Development of biomarkers to distinguish different origins of red seabreams (Pagrus major) from Korea and Japan by fatty acid, amino acid, and mineral profiling

Red seabream (Pagrus major) has been one of the most popular fish in East Asia since early times. However, the discharge of nuclear wastewater into the sea following the Fukushima nuclear disaster in Japan has led to violations of the country of origin labeling. Therefore, the aim of the present study was to determine the origin of fish based on fatty acid, amino acid, and mineral analyses, and to develop biomarkers that can discriminate between Japanese and Korean red seabream. To identify the differences between the two groups, 29 fatty acid families, 17 amino acids, and 4 minerals were analyzed in 60 fish samples (standard sample collected in autumn), and fatty acid profiles were analyzed using heatmap with hierarchical clustering analysis and orthogonal projections to latent structures discriminant analysis. The top 10 fatty acids that were different between the two groups were selected from all seasonal fish samples by combining variable importance in projection scores and p-values. According to the receiver operating characteristic curve analysis results, we proposed percentage linoleic acid (C18:2n-6, cis) as a candidate biomarker with excellent sensitivity and specificity. This study introduces a strategy to identify the origins of red seabream using linoleic acid obtained from fatty acid analysis.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Quantifying multi-dimensional services of water ecosystems and breakpoint-based spatial radiation of typical regulating services considering the hierarchical clustering-based classification

This study proposes a set of water ecosystem services (WES) research system, including classification, benefit quantification and spatial radiation effect, with the goal of promoting harmonious coexistence between humans and nature, as well as providing a theoretical foundation for optimizing water resources management. Hierarchical cluster analysis was applied to categorize WES taking in to account the four nature constraints of product nature, energy flow relationships, circularity, and human social utility. A multi-dimensional benefit quantification methodology system for WES was constructed by combining the emergy theory with multidisciplinary methods of ecology, economics, and sociology. Based on the theories of spatial autocorrelation and breaking point, we investigated the spatial radiation effects of typical services in the cyclic regulation category. The proposed methodology has been applied to Luoyang, China. The results show that the Resource Provisioning (RP) and Cultural Addition (CA) services change greatly over time, and drive the overall WES to increase and then decrease. The spatial and temporal distribution of water resources is uneven, with WES being slightly better in the southern region than the northern region. Additionally, spatial radiation effects of typical regulating services are most prominent in S County. This finding suggests the establishment of scientific and rational intra-basin or inter-basin water management systems to expand the beneficial impacts of water-rich areas on neighboring regions.

Segmentation of OECD countries on the basis of selected global environmental indicators using k-means non-hierarchical clustering

In order to allocate resources and describe progress, frequently nations are grouped together by many international authorities. A variety of pertinent indicators can provide a more useful basis for classification for each specific area of interest. Based on commonalities between various variables connected to the global environmental sector, we developed a novel typology of country clusters. Four indicators were chosen after a review of the literature. In order to optimize data availability across as many OECD nations as feasible, indicators were chosen based on their relevance for all the OECD countries. Countries were arranged into a natural cluster using the hierarchical clustering method. Four groups, covering 31 countries, were the result of two stages of grouping. These four clusters were found to be more compact and clearly divided which gives policymakers a clear-cut idea as to how these environmental indicators are deteriorating day by day and year by year and what needs to be done to be more environmentally sustainable and responsible.

PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping

BACKGROUND

Clustering analysis is widely used to interpret biomedical data and uncover new knowledge and patterns. However, conventional clustering methods are not effective when dealing with sparse biomedical data. To overcome this limitation, we propose a hierarchical clustering method called polynomial weight-adjusted sparse clustering (PWSC).

RESULTS

The PWSC algorithm adjusts feature weights using a polynomial function, redefines the distances between samples, and performs hierarchical clustering analysis based on these adjusted distances. Additionally, we incorporate a consensus clustering approach to determine the optimal number of classifications. This consensus approach utilizes relative change in the cumulative distribution function to identify the best number of clusters, resulting in more stable clustering results. Leveraging the PWSC algorithm, we successfully classified a cohort of gastric cancer patients, enabling categorization of patients carrying different types of altered genes. Further evaluation using Entropy showed a significant improvement (p = 2.905e-05), while using the Calinski-Harabasz index demonstrates a remarkable 100% improvement in the quality of the best classification compared to conventional algorithms. Similarly, significantly increased entropy (p = 0.0336) and comparable CHI, were observed when classifying another colorectal cancer cohort with microbial abundance. The above attempts in cancer subtyping demonstrate that PWSC is highly applicable to different types of biomedical data. To facilitate its application, we have developed a user-friendly tool that implements the PWSC algorithm, which canbe accessed at http://pwsc.aiyimed.com/ .

CONCLUSIONS

PWSC addresses the limitations of conventional approaches when clustering sparse biomedical data. By adjusting feature weights and employing consensus clustering, we achieve improved clustering results compared to conventional methods. The PWSC algorithm provides a valuable tool for researchers in the field, enabling more accurate and stable clustering analysis. Its application can enhance our understanding of complex biological systems and contribute to advancements in various biomedical disciplines.

Explainable hierarchical clustering for patient subtyping and risk prediction

We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.

A new and effective two-step clustering approach for single cell RNA sequencing data

BACKGROUND

The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, pathogenesis of disease and drug resistance etc. One major task in scRNA-seq data analysis is to cluster cells in terms of their expression characteristics. Up to now, a number of methods have been proposed to infer cell clusters, yet there is still much space to improve their performance.

RESULTS

In this paper, we develop a new two-step clustering approach to effectively cluster scRNA-seq data, which is called TSC - the abbreviation of Two-Step Clustering. Particularly, by dividing all cells into two types: core cells (those possibly lying around the centers of clusters) and non-core cells (those locating in the boundary areas of clusters), we first clusters the core cells by hierarchical clustering (the first step) and then assigns the non-core cells to the corresponding nearest clusters (the second step). Extensive experiments on 12 real scRNA-seq datasets show that TSC outperforms the state of the art methods.

CONCLUSION

TSC is an effective clustering method due to its two-steps clustering strategy, and it is a useful tool for scRNA-seq data analysis.

Implementation of machine learning in DNA barcoding for determining the plant family taxonomy

The DNA barcoding approach has been used extensively in taxonomy and phylogenetics. The differences in certain DNA sequences are able to differentiate and help classify organisms into taxa. It has been used in cases of taxonomic disputes where morphology by itself is insufficient. This research aimed to utilize hierarchical clustering, an unsupervised machine learning method, to determine and resolve disputes in plant family taxonomy. We take a case study of Leguminosae that historically some classify into three families (Fabaceae, Caesalpiniaceae, and Mimosaceae) but others classify into one family (Leguminosae). This study is divided into several phases, which are: (i) data collection, (ii) data preprocessing, (iii) finding the best distance method, and (iv) determining disputed family. The data used are collected from several sources, including National Center for Biotechnology Information (NCBI), journals, and websites. The data for validation of the methods were collected from NCBI. This was used to determine the best distance method for differentiating families or genera. The data for the case study in the Leguminosae group was collected from journals and a website. From the experiment that we have conducted, we found that the Pearson method is the best distance method to do clustering ITS sequence of plants, both in accuracy and computational cost. We use the Pearson method to determine the disputed family between Leguminosae. We found that the case study of Leguminosae should be grouped into one family based on our research.

