Multivariate Statistical Methods Research Articles

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

Tracking the spatial footprints of extreme storm surges around the coastline of the UK and Ireland

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.

Screening Iranian endemic coriander genebank germplasm for drought tolerance using a new multivariate statistical method

Drought is one of the serious abiotic factors influencing crop production such as coriander. Development of tolerant genotypes is prevented by the lack of effective selection criterion. Objectives of this study were evaluation of coriander accessions for water deficit stress and introduce a new multivariate method to select drought tolerant genotypes. For investigation of 19 traits, 16 Iranian endemic coriander genotypes were grown in a glasshouse under control and water deficit stress conditions. Shoot dry weight (SDW), fruit weight per plant (FWPP), fruit number per plant (FNPP) and umbel number per plant (UNPP) were decreased (Susceptibility Index>38%) under water deficit stress condition compared with the control condition. While the mean values of root dry weight (RDW) and root to shoot ratio (RTSR) were increased 1.49% and 97.33% under water stress condition, respectively. Because of high inheritance, high expected genetic gain, high genotypic correlation with together, well response to drought stress and high explanation of FWPP variation in regression model, the FWPP, branch number per plant (BNPP), FNPP and SPAD chlorophyll content in grain filing stage (SCCIGFS) traits were selected to screen coriander genotypes for drought tolerance in coriander. The principal component analysis mediated method (PCAMM) indicated as comprehensive criterion to screen drought tolerant genotypes. This method was highly heritabl, able to separate the Fernandez described A, B, C and D groups, no multicollinear and using multiple drought tolerance related traits. The PCAMM results showed that G13, G16, G2 and G12 genotypes belonged to Fernandez described A, B, C and D groups, respectively.

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system’s performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

Study on the Hydrochemical Characteristics and Evolution Law of Taiyuan Formation Limestone Water under the Influence of Grouting with Fly Ash Cement: A Case Study in Gubei Coal Mine of Huainan, China

The hydrogeological conditions of Huainan Coalfield are complex. The Taiyuan formation limestone water (Taihui water) in this area is a direct threat to the water source of the 1# coal mining floor. In order to prevent and control water disasters, Gubei Coal Mine adopted ground high-pressure grouting with fly ash cement to block the hydraulic connection between the Taiyuan formation limestone aquifer and the Ordovician limestone aquifer. However, the injected slurry will destroy the original hydrochemical balance of Taihui water and change its hydrochemical characteristics. Taking the influence area of the 2# karst collapse column in the Beiyi 1# coal mining area of Gubei Coal Mine as an example, a total of 25 Taihui water samples were collected. The hydrochemical characteristics and evolution law of Taihui water before and after grouting are studied via the multivariate statistical method. The research methods include constant index statistics, Piper diagram, correlation analysis, ion combination ratio, and saturation index analysis. The results show that after grouting, the concentrations of Na+ + K+, Ca2+, Mg2+, and Cl− in Taihui water decrease, while the concentrations of SO42− and HCO3− increase. The average values of PH and TDS become larger. The hydrochemical types of Taihui water are more concentrated, mainly HCO3-Na and Cl-Na. The correlations between conventional indicators decrease. According to the analysis of ion combination ratio, dissolution, cation exchange, and pyrite oxidation mainly occur in Taihui water, and these effects are enhanced after grouting. The saturation index results show that after grouting, the saturation index of dolomite, calcite, and gypsum is significantly reduced, and the saturation index of rock salt is slightly increased. The conclusion of this study is that the hydrochemical characteristics of Taihui water are greatly affected by fly ash cement. Moreover, because fly ash cement contains a lower calcium oxide content than ordinary Portland cement, the effect of fly ash cement on the ion concentration of Taihui water and the resulting hydrogeochemical effect are significantly different. Therefore, in the treatment of mine water disasters, the hydrogeochemical evolution law affected by fly ash cement grouting should be identified.

Check your outliers! An introduction to identifying statistical outliers in R with easystats.

Beyond the challenge of keeping up to date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the {performance} package of the easystats ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency. A preprint of this paper is available at: 10.31234/osf.io/bu6nt.

