is expected to be utilized as a smart sleep monitoring system in near future.

Patterns of Muscle-Related Risk Factors for Sarcopenia in Older Mexican Women

Early detriment in the muscle mass quantity, quality, and functionality, determined by calf circumference (CC), phase angle (PA), gait time (GT), and grip strength (GSt), may be considered a risk factor for sarcopenia. Patterns derived from these parameters could timely identify an early stage of this disease. Thus, the present work aims to identify those patterns of muscle-related parameters and their association with sarcopenia in a cohort of older Mexican women with neural network analysis. Methods: Information from the functional decline patterns at the end of life, related factors, and associated costs study was used. A self-organizing map was used to analyze the information. A SOM is an unsupervised machine learning technique that projects input variables on a low-dimensional hexagonal grid that can be effectively utilized to visualize and explore properties of the data allowing to cluster individuals with similar age, GT, GSt, CC, and PA. An unadjusted logistic regression model assessed the probability of having sarcopenia given a particular cluster. Results: 250 women were evaluated. Mean age was 68.54 ± 5.99, sarcopenia was present in 31 (12.4%). Clusters 1 and 2 had similar GT, GSt, and CC values. Moreover, in cluster 1, women were older with higher PA values (p < 0.001). From cluster 3 upward, there is a trend of worse scores for every variable. Moreover, 100% of the participants in cluster 6 have sarcopenia (p < 0.001). Women in clusters 4 and 5 were 19.29 and 90 respectively, times more likely to develop sarcopenia than those from cluster 2 (p < 0.01). Conclusions: The joint use of age, GSt, GT, CC, and PA is strongly associated with the probability women have of presenting sarcopenia.

Integrating multiplex immunofluorescent and mass spectrometry imaging to map myeloid heterogeneity in its metabolic and cellular context

Cells often adopt different phenotypes, dictated by tissue-specific or local signals such as cell-cell and cell-matrix contacts or molecular micro-environment. This holds in extremis for macrophages with their high phenotypic plasticity. Their broad range of functions, some even opposing, reflects their heterogeneity, and a multitude of subsets has been described in different tissues and diseases. Such micro-environmental imprint cannot be adequately studied by single-cell applications, as cells are detached from their context, while histology-based assessment lacks the phenotypic depth due to limitations in marker combination. Here, we present a novel, integrative approach in which 15-color multispectral imaging allows comprehensive cell classification based on multi-marker expression patterns, followed by downstream analysis pipelines to link their phenotypes to contextual, micro-environmental cues, such as their cellular ("community") and metabolic ("local lipidome") niches in complex tissue. The power of this approach is illustrated for myeloid subsets and associated lipid signatures in murine atherosclerotic plaque.

Long-Term COVID-19 Restrictions in Italy to Assess the Role of Seasonal Meteorological Conditions and Pollutant Emissions on Urban Air Quality

A year-round air quality analysis was addressed over four Italian cities (Milan, Turin, Bologna, and Florence) following the outbreak of the Coronavirus 2019 (COVID-19) pandemic. NO2, O3, PM2.5, and PM10 daily observations were compared with estimations of meteorological variables and observations of anthropogenic emission drivers as road traffic and heating systems. Three periods in 2020 were analysed: (i) the first (winter/spring) lockdown, (ii) the (spring/summer) partial relaxation period, and (iii) the second (autumn/winter) lockdown. During the first lockdown, only NO2 concentrations decreased systematically (and significantly, between −41.9 and −53.9%), mainly due to the drastic traffic reduction (−70 to −74%); PM2.5 varied between −21 and +18%, PM10 varied between −23 and +9%, and O3 increased (up to +17%). During the partly relaxation period, no air quality issues were observed. The second lockdown was particularly critical as, although road traffic significantly reduced (−30 to −44%), PM2.5 and PM10 concentrations dramatically increased (up to +87 and +123%, respectively), mostly due to remarkably unfavourable weather conditions. The latter was confirmed as the main driver of PM’s most critical concentrations, while strong limitations to anthropogenic activity—including traffic bans—have little effect when taken alone, even when applied for more than two months and involving a whole country.