An Artificial Intelligence Approach for Tackling Conformational Energy Uncertainties in Chiroptical Spectroscopies

Determination of the absolute configuration of chiral molecules is a prerequisite for obtaining a fundamental understanding in any chirality-related field. The interaction with polarised light has proven to be a powerful means to determine this absolute configuration, but its application rests on the comparison between experimental and computed spectra for which the inherent uncertainty in conformational Boltzmann factors has proven to be extremely hard to tackle. Here we present a novel approach that overcomes this issue by combining a genetic algorithm that identifies the relevant conformers by accounting for the uncertainties in DFT relative energies, and a hierarchical clustering algorithm that analyses the trends in the spectra of the considered conformers and identifies on-the-fly when a given chiroptical technique is not able to make reliable predictions. The effectiveness of this approach is demonstrated by considering the challenging cases of papuamine and haliclonadiamine, two bis-indane natural products with eight chiral centres and considerable conformational heterogeneity that could not be assigned unambiguously with current approaches.

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population.

Parea: Multi-view ensemble clustering for cancer subtype discovery

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods have been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.

Analysis and prediction of athlete's anxiety state based on artificial intelligence

Obtaining athletes' anxiety accurately and regulating their psychological state helps improve their competitive performance. Therefore, this article uses a hierarchical clustering algorithm to identify the sources of stress of track and field athletes. A novel and efficient hierarchical clustering algorithm is proposed in this article. The algorithm consists of two stages: dividing and agglomerating. In the dividing stage, the initial data set is taken as a class and subclasses more than the actual number of clusters are obtained through multiple dividing. In the agglomerating phase, the subclasses divided during the dividing process are merged into the correct class. In addition, we construct an analysis model of athletes' anxiety state based on the radial basis function (RBF) model, where athletes' anxiety is divided into three categories: physical condition anxiety, competition state and cognitive state. The proposed model is trained from the official website of the China Track and Field Association. The athletes' information from 500 samples was arranged to form the sample database of athletes' data. The implicit unit center, function width and connection weight record the characteristics of various sports anxiety states. Then we used the Bayesian and Lagrange models as comparative models for evaluating the psychological state. Precision and efficiency were used for evaluation indexes. The proposed model's results are much better in accuracy and time than those of the Lagrange and Bayesian models. The outcome of the proposed research can provide a reasonable basis for the decision-making of stress relief for track and field athletes.

Recommendations for validating hierarchical clustering in consumer sensory projects

Choosing the proper hierarchical clustering algorithm and number of clusters is always a key question in consumer sensory projects. In many cases, researchers do not publish any reason why it was chosen a given distance measure and linkage rule along with cluster numbers. The reason behind this could be that different cluster validation and comparison techniques give contradictory results in most cases. A complex evaluation to define the proper clustering might be time-consuming and tedious. The paper introduces the clustering of three sensory data sets using different distance metrics and linkage rules for different numbers of clusters. The results of the validation methods deviate, suggesting that clustering depends heavily on the data set in question. Although Euclidean distance, Ward's method seems a safe choice, testing, and validation of different clustering combinations is strongly suggested.

Infrared analysis in the aqueous humor of patients with uveitis: Preliminary results

Inflammatory processes affecting the uvea result in a temporary o permanent blurred vision and represent an important cause of visual impairment worldwide. It is often hard to make a precise diagnosis which is dependent on the clinical expertise, diagnostic tests, laboratory investigations in blood and sometimes in the aqueous humor (AH). With the aim of obtaining proof of principle Fourier Transformed Infrared (FT-IR) absorbance spectroscopy was applied to study the molecular composition of 72 AH samples collected in 26 patients with uveitis and in 44 controls. The unsupervised exploration of the internal structure of the dataset by principal component analysis reduced hundreds IR variables to those most representative allowing to obtain the predictive model that distinguished the AH spectra of patients with uveitis from controls. The same result was obtained by unsupervised agglomerative cluster analysis. After labeling the spectra with some clinical information it was observed that most severe uveitis with active processes were grouped separately from chronic and relapsing uveitis and controls. The consistence of prediction models is discussed in the light of supporting etiological diagnosis by machine learning processes. In conclusion, proof of principle has been obtained that the IR spectral pattern of AH may reflect particular uveal diseases.

Identifying responders to elamipretide in Barth syndrome: Hierarchical clustering for time series data

BACKGROUND

Barth syndrome (BTHS) is a rare genetic disease that is characterized by cardiomyopathy, skeletal myopathy, neutropenia, and growth abnormalities and often leads to death in childhood. Recently, elamipretide has been tested as a potential first disease-modifying drug. This study aimed to identify patients with BTHS who may respond to elamipretide, based on continuous physiological measurements acquired through wearable devices.

RESULTS

Data from a randomized, double-blind, placebo-controlled crossover trial of 12 patients with BTHS were used, including physiological time series data measured using a wearable device (heart rate, respiratory rate, activity, and posture) and functional scores. The latter included the 6-minute walk test (6MWT), Patient-Reported Outcomes Measurement Information System (PROMIS) fatigue score, SWAY Balance Mobile Application score (SWAY balance score), BTHS Symptom Assessment (BTHS-SA) Total Fatigue score, muscle strength by handheld dynamometry, 5 times sit-and-stand test (5XSST), and monolysocardiolipin to cardiolipin ratio (MLCL:CL). Groups were created through median split of the functional scores into "highest score" and "lowest score", and "best response to elamipretide" and "worst response to elamipretide". Agglomerative hierarchical clustering (AHC) models were implemented to assess whether physiological data could classify patients according to functional status and distinguish non-responders from responders to elamipretide. AHC models clustered patients according to their functional status with accuracies of 60-93%, with the greatest accuracies for 6MWT (93%), PROMIS (87%), and SWAY balance score (80%). Another set of AHC models clustered patients with respect to their response to treatment with elamipretide with perfect accuracy (all 100%).

CONCLUSIONS

In this proof-of-concept study, we demonstrated that continuously acquired physiological measurements from wearable devices can be used to predict functional status and response to treatment among patients with BTHS.

Application of statistics to correlate groundwater chemistry with land use on O'ahu, Hawai'i

This study compiles commonly available groundwater chemistry data from the Pearl Harbor Sole Source Aquifer (SSA), Hawai'i-O'ahu's primary drinking water source-and applies hierarchical clustering analysis (HCA), principal component analyses (PCA), piper diagrams, and box plots with geospatial analysis to better define groundwater regions and correlate groundwater chemistry in those regions with land use. Groundwater in this aquifer recharges and flows through chemically similar soil and rocks, such that anthropogenic activities are a primary influence on the chemical variability of the aquifer's differing regions. Our analyses link specific chemical species in groundwater to land use/cover categories: urban, agriculture, and natural and anthropogenically-induced saline water intrusion. To create distinct statistical groupings with different groundwater chemistry compositions, it was important that the suite of parameters used in the statistical analysis do not covary. In our case, Cl^- covaried with several major ions; however, by including F^-, alkalinity, and SiO_x that do not covary with Cl^- in the covariance matrix, we produced improved spatial grouping of HCA clusters and stronger affinities to land use designations. Results show that dominant groundwater chemistry changes with land use along flow paths. These results pertain to areas where groundwater flows from conservation land in high recharge areas of O'ahu's mountain ranges to urban and agricultural land use regions: groundwater retains its source characteristics until about 3-6 km into agricultural and urban zoned lands. Ultimately, this study outlines a simple method for water quality regulators to use groundwater chemistry to identify risks of target contaminants based on land use.