Upper control limit calculation for bad data detection in spatiotemporal power quality massive data and its application to harmonic load flow study

The current monitoring of modern distribution networks by operators with the aim to improve the network visibility and thus detect and mitigate challenges such as power quality issues has resulted in massive data generation. Such data need to be analysed and technical observations realised. Thus, this paper presents bad data detection for spatiotemporal harmonic power quality field measurements obtained from six power quality analysers (installed at the connection points of customer-owned photovoltaic and small-scale energy storage and the mains units) with high level of granularity (1s interval). A total of 4.1 billion measured values have been evaluated in the bad data detection. Multivariate statistical methods (principal component analysis and Hotelling’s T2 statistic) have been combined with Bag of Little Bootstrap (BLB) to analyse the massive data. Specifically, the BLB technique has been deployed for calculating the upper control limit for the bad data detection, thus, the spatiotemporal nature of the data has been fully captured. The method has been applied to harmonic load flow analysis of a practical distribution network. The findings reveal how the neglect of spatiotemporality could conceal the actual harmonic penetration levels in a data-driven harmonic load flow analysis. Other interesting technical findings are presented.

A New 4-Thiazolidinone Derivative (Les-6490) as a Gut Microbiota Modulator: Antimicrobial and Prebiotic Perspectives

A novel 4-thiazolidinone derivative Les-6490 (pyrazol-4-thiazolidinone hybrid) was designed, synthesized, and characterized by spectral data. The compound was screened for its antimicrobial activity against some pathogenic bacteria and fungi and showed activity against Staphylococcus and Saccharomyces cerevisiae (the Minimum Inhibitory Concentration (MIC) 820 μM). The compound was studied in the rat adjuvant arthritis model (Freund’s Adjuvant) in vivo. Parietal and fecal microbial composition using 16S rRNA metagenome sequences was checked. We employed a range of analytical techniques, including Taxonomic Profiling (Taxa Analysis), Diversity Metrics (Alpha and Beta Diversity Analysis), Multivariate Statistical Methods (Principal Coordinates Analysis, Principal Component Analysis, Non-Metric Multidimensional Scaling), Clustering Analysis (Unweighted Pair-group Method with Arithmetic Mean), and Comparative Statistical Approaches (Community Differences Analysis, Between Group Variation Analysis, Metastat Analysis). The compound significantly impacted an increasing level of anti-inflammatory microorganisms (Blautia, Faecalibacterium prausnitzii, Succivibrionaceae, and Coriobacteriales) relative recovery of fecal microbiota composition. Anti-Treponemal activity in vivo was also noted. The tested compound Les-6490 has potential prebiotic activity with an indirect anti-inflammatory effect.

Investigations of Chemical Element Distributions in Soil, North Macedonia—A Review

This review article considers the following aspects: naturally distributed chemical elements and their enrichments, and the increased occurrence of PTEs due to anthropogenic and urban activities, as well as due to the geochemical uniqueness of certain geochemical landscapes, depending on the lithological environment. The review article is the result of many years of successful cooperation between the Geological Survey of Slovenia and the Faculty of Natural Sciences in Skopje, Ss. Cyril and Methodius University in Skopje, as well as several other institutions from North Macedonia, Russia and Romania but, also, through the voluntary and enthusiastic work of Prof. Trajče Stafilov’s PhD and Master’s students. To create the Geochemical Atlas, the territory of North Macedonia was covered with 995 sampling locations, but 16 separate areas with soil contamination were additionally sampled. The total sum of all collected soil samples was 3983 from 2449 different sampling sites in the period from 2006 to 2017. The analyses were performed at the Institute of Chemistry, Faculty of Natural Sciences in Skopje, at the Ss. Cyril and Methodius University in Skopje, North Macedonia, at the Research Institute for Analytical Instrumentation (ICIA), Cluj-Napoca, Romania, at the Joint Institute for Nuclear Research in Dubna, Moscow Region, Russia, and at Acme Labs in Vancouver, Canada. The sum of all analysed soil samples in all four mentioned laboratories was 7991 from 2006 to 2017. Using advanced mathematical methods such as multivariate statistical methods (HCA, FA, PCA) and artificial neural networks–multilayer perceptron (ANN-MP), predictions were made about the concentrations of potentially toxic elements (PTEs) and their distribution in real space. In less than two decades (2007–2023) of fruitful collaboration, a large number of scientific works have been published: 188 scientific publications, 8 geochemical atlases and 23 chapters in monographs.

Typology and classification of water quality in an intermittent river in a semi-arid Mediterranean climate.