Networked Microgrid Energy Management Based on Supervised and Unsupervised Learning Clustering

Networked microgrid (NMG) is a novel conceptual paradigm that can bring multiple advantages to the distributed system. Increasing renewable energy utilization, reliability and efficiency of system operation and flexibility of energy sharing amongst several microgrids (MGs) are some specific privileges of NMG. In this paper, residential MGs, commercial MGs, and industrial MGs are considered as a community of NMG. The loads’ profiles are split into multiple sections to evaluate the maximum load demand (MLD). Based on the optimal operation of each MG, the operating reserve (OR) of the MGs is calculated for each section. Then, the self-organizing map as a supervised and a k-means algorithm as an unsupervised learning clustering method is utilized to cluster the MGs and effective energy-sharing. The clustering is based on the maximum load demand of MGs and the operating reserve of dispatchable energy sources, and the goal is to provide a more efficient system with high reliability. Eventually, the performance of this energy management and its benefits to the whole system is surveyed effectively. The proposed energy management system offers a more reliable system due to the possibility of reserved energy for MGs in case of power outage variation or shortage of power.

Identification of potential pan-coronavirus therapies using a computational drug repurposing platform

In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.

Delineating the controlling mechanisms of arsenic release into groundwater and its associated health risks in the Southern Loess Plateau, China

The mechanisms controlling arsenic (As) enrichment and mobilization associated with human health risk assessment of groundwater in the Longdong Basin, located in the southern part of the Loess Plateau, China, have been yet unexplained. This uncertainty is partly attributed to a poor understanding of groundwater arsenic management. To address this problem, this study investigated the occurrence and spatial distribution of As in unconfined groundwater (UG) and confined groundwater (CG) in the study area, integrated Self-Organizing Maps (SOM) and geochemical modeling to elucidate the mechanisms controlling As release and mobilization in groundwater, and conducted a health risk assessment of groundwater As. The results showed that 13.6% of UG samples (n = 66) and 22.4% of CG samples (n = 98) exceeded the WHO guideline limit of As (10 μg/L). The detailed hydrogeochemical studies showed that As-enrichment groundwater is dominated by Cl-Na type, and Gaillardet diagram indicated that evaporites weathering may contribute to As mobilization in CG. The SOM analysis combined with Spearman's correlation coefficient quantified the negative correlation between As and redox potential, dissolved oxygen, SO₄^2-, NO₃^-, and the positive correlation between As and HCO₃^-, Mn in UG. In CG, As is positively correlated to pH and negatively to electrical conductivity, SO₄^2-, Fe and Mn. The saturation indices of the mineral phases indicates an insignificant relationship between As and Fe. We conclude that under oxidizing conditions, evaporative controls and the desorption of Fe-oxides under alkaline and high salinity conditions are the dominant mechanisms controlling As release and mobilization in groundwater. In addition, exposure to groundwater As through drinking water posed potential risk of carcinogenic and non-carcinogenic effects on children and adults. This study contributes to groundwater As management and sustainable safe groundwater supply.

Cardio-metabolic risk modeling and assessment through sensor-based measurements

OBJECTIVE

Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors.

METHODS

We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables.

RESULTS

Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%.

CONCLUSIONS

Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.

Potentially toxic elemental contamination in Wainivesi River, Fiji impacted by gold-mining activities using chemometric tools and SOM analysis