Thalamus sends information about arousal but not valence to the amygdala

RATIONALE

The basolateral amygdala (BLA) and medial geniculate nucleus of the thalamus (MGN) have both been shown to be necessary for the formation of associative learning. While the role that the BLA plays in this process has long been emphasized, the MGN has been less well-studied and surrounded by debate regarding whether the relay of sensory information is active or passive.

OBJECTIVES

We seek to understand the role the MGN has within the thalamoamgydala circuit in the formation of associative learning.

METHODS

Here, we use optogenetics and in vivo electrophysiological recordings to dissect the MGN-BLA circuit and explore the specific subpopulations for evidence of learning and synthesis of information that could impact downstream BLA encoding. We employ various machine learning techniques to investigate function within neural subpopulations. We introduce a novel method to investigate tonic changes across trial-by-trial structure, which offers an alternative approach to traditional trial-averaging techniques.

RESULTS

We find that the MGN appears to encode arousal but not valence, unlike the BLA which encodes for both. We find that the MGN and the BLA appear to react differently to expected and unexpected outcomes; the BLA biased responses toward reward prediction error and the MGN focused on anticipated punishment. We uncover evidence of tonic changes by visualizing changes across trials during inter-trial intervals (baseline epochs) for a subset of cells.

CONCLUSION

We conclude that the MGN-BLA projector population acts as both filter and transferer of information by relaying information about the salience of cues to the amygdala, but these signals are not valence-specified.

Exploring COVID-19 transmission patterns and key factors during epidemics caused by three major strains in Asia

The COVID-19 epidemic has lasted for more than two years since the outbreak in late 2019. An urgent and challenging question is how to systematically evaluate epidemic developments in different countries, during different periods, and to determine which measures that could be implemented are key for successful epidemic prevention. In this study, SBD distance-based K-shape clustering and hierarchical clustering methods were used to analyse epidemics in Asian countries. For the hierarchical clustering, epidemic time series were divided into three periods (epidemics induced by the Original/Alpha, Delta and Omicron variants separately). Standard deviations, the Hurst index, mortality rates, peak value of confirmed cases per capita, average growth rates, and the control efficiency of each period were used to characterize the epidemics. In addition, the total numbers of cases in the different countries were analysed by correlation and regression in relation to 15 variables that could have impacts on COVID-19. Finally, some suggestions on prevention and control measures for each category of country are given. We found that the total numbers of cases per million of a population, total deaths per million and mortality rates were highly correlated with the proportion of people aged over 65 years, the prevalence of multiple diseases, and the national GDP. We also found significant associations between case numbers and vaccination rates, health expenditures, and stringency of control measures. Vaccinations have played a positive role in COVID-19, with a gradual decline in mortality rates in later periods, and are still playing protective roles against the Delta and Omicron strains. The stringency of control measures taken by a government is not an indicator of the appropriateness of a country's response to the outbreak, and a higher index does not necessarily mean more effective measures; a combination of factors such as national vaccination rates, the country's economic foundation and the availability of medical equipment is also needed. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

The magmatic evolution of South-East Crater (Mt. Etna) during the February-April 2021 sequence of lava fountains from a mineral chemistry perspective

The South-East Crater (SEC) at Mt. Etna started a period of lava fountaining in December 2020, producing over 60 paroxysms until February 2022. The activity had an intense sequence from February 16 to April 1, 2021, totaling 17 paroxysmal events separated by repose times varying from 1 to 7 days. The eruptive sequence was extensively monitored, providing a unique opportunity to relate the chemistry and texture of the erupted products to eruption dynamics. We investigate the temporal evolution of the magmatic system through this eruptive sequence by quantifying variations in the composition and texture of clinopyroxene. Clinopyroxene major element transects across crystals from five representative lava fountains allow us to determine the relative proportions of deep versus shallow-stored magmas that fed these events. We use hierarchical clustering (HC), an unsupervised machine learning technique, to objectively identify clinopyroxene compositional clusters and their variations during this intense eruptive phase. Our results show that variations of monitoring parameters and eruption intensity are expressed in the mineral record both as changes in cluster proportions and the chemical complexity of single crystals. We also apply random forest thermobarometry to relate each cluster to P-T conditions of formation. We suggest that the February-April 2021 eruptive sequence was sustained by the injection of a hotter and deeper magma into a storage area at 1-3 kbar, where it mixed with a slightly more evolved magma. The February 28 episode emitted the most mafic magma, in association with the highest mean lava fountain height and highest time-averaged discharge rate, which make it the peak of the analyzed eruptive interval. Our results show that after this episode, the deep magma supply decreased and the erupted magma become gradually more chemically evolved, with a lower time-average discharge rate and fountain height. We propose this approach as a means to rapidly, objectively, and effectively link petrological and geophysical/geochemical monitoring during ongoing eruptions. We anticipate that the systematic application of this approach will serve to shed light on the magmatic processes controlling the evolution of ongoing eruptions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00445-023-01643-2.

Improved Regularized Multi-class Logistic Regression for Gene Classification with Optimal Kernel PCA and HC Algorithm

A significant challenge in high-dimensional and big data analysis is related to the classification and prediction of the variables of interest. The massive genetic datasets are complex. Gene expression datasets are enriched with useful genes that are associated with specific diseases such as cancer. In this study, we used two gene expression datasets from the Gene Expression Omnibus and preprocessed them before classification. We used optimal kernel principal component analysis in which the optimal kernel function was chosen for dataset dimensionality reduction and extraction of the most important features. The gene sets with a high validity index were collected using a combined hieratical clustering and optimal kernel principal component analysis (KHC-RLR) algorithm. Logistic regression is one of the most common methods for classification, and it has been shown to be a useful classification approach for gene expression data analysis. In this study, we used multi-class logistic regression to classify the collected gene sets. We found that ordinary logistic regression caused a major overfitting problem; therefore, we used regularized multi-class logistic regression to classify the gene sets. The proposed KHC-RLR algorithm showed a high performance and satisfied accuracy measures.

Hydrochemical interpretation of groundwater in Yinchuan basin using self-organizing maps and hierarchical clustering

Self-organizing maps (SOM) is emerging as an alternative to traditional clustering methods for the hydrochemical analysis of groundwater due to the visualization of high-dimensional data. In this study, a combined method of the SOM and hierarchical clustering was applied to analyze the hydrochemical characteristics of groundwater in phreatic aquifer in the Yinchuan basin, China. 154 groundwater samples classified by SOM were projected on 65 neurons and grouped into 6 clusters with hierarchical clustering. The results showed that there exist three principal types of groundwater in the study area, namely high HCO₃^- type (Cluster-1, 2, and 6), high SO₄^2- type (Cluster-3, and 4), and high Na⁺ type (Cluster-5). Chadha diagram indicated that the phreatic water in Yinchuan basin mainly belongs to the group of alkaline earths that exceed alkali metals (n = 107, 69%). Rock weathering and evaporation-crystallization are the predominant mechanism in the hydrogeochemical evolution of phreatic groundwater. The present study suggested that the combined method of the SOM and hierarchical clustering provides a reliable approach for interpreting the hydrochemical characteristics of groundwater with high-dimensional data.