The typology and classification of rivers are highly relevant concepts in the field of limnology and freshwater ecology. Water body typology systematically categorizes water bodies based on their natural attributes, while water body classification groups them based on specific criteria or purposes for management, regulatory, or administrative reasons. Both concepts play important roles in understanding and managing water resources effectively. This scientific article focuses on the ZAT River in Morocco as a model for studying low-flow and intermittent rivers. The objective is to develop an accurate model for the typology and classification of small, low-flow rivers into homogeneous classes based on natural and anthropogenic factors. The study also investigates the impact of human activities on altering the uniformity and reference nature of the water body. The typology of water bodies is carried out according to the European methodology specified in The European Commission's Water Framework Directive (WFD) in 2000. The classification of water bodies is conducted by assessing their chemical and biological quality using the weighted index (WI), the Iberian Biological Monitoring Working Group (IBMWP) index, and multivariate statistical methods such as principal component analysis (PCA) for confirming water quality assessment. The results indicate the possibility of dividing the basin into four water bodies. Water bodies show homogeneity in terms of chemical quality when human influence is minimal or during periods of high river flow. However, increased human influence and decreased river flows lead to heterogeneity in chemical quality, indicating an unstable state. This study is the first of its kind in arid and semi-arid intermittent rivers, where such an approach could be suggested to determine their typology and classification.

Evaluation of growth performance, carcass yield, and proximate composition of fillets of male and female pirarucu (Arapaima gigas) during the fattening phase

The objective of this study was to evaluate the comparative growth performance, carcass yield, and the proximate composition of the muscle between male and female Arapaima gigas (pirarucu) during the fattening phase. Juvenile pirarucu (n = 114; 1126 ± 219 g; 55 ± 3 cm) were kept in 120 m2 brick tanks at a stocking density of 1.55 g m-2. Fish were raised under the same management conditions during 427 days and biometrics were performed monthly to evaluate the growth performance. Fish (n = 15 per collection day) were sampled at 91, 187, 273, 341 and 427 days to evaluate the carcass yield, proximate composition of the muscle and to determine its gender via histological analysis of the gonads. Data were analyzed using a multivariate statistical method. Growth performance, carcass yield and proximate composition of the muscle were similar between genders up to 427 days of rearing. The coefficients of variation in total weight and length that express the batch heterogeneity were higher for females throughout practically the whole experimental period. This was probably due to the dominance behavior between female fish, which also justifies why some females reached larger sizes at the end of the experiment. The yields for the fillet, abdominal muscle and loin were greater at 273 days compared to other sampling events, regardless of gender. Partial harvesting of pirarucu during the fattening phase can therefore be a management strategy for slaughtering fish with a higher meat yield.

Regional covariance of white matter hyperintensity volume patterns associated with hippocampal volume in healthy aging.

Hippocampal volume is particularly sensitive to the accumulation of total brain white matter hyperintensity volume (WMH) in aging, but how the regional distribution of WMH volume differentially impacts the hippocampus has been less studied. In a cohort of 194 healthy older adults ages 50-89, we used a multivariate statistical method, the Scaled Subprofile Model (SSM), to (1) identify patterns of regional WMH differences related to left and right hippocampal volumes, (2) examine associations between the multimodal neuroimaging covariance patterns and demographic characteristics, and (3) investigate the relation of the patterns to subjective and objective memory in healthy aging. We established network covariance patterns of regional WMH volume differences associated with greater left and right hippocampal volumes, which were characterized by reductions in left temporal and right parietal WMH volumes and relative increases in bilateral occipital WMH volumes. Additionally, we observed lower expression of these hippocampal-related regional WMH patterns were significantly associated with increasing age and greater subjective memory complaints, but not objective memory performance in this healthy older adult cohort. Our findings indicate that, in cognitively healthy older adults, left and right hippocampal volume reductions were associated with differences in the regional distribution of WMH volumes, which were exacerbated by advancing age and related to greater subjective memory complaints. Multivariate network analyses, like SSM, may help elucidate important early effects of regional WMH volume on brain and cognitive aging in healthy older adults.

Subgingival biofilm microbiome in individuals with asthma and periodontitis: Metagenomic analysis.

This observational study aimed to explore the metagenomics of subgingival biofilms in individuals with varying degrees of asthma, from severe to none, to elucidate the association between the subgingival microbiome and asthma. Subgingival biofilm samples were collected from thirty participants at the Asthma Control Program Outpatient Clinic in Bahia (ProAR). These samples were categorized into six groups based on the severity of asthma and the presence or absence of periodontitis. We employed next-generation sequencing (Illumina MiSeq), targeting the 16S rRNA gene, to characterize the microbial communities present. Our analysis included descriptive statistics and sequencing data, evaluated using multivariate statistical methods such as the Shannon index, principal coordinate analysis, and the Bray-Curtis dissimilarity. Our findings indicate a higher prevalence of periodontally detrimental bacterial genera in individuals with severe asthma and periodontitis. Additionally, individuals with asthma, but without periodontitis, exhibited a tendency toward dysbiosis, particularly in cases of severe asthma. This research provides new insights into the composition of the subgingival microbiome in individuals with varying severities of asthma and periodontitis. The genera identified in this study underscore the need for further investigations to build upon these findings.