Potentially toxic element (PTE) contamination in Wainivesi River, Fiji triggered by gold-mining activities is a major public health concern deserving attention. However, chemometric approaches and pattern recognition of PTEs in surface water and sediment are yet hardly studied in Pacific Island countries like Fijian urban River. In this study, twenty-four sediment and eight water sampling sites from the Wainivesi River, Fiji were explored to evaluate the spatial pattern, eco-environmental pollution, and source apportionment of PTEs. This analysis was done using an integrated approach of self-organizing map (SOM), principle component analysis (PCA), hierarchical cluster analysis (HCA), and indexical approaches. The PTE average concentration is decreasing in the order of Fe > Pb > Zn > Ni > Cr > Cu > Mn > Co > Cd for water and Fe > Zn > Pb > Mn > Cr > Ni > Cu > Co > Cd for sediment, respectively. Outcomes of eco-environmental indices including contamination and enrichment factors, and geo-accumulation index differed spatially indicated that majority of the sediment sites were highly polluted by Zn, Cd, and Ni. Cd and Ni contents can cause both ecological and human health risks. According to PCA, both mixed sources (geogenic and anthropogenic such as mine wastes discharge and farming activities) of PTEs for water and sediment were identified in the study area. The SOM analysis identified three spatial patterns, e.g., Cr-Co-Zn-Mn, Fe-Cd, and Ni-Pb-Cu in water and Zn-Cd-Cu-Mn, Cr-Ni and Fe, Co-Pb in sediment. Spatial distribution of entropy water quality index (EWQI) values depicted that northern and northwestern areas possess "poor" to "extremely poor" quality water. The entropy weights indicated Zn, Cd, and Cu as the major pollutants in deteriorating the water quality. This finding provides a baseline database with eco-environmental and health risk measures for the Wainivesi river contamination.

Potentially toxic elemental contamination in Wainivesi River, Fiji impacted by gold-mining activities using chemometric tools and SOM analysis

Potentially toxic element (PTE) contamination in Wainivesi River, Fiji triggered by gold-mining activities is a major public health concern deserving attention. However, chemometric approaches and pattern recognition of PTEs in surface water and sediment are yet hardly studied in Pacific Island countries like Fijian urban River. In this study, twenty-four sediment and eight water sampling sites from the Wainivesi River, Fiji were explored to evaluate the spatial pattern, eco-environmental pollution, and source apportionment of PTEs. This analysis was done using an integrated approach of self-organizing map (SOM), principle component analysis (PCA), hierarchical cluster analysis (HCA), and indexical approaches. The PTE average concentration is decreasing in the order of Fe > Pb > Zn > Ni > Cr > Cu > Mn > Co > Cd for water and Fe > Zn > Pb > Mn > Cr > Ni > Cu > Co > Cd for sediment, respectively. Outcomes of eco-environmental indices including contamination and enrichment factors, and geo-accumulation index differed spatially indicated that majority of the sediment sites were highly polluted by Zn, Cd, and Ni. Cd and Ni contents can cause both ecological and human health risks. According to PCA, both mixed sources (geogenic and anthropogenic such as mine wastes discharge and farming activities) of PTEs for water and sediment were identified in the study area. The SOM analysis identified three spatial patterns, e.g., Cr-Co-Zn-Mn, Fe-Cd, and Ni-Pb-Cu in water and Zn-Cd-Cu-Mn, Cr-Ni and Fe, Co-Pb in sediment. Spatial distribution of entropy water quality index (EWQI) values depicted that northern and northwestern areas possess "poor" to "extremely poor" quality water. The entropy weights indicated Zn, Cd, and Cu as the major pollutants in deteriorating the water quality. This finding provides a baseline database with eco-environmental and health risk measures for the Wainivesi river contamination.

Alterations of blood monocyte subset distribution and surface phenotype are linked to infection severity in COVID-19 inpatients

Severe coronavirus disease 19 (COVID‐19) manifests with systemic immediate proinflammatory innate immune activation and altered iron turnover. Iron homeostasis, differentiation, and function of myeloid leukocytes are interconnected. Therefore, we characterized the cellularity, surface marker expression, and iron transporter phenotype of neutrophils and monocyte subsets in COVID‐19 patients within 72 h from hospital admission, and analyzed how these parameters relate to infection severity. Between March and November 2020, blood leukocyte samples from hospitalized COVID‐19 patients (n = 48) and healthy individuals (n = 7) were analyzed by flow cytometry enabling comparative analysis of 40 features. Inflammation‐driven neutrophil expansion, depletion of CD16⁺ nonclassical monocytes, and changes in surface expression of neutrophil and monocyte CD64 and CD86 were associated with COVID‐19 severity. By unsupervised self‐organizing map clustering, four patterns of innate myeloid response were identified and linked to varying levels of systemic inflammation, altered cellular iron trafficking and the severity of disease. These alterations of the myeloid leukocyte compartment during acute COVID‐19 may be hallmarks of inefficient viral control and immune hyperactivation and may help at risk prediction and treatment optimization.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O₃, NO₂, SO₂, PM_2.5 and PM₁₀ of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM_2.5, PM₁₀ and CO are the main pollutants affecting air quality and fluctuation, followed by SO₂, NO₂ and O₃.