Global abundance of short tandem repeats is non-random in rodents and primates

Background

While of predominant abundance across vertebrate genomes and significant biological implications, the relevance of short tandem repeats (STRs) (also known as microsatellites) to speciation remains largely elusive and attributed to random coincidence for the most part. Here we collected data on the whole-genome abundance of mono-, di-, and trinucleotide STRs in nine species, encompassing rodents and primates, including rat, mouse, olive baboon, gelada, macaque, gorilla, chimpanzee, bonobo, and human. The collected data were used to analyze hierarchical clustering of the STR abundances in the selected species.

Results

We found massive differential STR abundances between the rodent and primate orders. In addition, while numerous STRs had random abundance across the nine selected species, the global abundance conformed to three consistent < clusters>, as follows: <rat, mouse>, <gelada, macaque, olive baboon>, and <gorilla, chimpanzee, bonobo, human>, which coincided with the phylogenetic distances of the selected species (p < 4E-05). Exceptionally, in the trinucleotide STR compartment, human was significantly distant from all other species.

Conclusion

Based on hierarchical clustering, we propose that the global abundance of STRs is non-random in rodents and primates, and probably had a determining impact on the speciation of the two orders. We also propose the STRs and STR lengths, which predominantly conformed to the phylogeny of the selected species, exemplified by (t)10, (ct)6, and (taa4). Phylogenetic and experimental platforms are warranted to further examine the observed patterns and the biological mechanisms associated with those STRs.

Convergent gray matter alterations across drugs of abuse and network-level implications: A meta-analysis of structural MRI studies

BACKGROUND

Neuroimaging studies often consider brain alterations linked with substance abuse within the context of individual drugs (e.g., nicotine), while neurobiological theories of addiction emphasize common brain network-level alterations across drug classes. Using emergent meta-analytic techniques, we identified common structural brain alterations across drugs and characterized the functionally-connected networks with which such structurally altered regions interact.

METHODS

We identified 82 articles characterizing gray matter (GM) volume differences for substance users vs. controls. Using the anatomical likelihood estimation algorithm, we identified convergent GM reductions across drug classes. Next, we performed resting-state and meta-analytic functional connectivity analyses using each structurally altered region as a seed and computed whole-brain functional connectivity profiles as the union of both maps. We characterized an "extended network" by identifying brain areas demonstrating the highest degree of functional coupling with structurally impacted regions. Finally, hierarchical clustering was performed leveraging extended network nodes' functional connectivity profiles to delineate subnetworks.

RESULTS

Across drug classes, we identified medial frontal/ventromedial prefrontal, and multiple regions in anterior cingulate (ACC) and insula as regions displaying convergent GM reductions among users. Overlap of these regions' functional connectivity profiles identified ACC, inferior frontal, PCC, insula, superior temporal, and putamen as regions of an impacted extended network. Hierarchical clustering revealed 3 subnetworks closely corresponding to default mode (PCC, angular), salience (dACC, caudate), and executive control networks (dlPFC and parietal).

CONCLUSIONS

These outcomes suggest that substance-related structural brain alterations likely have implications for the functioning of canonical large-scale networks and the perpetuation of substance use and neurocognitive alterations.

Learned Practical Guidelines for Evaluating Conditional Entropy and Mutual Information in Discovering Major Factors of Response-vs.-Covariate Dynamics

We reformulate and reframe a series of increasingly complex parametric statistical topics into a framework of response-vs.-covariate (Re-Co) dynamics that is described without any explicit functional structures. Then we resolve these topics' data analysis tasks by discovering major factors underlying such Re-Co dynamics by only making use of data's categorical nature. The major factor selection protocol at the heart of Categorical Exploratory Data Analysis (CEDA) paradigm is illustrated and carried out by employing Shannon's conditional entropy (CE) and mutual information (I[Re;Co]) as the two key Information Theoretical measurements. Through the process of evaluating these two entropy-based measurements and resolving statistical tasks, we acquire several computational guidelines for carrying out the major factor selection protocol in a do-and-learn fashion. Specifically, practical guidelines are established for evaluating CE and I[Re;Co] in accordance with the criterion called [C1:confirmable]. Following the [C1:confirmable] criterion, we make no attempts on acquiring consistent estimations of these theoretical information measurements. All evaluations are carried out on a contingency table platform, upon which the practical guidelines also provide ways of lessening the effects of the curse of dimensionality. We explicitly carry out six examples of Re-Co dynamics, within each of which, several widely extended scenarios are also explored and discussed.

Impact of COVID-19 on G20 countries: analysis of economic recession using data mining approaches

The G20 countries are the locomotives of economic growth, representing 64% of the global population and including 4.7 billion inhabitants. As a monetary and market value index, real gross domestic product (GDP) is affected by several factors and reflects the economic development of countries. This study aimed to reveal the hidden economic patterns of G20 countries, study the complexity of related economic factors, and analyze the economic reactions taken by policymakers during the coronavirus disease of 2019 (COVID-19) pandemic recession (2019-2020). In this respect, this study employed data-mining techniques of nonparametric classification tree and hierarchical clustering approaches to consider factors such as GDP/capita, industrial production, government spending, COVID-19 cases/population, patient recovery, COVID-19 death cases, number of hospital beds/1000 people, and percentage of the vaccinated population to identify clusters for G20 countries. The clustering approach can help policymakers measure economic indices in terms of the factors considered to identify the specific focus of influences on economic development. The results exhibited significant findings for the economic effects of the COVID-19 pandemic on G20 countries, splitting them into three clusters by sharing different measurements and patterns (harmonies and variances across G20 countries). A comprehensive statistical analysis was performed to analyze endogenous and exogenous factors. Similarly, the classification and regression tree method was applied to predict the associations between the response and independent factors to split the G-20 countries into different groups and analyze the economic recession. Variables such as GDP per capita and patient recovery of COVID-19 cases with values of $12,012 and 82.8%, respectively, were the most significant factors for clustering the G20 countries, with a correlation coefficient (R2) of 91.8%. The results and findings offer some crucial recommendations to handle pandemics in terms of the suggested economic systems by identifying the challenges that the G20 countries have experienced.

Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain

Background

Swine are considered a major source of foodborne salmonellosis, a public health issue further complicated by the circulation of multidrug-resistant Salmonella strains that threaten the safety of the food chain. The current study aimed to identify patterns that can help to understand the epidemiology of antimicrobial resistance (AMR) in Salmonella in pigs in Spain through the application of several multivariate statistical methods to data from the AMR national surveillance programs from 2001 to 2017.