Psychological harassment at work in Hungary and Slovakia

Abstract Bullying, harassment, and sexual harassment in the workplace are not new issues; they have always existed in various forms. Recent studies have highlighted their impacts on workers and their well-being at work. However, the perception of these problems differ across the United States, Europe, and Asia. These differences are reflected not only in terms of sensitivities, workplace policies, and relevant laws, but also in the language/vocabulary used to designate the same. Psychological harassment is a bitter issue for many who are affected by it. Unfortunately, it remains a less popular topic in academic research. This may be due to the sensitivity associated with the topic, the possible shame and value judgements of those involved, and the myriad cultural differences that complicate its perceptions. This study deals with the characteristics of psychological harassment in workplaces across Hungary and Slovakia. It addresses the following questions: How are the sources of psychological bullying perceived in the two countries, how do victims tolerate the abuse they are subjected to, and what factors influence the same. The study also examines whether the company’s tools for combating psychological bullying in the workplace have an impact on an employee’s decision to leave the organization as a result of mobbing. It conducted quantitative surveys involving 500 Hungarian and 108 Slovakian employees. The results were analyzed by the authors using SPSS version 28 with univariate and multivariate statistical methods. Frequency analysis, ANOVA, T- test and factor analysis, among others, were performed. The results show that the perception of the sources of psychological bullying and the tolerance of the related phenomena depend on a number of personal factors. However, the tools that firms use to deal with mobbing do not necessarily play a determining role in an employee’s decision to leave their workplace following psychological harassment.

Leveraging multivariate approaches to advance the science of early-life adversity

Since the landmark Adverse Childhood Experiences (ACEs) study, adversity research has expanded to more precisely account for the multifaceted nature of adverse experiences. The complex data structures and interrelated nature of adversity data require robust multivariate statistical methods, and recent methodological and statistical innovations have facilitated advancements in research on childhood adversity. Here, we provide an overview of a subset of multivariate methods that we believe hold particular promise for advancing the field's understanding of early-life adversity, and discuss how these approaches can be practically applied to explore different research questions. This review covers data-driven or unsupervised approaches (including dimensionality reduction and person-centered clustering/subtype identification) as well as supervised/prediction-based approaches (including linear and tree-based models and neural networks). For each, we highlight studies that have effectively applied the method to provide novel insight into early-life adversity. Taken together, we hope this review serves as a resource to adversity researchers looking to expand upon the cumulative approach described in the original ACEs study, thereby advancing the field's understanding of the complexity of adversity and related developmental consequences.

Assessment of the Relationship Between Humic Acid Contents and Trace Elements of Some Agricultural Soils in Diyarbakır Region by Multivariate Statistical Methods

There are important relationships between humic acid (HA) and the bioavailability, reactions and mobility of trace elements in the soil. For this reason, soils are tried to be improved chemically, biologically and physically with HA applications. In this study, the relationship of humic acid contents of 118 agricultural soil samples from Diyarbakır region with some trace elements (Al, As, Ba, Be, Cd, Fe, Mn, Pb, Sb, Sn, Se, V and P) was evaluated by multivariate statistical analysis. After the soil samples were solubilized by the microwave wet digestion method, the element contents were determined with the ICP OES (Inductively Coupled Plasma Optical Emission Spectrometer) device. SRM NIST 2586 was used as SRM (Standard Reference Material) for the accuracy of the method. Recovery values were found between 91.6% and 105.9% as a result of the analysis. Humic acid was extracted from soils by the International Society for Humic Substances (IHSS) method and determined using a shaker and centrifuge device. For the accuracy of the method, it was tested with Humic Acid Sodium Salt (HA-Na). Pearson correlation and partial correlation analysis were applied to the obtained data set. In addition, multivariate statistical analyses such as multiple regression HCA (Hierarchical Cluster Analysis) and PCA (Principal Component Analysis) were applied. Multiple regression analysis was performed according to the Step-wise method. Manganese and P (p< 0.01) were significant when HA was taken as the dependent variable. According to the Pearson correlation coefficient, the correlation between HA and As (r = -0.282**) in soil was negative and significant, while Fe (r = 0.185*), Mn (r = 0.273**)), Sn (r = 0.242*), Se (r = 0.325**) and P (r = 0.315**) were determined as positive and significant. In clustering and PCA analysis, HA, P Mn and Fe were found to be in the same group. The analyses have shown that HA has a positive effect on the plant nutrients in the soil.