Using Clustering, Statistical Modeling, and Climate Change Projections to Analyze Recent and Future Region-Specific Compound Ozone and Temperature Burden Over Europe

High ground-level ozone concentrations and high air temperatures present two health-relevant natural hazards. The most severe health outcomes are generally associated with concurrent elevated levels of both variables, representing so-called compound ozone and temperature (o-t-) events. These o-t-events, their relationship with identified main meteorological and synoptic drivers, as well as ozone and temperature levels themselves and the linkage between both variables, vary temporally and with the location of sites. Due to the serious health burden and its spatiotemporal variations, the analysis of o-t-events across the European domain represents the focus of the current work. The main objective is to model and project present and future o-t-events, taking region-specific differences into account. Thus, a division of the European domain into six o-t-regions with homogeneous, similar ground-level ozone and temperature characteristics and patterns built the basis of the study. In order to assess region-specific main meteorological and synoptic drivers of o-t-events, statistical downscaling models were developed for selected representative stations per o-t-region. Statistical climate change projections for all central European o-t-regions were generated to assess potential frequency shifts of o-t-events until the end of the 21st century. The output of eight Earth System Models from the sixth phase of the Coupled Model Intercomparison Project considering SSP245 and SSP370 scenario assumptions was applied. By comparing midcentury (2041-2060) and late century (2081-2100) time slice differences with respect to a historical base period (1995-2014), substantial increases of the health-relevant compound o-t-events were projected across all central European regions.

The Fungal Endophyte Penicillium olsonii ML37 Reduces Fusarium Head Blight by Local Induced Resistance in Wheat Spikes

The fungal endophyte Penicillium olsonii ML37 is a biocontrol agent of Fusarium head blight in wheat (caused by Fusarium graminearum), which has shown a limited direct inhibition of fungal growth in vitro. We used RNA-seq and LC-MS/MS analyses to elucidate metabolic interactions of the three-way system Penicillium–wheat–Fusarium in greenhouse experiments. We demonstrated that P. olsonii ML37 colonises wheat spikes and transiently activates plant defence mechanisms, as pretreated spikes show a faster and stronger expression of the defence metabolism during the first 24 h after pathogen inoculation. This effect was transient and the expression of the same genes was lower in the pathogen-infected spikes than in those infected by P. olsonii alone. This response to the endophyte includes the transcriptional activation of several WRKY transcription factors. This early activation is associated with a reduction in FHB symptoms and significantly lower levels of the F. graminearum metabolites 15-acetyl-DON and culmorin. An increase in the Penicillium-associated metabolite asperphanamate confirms colonisation by the endophyte. Our results suggest that the mode of action used by P. olsonii ML37 is via a local defence activation in wheat spikes, and that this fungus has potential as a novel biological alternative in wheat disease control.

Who Is at Risk of Poor Mental Health Following Coronavirus Disease-19 Outpatient Management?

Background

Coronavirus Disease-19 (COVID-19) convalescents are at risk of developing a de novo mental health disorder or worsening of a pre-existing one. COVID-19 outpatients have been less well characterized than their hospitalized counterparts. The objectives of our study were to identify indicators for poor mental health following COVID-19 outpatient management and to identify high-risk individuals.