Results

A total of 1,318 pig Salmonella isolates belonging to 63 different serotypes were isolated and their AMR profiles were determined. Tetracycline resistance across provinces in Spain was the highest among all antimicrobials and ranged from 66.7% to 95.8%, followed by sulfamethoxazole resistance (range: 42.5% − 77.8%), streptomycin resistance (range: 45.7% − 76.7%), ampicillin resistance (range: 24.3% − 66.7%, with a lower percentage of resistance in the South-East of Spain), and chloramphenicol resistance (range: 8.5% − 41.1%). A significant increase in the percentage of resistant isolates to chloramphenicol, sulfamethoxazole, ampicillin and trimethoprim from 2013 to 2017 was observed. Bayesian network analysis showed the existence of dependencies between resistance to antimicrobials of the same but also different families, with chloramphenicol and sulfamethoxazole in the centre of the networks. In the networks, the conditional probability for an isolate susceptible to ciprofloxacin that was also susceptible to nalidixic acid was 0.999 but for an isolate resistant to ciprofloxacin that was also resistant to nalidixic acid was only 0.779. An isolate susceptible to florfenicol would be expected to be susceptible to chloramphenicol, whereas an isolate resistant to chloramphenicol had a conditional probability of being resistant to florfenicol at only 0.221. Hierarchical clustering further demonstrated the linkage between certain resistances (and serotypes). For example, a higher likelihood of multidrug-resistance in isolates belonging to 1,4,[5],12:i:- serotype was found, and in the cluster where all isolates were resistant to tetracycline, chloramphenicol and florfenicol, 86.9% (n = 53) of the isolates were Typhimurium.

Conclusion

Our study demonstrated the power of multivariate statistical methods in discovering trends and patterns of AMR and found the existence of serotype-specific AMR patterns for serotypes of public health concern in Salmonella isolates in pigs in Spain.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-022-03377-3.

Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study

Attenuation correction (AC) is important for accurate interpretation of SPECT myocardial perfusion imaging (MPI). However, it is challenging to perform AC in dedicated cardiac systems not equipped with a transmission imaging capability. Previously, we demonstrated the feasibility of generating attenuation-corrected SPECT images using a deep learning technique (SPECTDL) directly from non-corrected images (SPECTNC). However, we observed performance variability across patients which is an important factor for clinical translation of the technique. In this study, we investigate the feasibility of overcoming the performance variability across patients for the direct AC in SPECT MPI by proposing to develop an advanced network and a data management strategy. To investigate, we compared the accuracy of the SPECTDL for the conventional U-Net and Wasserstein cycle GAN (WCycleGAN) networks. To manage the training data, clustering was applied to a representation of data in the lower-dimensional space, and the training data were chosen based on the similarity of data in this space. Quantitative analysis demonstrated that DL model with an advanced network improves the global performance for the AC task with the limited data. However, the regional results were not improved. The proposed data management strategy demonstrated that the clustered training has potential benefit for effective training.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02646-3.

Assessing Australian consumer preferences for fresh pork meat attributes: A best-worst approach on 46 attributes

Constant competition and changing consumer preferences prompts the need to improve the competitiveness of the Australian pork industry. This study examines the heterogeneity of Australian consumer preferences related to fresh pork cues. Using best-worst scaling, we examine the importance of 15 intrinsic and 31 extrinsic product attributes to 196 Australian consumers. Findings reveal that taste, succulence and the smell of boar taint were the most important intrinsic cues, while animal welfare and naturalness were the most important extrinsic cues. Based on the importance of intrinsic cues, four segments were identified, namely boar taint haters, lean meat eaters, colour lovers and cuts and size matters. Four segments based on extrinsic cues were identified as animal and environment lovers, naturalness lovers, demanding buyers and utilitarian buyers. This study contributes significantly to the industry by offering granular insights with respect to Australian consumer demands and optimal communication of cues.

Alkylated Polycyclic Aromatic Compounds in Road Runoff Are an Environmental Risk and Should Be Included in Future Investigations

Polycyclic aromatic compounds (PACs) and metals are important contaminants in road runoff. Vital mitigation measures against aquatic contamination from road runoff include the use of sedimentation ponds. However, it has been demonstrated that ecosystems in sedimentation ponds might be affected by road runoff. Sediments from six natural ponds and 27 sedimentation ponds were investigated to determine the environmental risk of contaminants. The amount of traffic on the respective roads associated with the sedimentation ponds was correlated with the environmental concentrations. Quantification included seven metals, the 16 US Environmental Protection Agency polycyclic aromatic hydrocarbons, alkylated PACs, dibenzothiophene, benzo[e]pyrene, and perylene. The environmental risk was assessed according to the European Union Water Framework Directive guidelines. Sedimentation ponds had a higher level of contaminants than natural ponds, and environmental risks were dominated by the concentration of PACs. Alkylated PACs contributed to the environmental risk posed by PACs and should be included in future monitoring. Only Cu and Zn were measured at concentrations above the environmental quality standards (EQSs), while 13 PACs exceeded the EQS. Sediment concentrations of Cu, Zn, and most PACs correlated significantly with the amount of traffic. The sources of PACs were determined by source apportionment ratios between PACs. Alkylation and pyrogenic indices showed that sources in natural ponds were of mostly pyrogenic origin, whereas in sedimentation ponds they were predominantly petrogenic. Asphalt was the probable main source of PACs. A hierarchical clustering technique was used to investigate both the environmental risks and the pattern of PACs in the ponds and revealed that a few sedimentation ponds were not protecting the environment as intended because the upper sediment layers had not been regularly dredged. Environ Toxicol Chem 2022;41:1838-1850. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Hierarchical clustering of liquid chromatography-tandem mass spectrometry data for screening of phosphodiesterase type 5 inhibitors and their analogues in adulterated dietary supplements

Sexual enhancement dietary supplements have often been adulterated with phosphodiesterase type 5 (PDE-5) inhibitors used for treatment of erectile dysfunction, and widely distributed through online markets. As the illegal adulterants, the original PDE-5 inhibitor drugs and a numerous number of synthetized analogues, more than 80, have already been found. Therefore, analytical methods that detect various PDE-5 inhibitors and uncover newly synthesized analogues are needed. In this study, we have developed a rapid and reliable screening method for PDE-5 inhibitors and their structural analogues by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by hierarchical clustering based on similarity of MS/MS spectra. Forty reference standards of PDE-5 inhibitors/analogues were measured using a quadrupole-orbitrap mass spectrometer in data-dependent mode. The 60 most intense fragment ions were extracted from each MS/MS spectra, and the ions observed within 1.5 mDa mass tolerance were considered to be the same ion. Based on fragment ion tables representing detected ions for each compound, hierarchical clustering was performed. The resulting dendrogram showed that the reference standards were separated into seven clusters according to their characteristic structures. Subsequently, two additional standards spiked into a herbal sample were analyzed. While herbal components were clearly separated from the clusters of the reference standards, the spiked standards were clustered closely with the structurally similar standards. Furthermore, application of our method to dietary supplements allowed for detection of sildenafil and tadalafil as adulterants. These results suggest that our screening method facilitates discovery of adulterant PDE-5 inhibitors/analogues by illustrating their structural similarity.