Survey of state laws banning the use of conventional plastic bags in Brazil and their similarities

Many countries have created laws limiting some plastic-based products, such as conventional bags, but Brazil does not have a federal law to this effect, leaving only state regulations. In this perspective, the study aimed to identify and analyze which Brazilian states have passed laws restricting the use of plastic bags, verifying their differences and similarities. The article was developed by mapping the state laws, categorizing them based on their respective texts for subsequent analysis using multivariate statistical methods. Only 12 of Brazil's 27 states have passed laws banning the use of non-biodegradable plastic bags, half of which address the restrictions more broadly with at least two provisions, while the others are limited to just one. It was noted that some laws have more educational instruments to the detriment of others with more coercive measures. Therefore, it was observed that many states continue to show no concern about the impacts of this material, and there are significant differences between those that already exist.

Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

URBAN WATER SECURITY PROTECTION: IDENTIFYING POLLUTION SOURCES IN JURU RIVER BASIN USING CHEMOMETRICS

The consumption of surface water is becoming increasingly significant as a main solution for Malaysia's issues with water supply, especially in urban areas. The study addresses the protection of urban water security by multivariate analysis, evaluating trends in the distribution of water quality parameters and identifies the primary sources and processes involved in water quality contamination in the Malaysian Juru River Basin. Conventional graphical and multivariate statistical methods HACA and PCA from chemometric techniques were used. The data collected in the Juru River was subjected to this investigation, which recorded 19 physical-chemical and microbiological characteristics at two sampling locations throughout the Juru River Basin. Consequently, the HACA was effectively split into the downstream and upstream areas. Six VFs are displayed by PCA in the high pollution source area (HPS), which represents 81.11% of the variance. The main cause of a decrease in water quality in the downstream areas of the Juru River Basin is anthropogenic pollution, or pollution caused by human activities. The study concludes by demonstrating how chemometric techniques can be used to identify significant details about their capacity to interpret complex data that determines the Juru River Basin's spatial and temporal variation in water quality distribution trends into MPS and HPS areas to ensure the urban water security protection.

Pan-Indian Clinical Registry of Invasive Fungal Infections Among Patients in the Intensive Care Unit: Protocol for a Multicentric Prospective Study.

Fungal infections are now a great public health threat, especially in those with underlying risk factors such as neutropenia, diabetes, high-dose steroid treatment, cancer chemotherapy, prolonged intensive care unit stay, and so on, which can lead to mycoses with higher mortality rates. The rates of these infections have been steadily increasing over the past 2 decades due to the increasing population of patients who are immunocompromised. However, the data regarding the exact burden of such infection are still not available from India. Therefore, this registry was initiated to collate systematic data on invasive fungal infections (IFIs) across the country. The primary aim of this study is to create a multicenter digital clinical registry and monitor trends of IFIs and emerging fungal diseases, as well as early signals of any potential fungal outbreak in any region. The registry will also capture information on the antifungal resistance patterns and the contribution of fungal infections on overall morbidity and inpatient mortality across various conditions. This multicenter, prospective, noninterventional observational study will be conducted by the Indian Council of Medical Research through a web-based data collection method from 8 Advanced Mycology Diagnostic and Research Centers across the country. Data on age, gender, clinical signs and symptoms, date of admission, date of discharge or death, diagnostic tests performed, identified pathogen details, antifungal susceptibility testing, outcome, and so on will be obtained from hospital records. Descriptive and multivariate statistical methods will be applied to investigate clinical manifestations, risk variables, and treatment outcomes. These Advanced Mycology Diagnostic and Research Centers are expected to find the hidden cases of fungal infections in the intensive care unit setting. The study will facilitate the enhancement of the precision of fungal infection diagnosis and prompt treatment modalities in response to antifungal drug sensitivity tests. This registry will improve our understanding of IFIs, support evidence-based clinical decision-making ability, and encourage public health policies and actions. Fungal diseases are a neglected public health problem. Fewer diagnostic facilities, scanty published data, and increased vulnerable patient groups make the situation worse. This is the first systematic clinical registry of IFIs in India. Data generated from this registry will increase our understanding related to the diagnosis, treatment, and prevention of fungal diseases in India by addressing pertinent gaps in mycology. This initiative will ensure a visible impact on public health in the country. DERR1-10.2196/54672.