Methods

We conducted a binational online survey study with adult non-hospitalized COVID-19 convalescents (Austria/AT: n = 1,157, Italy/IT: n = 893). Primary endpoints were positive screening for depression and anxiety (Patient Health Questionnaire; PHQ-4) and self-perceived overall mental health (OMH) and quality of life (QoL) rated with 4 point Likert scales. Psychosocial stress was surveyed with a modified PHQ stress module. Associations of the mental health and QoL with socio-demographic, COVID-19 course, and recovery variables were assessed by multi-parameter Random Forest and Poisson modeling. Mental health risk subsets were defined by self-organizing maps (SOMs) and hierarchical clustering algorithms. The survey analyses are publicly available (https://im2-ibk.shinyapps.io/mental_health_dashboard/).

Results

Depression and/or anxiety before infection was reported by 4.6% (IT)/6% (AT) of participants. At a median of 79 days (AT)/96 days (IT) post-COVID-19 onset, 12.4% (AT)/19.3% (IT) of subjects were screened positive for anxiety and 17.3% (AT)/23.2% (IT) for depression. Over one-fifth of the respondents rated their OMH (AT: 21.8%, IT: 24.1%) or QoL (AT: 20.3%, IT: 25.9%) as fair or poor. Psychosocial stress, physical performance loss, high numbers of acute and sub-acute COVID-19 complaints, and the presence of acute and sub-acute neurocognitive symptoms (impaired concentration, confusion, and forgetfulness) were the strongest correlates of deteriorating mental health and poor QoL. In clustering analysis, these variables defined subsets with a particularly high propensity of post-COVID-19 mental health impairment and decreased QoL. Pre-existing depression or anxiety (DA) was associated with an increased symptom burden during acute COVID-19 and recovery.

Conclusion

Our study revealed a bidirectional relationship between COVID-19 symptoms and mental health. We put forward specific acute symptoms of the disease as "red flags" of mental health deterioration, which should prompt general practitioners to identify non-hospitalized COVID-19 patients who may benefit from early psychological and psychiatric intervention.

Clinical Trial Registration

[ClinicalTrials.gov], identifier [NCT04661462].

Analysis of the efficiency and structure of energy consumption in the industrial sector in the European Union countries between 1995 and 2019

The industrial sector is one of the most important sectors of the global economy, having a huge impact on the development of individual countries and regions. This sector covers a wide and diverse range of activities, which makes it of key importance for the economy of the European Union (EU) countries. As a result, the processes related to energy transformation and climate policy are increasingly connected with the sector in question. The need to improve the competitiveness of the economy and the implementation of climate and energy strategies means that this sector, like the entire EU economy, must rapidly enhance its energy efficiency and the structure of energy consumption. The following paper addresses this problem by presenting the results of a comprehensive study of the structure and volume of energy consumed by this sector in the period between 1995 and 2019. Based on this study, quantitative changes and the structure of energy consumed in this sector in the studied period were determined for the entire EU and its individual countries. The use of the Gini coefficient and the Lorenz curves allowed for the determination of the inequality of energy consumption in the industrial sector. The coefficients of variation and the dynamics of changes in energy consumption, both in total and from individual sources, for the EU countries were also determined. The aim of this part of the study was to indicate directions and the intensity of changes related to the structure and consumption of energy in this sector. In the next stage, groups of similar countries were created and compared in terms of the structure of energy consumed by the industrial sector in 1995 and 2019 (using the Kohonen's neural network). Relationships between the amount of energy consumed by the industrial sector in the entire EU and the basic economic and climate parameters of the economy were also delineated. The energy intensity of this sector and the dynamics of its changes in individual EU countries over the analyzed period were also specified. The results proved a great diversity of the EU countries and the improving energy efficiency and structure of energy consumed by the industrial sector. The research, together with its results, significantly broaden the knowledge of changes in the volume and structure of energy consumption in the industrial sector for the EU countries. The results make it possible to assess the actions of individual countries and the current state of implementation of EU climate and energy policy. They should also be used to develop future assumptions of this policy.

Investigating phenotypes of pulmonary COVID-19 recovery - a longitudinal observational prospective multicenter trial

Background:

The optimal procedures to prevent, identify, monitor, and treat long-term pulmonary sequelae of COVID-19 are elusive. Here, we characterized the kinetics of respiratory and symptom recovery following COVID-19.