FastANI, Mash and Dashing equally differentiate between Klebsiella species

Bacteria of the genus Klebsiella are among the most important multi-drug resistant human pathogens, though they have been isolated from a variety of environments. The importance and ubiquity of these organisms call for quick and accurate methods for their classification. Average Nucleotide Identity (ANI) is becoming a standard for species delimitation based on whole genome sequence comparison. However, much faster genome comparison tools have been appearing in the literature. In this study we tested the quality of different approaches for genome-based species delineation against ANI. To this end, we compared 1,189 Klebsiella genomes using measures calculated with Mash, Dashing, and DNA compositional signatures, all of which run in a fraction of the time required to obtain ANI. Receiver Operating Characteristic (ROC) curve analyses showed equal quality in species discrimination for ANI, Mash and Dashing, with Area Under the Curve (AUC) values above 0.99, followed by DNA signatures (AUC: 0.96). Accordingly, groups obtained at optimized cutoffs largely agree with species designation, with ANI, Mash and Dashing producing 15 species-level groups. DNA signatures broke the dataset into more than 30 groups. Testing Mash to map species after adding draft genomes to the dataset also showed excellent results (AUC above 0.99), producing a total of 26 Klebsiella species-level groups. The ecological niches of Klebsiella strains were found to neither be related to species delimitation, nor to protein functional content, suggesting that a single Klebsiella species can have a wide repertoire of ecological functions.

Estimation of the generation interval using pairwise relative transmission probabilities

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset

As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited sample sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.

Understanding the relationship between globalization and biophysical resource consumption within safe operating limits for major Belt and Road Initiative countries

Over the past few years, a growing number of scholars have explored environmental deterioration and its connection to various indicators acting as proxies for growth and globalisation. Taking this into view, the current study examines the globalisation-environment nexus, using 66 major countries and administrative regions of the Belt and Road Initiative (BRI) as case studies for 2000-2015. For this analysis, six biophysical resource usages were used within the safe operating space of the planetary boundary concept as proxies for the environmental state, along with the four main and five sub-indices of the Konjunkturforschungsstelle (KOF) globalisation index. Pearson's correlation, hierarchical clustering, redundancy analysis, linear regression, autoregressive integrated moving average (ARIMA) forecasting, etc. were used to infer existing trends, the interactions between the environment and globalisation, a projected future, and coupling with safe operating space aspects. The findings reveal the long-run asymmetric relationship of variables. Surpassing safe operating limits to achieve globalisation is the most prominent outcome. Economic, trade, and financial globalisation are more crucially related to biophysical resource usage. Nitrogen use and material footprint act as strong drivers for various indices of globalisation. At least 40% of countries are above the global average resource usage and 50% have crossed all of the safe operating limits. At the present rate, nearly 51% of countries might cross all their safe operating spaces in 2030. In a race to achieve more globalisation (0.95), more than 30% of countries might cross 5 of the 6 planetary boundaries. Land system change, the biogeochemical cycle, and climate change are impending as the most important domains to be focused on regarding globalisation. Based on the findings, it can be recommended that governments and policymakers devote more attention to reframing and redesigning globalisation to be more environment friendly to achieve long-term sustainable development goals.

Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network

Diabetic retinopathy (DR) is a progressive vascular complication that affects people who have diabetes. This retinal abnormality can cause irreversible vision loss or permanent blindness; therefore, it is crucial to undergo frequent eye screening for early recognition and treatment. This paper proposes a feature extraction algorithm using discriminative multi-sized patches, based on deep learning convolutional neural network (CNN) for DR grading. This comprehensive algorithm extracts local and global features for efficient decision-making. Each input image is divided into small-sized patches to extract local-level features and then split into clusters or subsets. Hierarchical clustering by Siamese network with pre-trained CNN is proposed in this paper to select clusters with more discriminative patches. The fine-tuned Xception model of CNN is used to extract the global-level features of larger image patches. Local and global features are combined to improve the overall image-wise classification accuracy. The final support vector machine classifier exhibits 96% of classification accuracy with tenfold cross-validation in classifying DR images.

tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma

Background

As a new kind of non-coding RNAs (ncRNAs), tRNA derivatives play an important role in gastric carcinoma (GC). Nevertheless, the underlying mechanism tRNA derivatives were involved in was rarely illustrated.

Methods

We screened out the tRNA derivative, tRF-Val-CAC-016, based on the tsRNA sequencing and demonstrated the effect tRF-Val-CAC-016 exerted on GC proliferation in vitro and in vivo. We applied Dual-luciferase reporter assay, RIP assay, and bioinformatic analysis to discover the downstream target of tRF-Val-CAC-016. Then CACNA1d was selected, and the oncogenic characteristics were verified. Subsequently, we detected the possible regulation of the canonical MAPK signaling pathway to further explore the downstream mechanism of tRF-Val-CAC-016.

Results

As a result, we found that tRF-Val-CAC-016 was low-expressed in GC, and upregulation of tRF-Val-CAC-016 could significantly suppress the proliferation of GC cell lines. Meanwhile, tRF-Val-CAC-016 regulated the canonical MAPK signaling pathway by targeting CACNA1d.

Conclusions

tRF-Val-CAC-016 modulates the transduction of CACNA1d-mediated MAPK signaling pathways to suppress the proliferation of gastric carcinoma. This study discussed the function and mechanism of tRF-Val-CAC-016 in GC for the first time. The pioneering work has contributed to our present understanding of tRNA derivative, which might provide an alternative mean for the targeted therapy of GC.

Monitoring COVID-19 Cases and Vaccination in Indian States and Union Territories Using Unsupervised Machine Learning Algorithm

The worldwide spread of the novel coronavirus originating from Wuhan, China led to an ongoing pandemic as COVID-19. The disease being a contagion transmitted rapidly in India through the people having travel histories to the affected countries, and their contacts that tested positive. Millions of people across all states and union territories (UT) were affected leading to serious respiratory illness and deaths. In the present study, two unsupervised clustering algorithms namely k-means clustering and hierarchical agglomerative clustering are applied on the COVID-19 dataset in order to group the Indian states/UTs based on the pandemic effect and the vaccination program from the period of March, 2020 to early June, 2021. The aim of the study is to observe the plight of each state and UT of India combating the novel coronavirus infection and to monitor their vaccination status. The research study will be helpful to the government and to the frontline workers coping to restrict the transmission of the virus in India. Also, the results of the study will provide a source of information for future research regarding the COVID-19 pandemic in India.

Multi-transcriptomic analysis points to early organelle dysfunction in human astrocytes in Alzheimer's disease

The phenotypic transformation of astrocytes in Alzheimer's disease (AD) is still not well understood. Recent analyses based on single-nucleus RNA sequencing of postmortem Alzheimer's disease (AD) samples are limited by the low number of sequenced astrocytes, small cohort sizes, and low number of differentially expressed genes detected. To optimize the detection of astrocytic genes, we employed a novel strategy consisting of the localization of pre-determined astrocyte and neuronal gene clusters in publicly available whole-brain transcriptomes. Specifically, we used cortical transcriptomes from 766 individuals, including cognitively normal subjects (Controls), and people diagnosed with mild cognitive impairment (MCI) or dementia due to AD. Samples came from three independent cohorts organized by the Mount Sinai Hospital, the Mayo Clinic, and the Religious Order Study/Memory and Aging Project (ROSMAP). Astrocyte- and neuron-specific gene clusters were generated from human brain cell-type specific RNAseq data using hierarchical clustering and cell-type enrichment scoring. Genes from each cluster were manually annotated according to cell-type specific functional Categories. Gene Set Variation Analysis (GSVA) and Principal Component Analysis (PCA) were used to establish changes in these functional categories among clinical cohorts. We highlight three novel findings of the study. First, individuals with the same clinical diagnosis were molecularly heterogeneous. Particularly in the Mayo Clinic and ROSMAP cohorts, over 50% of Controls presented down-regulation of genes encoding synaptic proteins typical of AD, whereas 30% of patients diagnosed with dementia due to AD presented Control-like transcriptomic profiles. Second, down-regulation of neuronal genes related to synaptic proteins coincided, in astrocytes, with up-regulation of genes related to perisynaptic astrocytic processes (PAP) and down-regulation of genes encoding endolysosomal and mitochondrial proteins. Third, down-regulation of astrocytic mitochondrial genes inversely correlated with the disease stages defined by Braak and CERAD scoring. Finally, we interpreted these changes as maladaptive or adaptive from the point of view of astrocyte biology in a model of the phenotypical transformation of astrocytes in AD. The main prediction is that early malfunction of the astrocytic endolysosomal system, associated with progressive mitochondrial dysfunction, contribute to Alzheimer's disease. If this prediction is correct, therapies preventing organelle dysfunction in astrocytes may be beneficial in preclinical and clinical AD.

Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations

Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM₁₀). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.

A co-alteration parceling of the cingulate cortex

The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case-control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.

Discrimination of waterborne pathogens, Cryptosporidium parvum oocysts and bacteria using surface-enhanced Raman spectroscopy coupled with principal component analysis and hierarchical clustering

Waterborne pathogens (parasites, bacteria) are serious threats to human health. Cryptosporidium parvum is one of the protozoan parasites that can contaminate drinking water and lead to diarrhea in animals and humans. Rapid and reliable detection of these kinds of waterborne pathogens is highly essential. Yet, current detection techniques are limited for waterborne pathogens and time-consuming and have some major drawbacks. Therefore, rapid screening methods would play an important role in controlling the outbreaks of these pathogens. Here, we used label-free surface-enhanced Raman Spectroscopy (SERS) combined with multivariate analysis for the detection of C. parvum oocysts along with bacterial contaminants including, Escherichia coli, and Staphylococcus aureus. Silver nanoparticles (AgNPs) are used as SERS substrate and samples were prepared with simply mixed of concentrated AgNPs with microorganisms. Each species presented distinct SERS spectra. Principal component analysis (PCA) and hierarchical clustering were performed to discriminate C. parvum oocysts, E. coli, and S. aureus. PCA was used to visualize the dataset and extract significant spectral features. According to score plots in 3 dimensional PCA space, species formed distinct group. Furthermore, each species formed different clusters in hierarchical clustering. Our study indicates that SERS combined with multivariate analysis techniques can be utilized for the detection of C. parvum oocysts quickly.

User Privacy, Surveillance and Public Health during COVID-19 - An Examination of Twitterverse

Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects-contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild.

Parsing heterogeneity within dementia with Lewy bodies using clustering of biological, clinical, and demographic data

BACKGROUND

Dementia with Lewy bodies (DLB) includes various core clinical features that result in different phenotypes. In addition, Alzheimer's disease (AD) and cerebrovascular pathologies are common in DLB. All this increases the heterogeneity within DLB and hampers clinical diagnosis. We addressed this heterogeneity by investigating subgroups of patients with similar biological, clinical, and demographic features.

METHODS

We studied 107 extensively phenotyped DLB patients from the European DLB consortium. Factorial analysis of mixed data (FAMD) was used to identify dimensions in the data, based on sex, age, years of education, disease duration, Mini-Mental State Examination (MMSE), cerebrospinal fluid (CSF) levels of AD biomarkers, core features of DLB, and regional brain atrophy. Subsequently, hierarchical clustering analysis was used to subgroup individuals based on the FAMD dimensions.

RESULTS

We identified 3 dimensions using FAMD that explained 38% of the variance. Subsequent hierarchical clustering identified 4 clusters. Cluster 1 was characterized by amyloid-β and cerebrovascular pathologies, medial temporal atrophy, and cognitive fluctuations. Cluster 2 had posterior atrophy and showed the lowest frequency of visual hallucinations and cognitive fluctuations and the worst cognitive performance. Cluster 3 had the highest frequency of tau pathology, showed posterior atrophy, and had a low frequency of parkinsonism. Cluster 4 had virtually normal AD biomarkers, the least regional brain atrophy and cerebrovascular pathology, and the highest MMSE scores.

CONCLUSIONS

This study demonstrates that there are subgroups of DLB patients with different biological, clinical, and demographic characteristics. These findings may have implications in the diagnosis and prognosis of DLB, as well as in the treatment response in clinical trials.

Forecasting air pollutant concentration using a novel spatiotemporal deep learning model based on clustering, feature selection and empirical wavelet transform

Accurate forecasting of air pollutant concentration is of great importance since it is an essential part of the early warning system. However, it still remains a challenge due to the limited information of emission source and high uncertainties of the dynamic processes. In order to improve the accuracy of air pollutant concentration forecast, this study proposes a novel hybrid model using clustering, feature selection, real-time decomposition by empirical wavelet transform, and deep learning neural network. First, all air pollutant time series are decomposed by empirical wavelet transform based on real-time decomposition, and subsets of output data are constructed by combining corresponding decomposed components. Second, each subset of output data is classified into several clusters by clustering algorithm, and then appropriate inputs are selected by feature selection method. Third, a deep learning-based predictor, which uses three dimensional convolutional neural network and bidirectional long short-term memory neural network, is applied to predict decomposition components of each cluster. Last, air pollutant concentration forecast for each monitoring station is obtained by reconstructing predicted values of all the decomposition components. PM_2.5 concentration data of Beijing, China is used to validate and test our model. Results show that the proposed model outperforms other models used in this study. In our model, mean absolute percentage error for 1, 6, 10 h ahead PM_2.5 concentration prediction is 4.03%, 6.87%, and 8.98%, respectively. These outcomes demonstrate that the proposed hybrid model is a powerful tool to provide highly accurate forecast for air pollutant concentration.

The automation of relevant trial registration screening for systematic review updates: an evaluation study on a large dataset of ClinicalTrials.gov registrations

BACKGROUND

Clinical trial registries can be used as sources of clinical evidence for systematic review synthesis and updating. Our aim was to evaluate methods for identifying clinical trial registrations that should be screened for inclusion in updates of published systematic reviews.

METHODS

A set of 4644 clinical trial registrations (ClinicalTrials.gov) included in 1089 systematic reviews (PubMed) were used to evaluate two methods (document similarity and hierarchical clustering) and representations (L2-normalised TF-IDF, Latent Dirichlet Allocation, and Doc2Vec) for ranking 163,501 completed clinical trials by relevance. Clinical trial registrations were ranked for each systematic review using seeding clinical trials, simulating how new relevant clinical trials could be automatically identified for an update. Performance was measured by the number of clinical trials that need to be screened to identify all relevant clinical trials.

RESULTS

Using the document similarity method with TF-IDF feature representation and Euclidean distance metric, all relevant clinical trials for half of the systematic reviews were identified after screening 99 trials (IQR 19 to 491). The best-performing hierarchical clustering was using Ward agglomerative clustering (with TF-IDF representation and Euclidean distance) and needed to screen 501 clinical trials (IQR 43 to 4363) to achieve the same result.

CONCLUSION

An evaluation using a large set of mined links between published systematic reviews and clinical trial registrations showed that document similarity outperformed hierarchical clustering for identifying relevant clinical trials to include in systematic review updates.