Methods:

We conducted a longitudinal, multicenter observational study in ambulatory and hospitalized COVID-19 patients recruited in early 2020 (n = 145). Pulmonary computed tomography (CT) and lung function (LF) readouts, symptom prevalence, and clinical and laboratory parameters were collected during acute COVID-19 and at 60, 100, and 180 days follow-up visits. Recovery kinetics and risk factors were investigated by logistic regression. Classification of clinical features and participants was accomplished by unsupervised and semi-supervised multiparameter clustering and machine learning.

Results:

At the 6-month follow-up, 49% of participants reported persistent symptoms. The frequency of structural lung CT abnormalities ranged from 18% in the mild outpatient cases to 76% in the intensive care unit (ICU) convalescents. Prevalence of impaired LF ranged from 14% in the mild outpatient cases to 50% in the ICU survivors. Incomplete radiological lung recovery was associated with increased anti-S1/S2 antibody titer, IL-6, and CRP levels at the early follow-up. We demonstrated that the risk of perturbed pulmonary recovery could be robustly estimated at early follow-up by clustering and machine learning classifiers employing solely non-CT and non-LF parameters.

Conclusions:

The severity of acute COVID-19 and protracted systemic inflammation is strongly linked to persistent structural and functional lung abnormality. Automated screening of multiparameter health record data may assist in the prediction of incomplete pulmonary recovery and optimize COVID-19 follow-up management.

Funding:

The State of Tyrol (GZ 71934), Boehringer Ingelheim/Investigator initiated study (IIS 1199-0424).

Clinical trial number:

ClinicalTrials.gov: NCT04416100

Visualization of tumor heterogeneity and prediction of isocitrate dehydrogenase mutation status for human gliomas using multiparametric physiologic and metabolic MRI

This study aimed to differentiate isocitrate dehydrogenase (IDH) mutation status with the voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and to discover biological underpinnings of the clusters. A total of 69 patients with treatment-naïve diffuse glioma were scanned with pH-sensitive amine chemical exchange saturation transfer MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach was used for feature extraction from acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH status. The highest performance to predict IDH mutation status was found for 10-class clustering, with a mean area under the curve, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. Targeted biopsies revealed that the tissues with labels 7-10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. In conclusion, A machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.

Self-Organizing Maps to Evaluate Multidimensional Trajectories of Shrinkage in Spain

The analysis of factors influencing urban shrinkage is of great interest to spatial planners and policy makers. Population loss is usually the most relevant indicator of this shrinkage, but many other factors interact in complex ways over time. This paper proposes a multidimensional and spatio-temporal analysis of the shrinkage process in Spanish municipalities between 1991 and 2020. The method is based on the potentiality provided by self-organizing maps. The generated maps group municipalities according to hidden partial correlations among the data behind the variables characterizing the municipalities at different dates. In addition, as the number of map nodes is too big to allow for the detection of distinct types of municipalities, a Ward clustering algorithm is applied to identify homogeneous areas with a higher probability of shrinkage occurring over time. The results indicate that the municipalities with the lowest shrinkage are more stable and have a geographical concentration: they correspond to areas where peripheralization may occur (creation of surrounding districts close to main urban centers) and constitute the hinterland of large functional areas. The results also report a path of decline, with an important increase in the number of municipalities with higher shrinkage values. This approach has important implications for policy making since local governments may profit from shrinkage predictions.

Ligand Based Virtual Screening Using Self-organizing Maps

Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN.

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data because it is easier to process short hash values instead of longer strings. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.

Characterizing dissolved organic matter in Taihu Lake with PARAFAC and SOM method

The three-dimensional fluorescence spectrum has a significantly greater amount of information than the single-stage scanning fluorescence spectrum. At the same time, the parallel factor (PARAFAC) analysis and neural network method can help explore the fluorescence characteristics further, thus could be used to analyse multiple sets of three-dimensional matrix data. In this study, the PARAFAC analysis and the self-organizing mapping (SOM) neural network method are firstly introduced comprehensively. They are then adopted to extract information of the three-dimensional fluorescence spectrum data set for fluorescence characteristics analysis of dissolved organic matter (DOM) in Taihu Lake water. Forty water samples with DOM species were taken from different seasons with the fluorescence information obtained through three-dimensional fluorescence spectrum analysis, PARAFAC analysis and SOM analysis. The PARAFAC analysis results indicated that the main fluorescence components of dissolved organic matter in Taihu Lake water were aromatic proteins, fulvic acids, and dissolved microorganisms. The SOM analysis results showed that the fluorescence characteristics of the dissolved organics in Taihu Lake varied seasonally. Therefore, the combined method of three-dimensional fluorescence spectrum analysis, PARAFAC and SOM analysis can provide important information for characterization of the fluorescence properties of dissolved organic matter in surface water bodies.

Self-Organising Map Based Framework for Investigating Accounts Suspected of Money Laundering

There has been an emerging interest by financial institutions to develop advanced systems that can help enhance their anti-money laundering (AML) programmes. In this study, we present a self-organising map (SOM) based approach to predict which bank accounts are possibly involved in money laundering cases, given their financial transaction histories. Our method takes advantage of the competitive and adaptive properties of SOM to represent the accounts in a lower-dimensional space. Subsequently, categorising the SOM and the accounts into money laundering risk levels and proposing investigative strategies enables us to measure the classification performance. Our results indicate that our framework is well capable of identifying suspicious accounts already investigated by our partner bank, using both proposed investigation strategies. We further validate our model by analysing the performance when modifying different parameters in our dataset.

Damage Pattern Recognition and Crack Propagation Prediction for Crumb Rubber Concrete Based on Acoustic Emission Techniques

In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment of the specimens was a four-point bending load. Six clustering methods including k-means, fuzzy c-means (FCM), self-organizing mapping (SOM), Gaussian mixture model (GMM), hierarchical model, and density peak clustering method were analyzed; the results illustrated that the density peak clustering has the best performance. Next, the optimal clustering algorithm was used to cluster AE signals so as to study the evolution behavior of different damage modes, and the ultimate load of crumb rubber concrete was predicted by an artificial neural network. The results indicated that the combination of AE techniques and appropriate clustering methods such as the density peak clustering method and the artificial neural network could be used as a practical tool for structural health monitoring of crumb rubber concrete.

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches

River water and sediment embody environmental characteristics that give valuable environmental management information. However, indexical and chemometric appraisal of heavy metals (HMs) in river water and sediment is very scarce in Island countries including Fiji. In this research, forty-five sediment and fifteen water samples from the Nakuvadra-Rakiraki River, Fiji were analyzed for appraising spatial distribution, pollution, and source identification of selected heavy metals (HMs) using the coupling tools of self-organizing map (SOM), compositional data analysis (CDA), and sediment and water quality indices. The mean concentration of HMs increased in the order of Cd < Co < Pb < Cu < Zn < Ni < Cr < Mn < Fe for sediment and Cd < Pb < Cu < Ni < Zn < Co < Cr < Fe < Mn for water, respectively. The outcomes of the enrichment factor, geo-accumulation index and contamination factor index varied spatially and most of the sediment samples were polluted by Pb, Mn, and Cu. The potential ecological risk recognized Cd, and Pb as ecological and public health risks to the surrounding communities. Based on SOM and CDA, three potential sources (e.g., point, nonpoint and lithological sources) of HMs for sediment and two sources (e.g., geogenic and human-induced sources) of HMs for water were identified. The spatial patterns of EWQI values revealed that the northern and northeast zones of the studied area possess a high degree of water pollution. The entropy weight indicated Ni and Cd as the main pollutants degrading the water quality. This study gives a baseline dataset for combined eco-environmental measures for the river's water and sediment pollution as well as contributes to an inclusive appraisal of HMs contamination in global rivers.

Multidimensional indexing technique for medical images retrieval

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.