Machine Learning-Based Clustering Analysis: Foundational Concepts, Methods, and Applications

Unsupervised learning, the task of clustering observations in such a way that observations within cluster are more similar than those assigned to other clusters is one the central tasks of data science. Its exploratory and descriptive nature make it one of the most underused and underappreciated methods. In the present chapter we describe its core function with applied examples, explore different approaches, and discuss meaningful applications of the approach for the practicing researcher.

Sheep's coping style can be identified by unsupervised machine learning from unlabeled data

The objective of this study was to define coping style of sheep by using unsupervised machine learning approaches. A total of 105 Norduz sheep (age 3-5 years) were subjected to a 5-minute arena test. Agglomerative Hierarchical Clustering (HCA) was performed on scores of selected principal components retained from Principal Components Analysis (PCA) on arena behaviors to identify sheep coping style. Initially, the variables retained for the PCA were determined with Bartlett's test for sphericity and Kaiser-Meyer-Olkin (KMO) measure of sample adequacy. Seven behavioral variables with KMO values greater than 0.5 were used for final PCA: the average distance to group sheep (DTG), the average distance to stimulus (DTS), the duration of locomotion (LOC), the total number of zone boundaries crossed during the test (CRS), the total number of times that tested sheep sniffed stimulus (NSS), latency to the first sniff the stimulus (LSS), and subjective scores (SCR) scored by an observer on a scale from 1 to 5 (1: extremely calm, 5: extremely restless). The first two components, which were the only ones with an eigenvalue greater than one, accounted for 70.32% of the total variation and were used for clustering analysis. Clustering tendency showed that the scores for the first two components were suitable for clustering (Hopkins' H = 0.852). Several cluster validity indexes were used to obtain aggregated results to determine the most appropriate clustering method and number of clusters. Five different clustering methods: k-means and hierarchical clustering with Ward, average, single and complete linkage were compared. Bootstrap resampling was used to evaluate the stability of a given cluster using the Jaccard coefficient. The clustering method and number of clusters corresponding to the highest rank aggregation score from the bootstrap resampling indicate that the hierarchical clustering method with average linkage and 5 clusters is the most suggested clustering method. However, Ward's algorithm identified the strongest clustering structure for hierarchical clustering, as it had the highest agglomerative coefficient value (0.98). When both Jaccard and aggregation scores are considered together, Ward's method with 3 clusters was selected as the most appropriate method. Sheep were classified into three coping styles (CS) based on HCA results as reactive (Cluster 1, n = 71), intermediate (Cluster 2, n = 22) or proactive (Cluster 3, n = 12). Coping style had significant effect on behavioral variables, DTG, DTS, LOC, CRS and NSS (P < 0.05). The individuals that have proactive coping style had the highest mean values for the variables DTG, DTS and LOC and SCR (P < 0.0001). This indicates that proactive sheep are more active then reactive sheep. The CRS, LOC and NSS mean values were higher for intermediate sheep compared to reactive sheep (P < 0.05). The NSS values were higher for intermediate sheep compare to proactive sheep (P < 0.0001). The findings of the current study show that distinct coping styles in sheep may be identified based on behaviors recorded in an arena test. The findings also revealed that sheep's coping style can be objectively identified by unsupervised machine learning from unlabeled behavioral data.

Data-driven clustering supports adaptive remodeling of athlete's hearts: An echocardiographic study from the Taipei Summer Universiade

BACKGROUND/PURPOSE

Sport-specific adaptations of athlete's hearts are still under investigation. This study sought to 1) identify athlete groups with similar characteristics by clustering echocardiographic data; 2) externally validate the data-driven clusters with sport classifications of various dynamic or static loads to support the conventional hypothesis-driven approach in delineating the athlete's heart.

METHODS

Anthropometric, echocardiographic and electrocardiographic assessments were collected during the 2017 Summer Universiade in Taiwan. Besides standard echocardiography and strain measurements, ventricular-arterial coupling (VAC) was assessed by the ratio of effective arterial elastance (Ea) to left ventricular end-systolic elastance (Ees) as calculated by a modified single-beat algorithm.

RESULTS

We grouped 598 elite athletes (348 male, age 23 ± 2.5 years, across 24 disciplines) using Mitchell's classification. The hypothesis-driven analysis showed dynamic training-related adaptations in heart rate and morphology, including ventricular size, mass, and stroke volume. In comparison, the unsupervised approach found two clusters for each sex. Male athletes participating in high dynamic-load exercises had larger chambers, supranormal diastolic functions, depressed Ees, lower Ea and preserved optimal VAC implicating the resting status of a reservoir-rich pump, which affirmed sport-specific adaptation. The female athletes could be clustered with more noticeable functional alterations, such as depressed biventricular strain. However, the imbalanced number between clusters impeded the validation of load-related remodeling.

CONCLUSION

Hierarchical clustering could analyze complicated multiparametric interactions among numerous echocardiography-derived phenotypes to discern the adaptive propensity of the athlete's heart. The endorsement or generation of hypotheses by a data-driven approach can be applied to various domains.

The feasibility of a dose painting procedure to treat prostate cancer based on mpMR images and hierarchical clustering

BACKGROUND

We aimed to assess the feasibility of a dose painting (DP) procedure, known as simultaneous integrated boost intensity modulated radiation Therapy (SIB-IMRT), for treating prostate cancer with dominant intraprostatic lesions (DILs) based on multi-parametric magnetic resonance (mpMR) images and hierarchical clustering with a machine learning technique.

METHODS

The mpMR images of 120 patients were used to create hierarchical clustering and draw a dendrogram. Three clusters were selected for performing agglomerative clustering. Then, the DIL acquired from the mpMR images of 20 patients were categorized into three groups to have them treated with a DP procedure being composed of three planning target volumes (PTVs) determined as PTV1, PTV2, and PTV3 in treatment plans. The DP procedure was carried out on the patients wherein a total dose of 80, 85 and 91 Gy were delivered to the PTV1, PTV2, and PTV3, respectively. Dosimetric and radiobiologic parameters [Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP)] of the DP procedure were compared with those of the conventional IMRT and Three-Dimensional Conformal Radiation Therapy (3DCRT) procedures carried out on another group of 20 patients. A post-treatment follow-up was also made four months after the radiotherapy procedures.

RESULTS

All the dosimetric variables and the NTCPs of the organs at risks (OARs) revealed no significant difference between the DP and IMRT procedures. Regarding the TCP of three investigated PTVs, significant differences were observed between the DP versus IMRT and also DP versus 3DCRT procedures. At post-treatment follow-up, the DIL volumes and apparent diffusion coefficient (ADC) values in the DP group differed significantly (p-value < 0.001) from those of the IMRT. However, the whole prostate ADC and prostate-specific antigen (PSA) indicated no significant difference (p-value > 0.05) between the DP versus IMRT.

CONCLUSIONS

The results of this comprehensive clinical trial illustrated the feasibility of our DP procedure for treating prostate cancer based on mpMR images validated with acquired patients' dosimetric and radiobiologic assessment and their follow-ups. This study confirms significant potential of the proposed DP procedure as a promising treatment planning to achieve effective dose escalation and treatment for prostate cancer.

TRIAL REGISTRATION

IRCT20181006041257N1; Iranian Registry of Clinical Trials, Registered: 23 October 2019, https://en.irct.ir/trial/34305 .