Characterization and Classification of ADHD Subtypes: An Approach Based on the Nodal Distribution of Eigenvector Centrality and Classification Tree Model

In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis and associated subtype identification majorly rely on the baseline correlation matrix obtained from the functional MRI scan. Thus, the existing protocols are either full of personalized bias or computationally expensive as network complexity-based simple but deterministic protocols are yet to be developed and formalized. This article proposes a deterministic method to identify and differentiate the common ADHD subtypes, which is based on a single complexity measure, namely the eigenvector centrality. The node-wise centrality differences were explored using a classification tree model (p < 0.05) to diagnose the subtypes. Identification of marker nodes from default mode, visual, frontoparietal, limbic, and cerebellar networks strongly vouch for the involvement of multiple brain regions in ADHD neuropathology.

Multi-criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment.

Pollution indices and correlation of heavy metals contamination in the groundwater around brick kilns in Jammu and Kashmir, India

The present investigation focuses on assessing the water quality of groundwater surrounding brick kilns in the Jammu district of Jammu and Kashmir (J&K). At 43 different brick kiln sites in both north and south regions of Jammu, concentrations of heavy metals were measured using established techniques. The elements zinc, copper, iron, lead, cadmium, nickel, and manganese were analyzed utilizing an Atomic Absorption Spectrophotometer (AAS). The pollution load index value was consistently below unity across all sites, suggesting an absence of pollution and making the water suitable for consumption. The average concentrations, listed in ascending order, were found to be 0.38 mg/L for copper, 0.31 mg/L for zinc, 0.01 mg/L for iron, and 0.09 mg/L for manganese. Notably, concentrations of lead, cadmium, and nickel were found below the detectable levels. Evaluation of contamination factors revealed the sequence Cu > Fe > Zn > Mn, while the geo accumulation index followed the sequence Cu > Fe > Mn > Zn. Comparison of these findings with the established standards of World Health Organization and Bureau of Indian Standards indicated that the recorded ranges were within permissible limits. The study's outcomes suggest that heavy metal emissions from brick kilns may not significantly impact the quality of groundwater. Elevated copper levels found near brick kilns were likely to result from plumbing materials in the study area. Iron and manganese in groundwater seems to have geo-genic origin and not emission-related. This research represents a foundational step in examining groundwater contamination by heavy metals specifically in the neighborhood of brick kilns in Jammu district. It contributes to the establishment of a comprehensive database and serves as a reference point for future studies. Additionally, the study recommends regular monitoring of groundwater to ensure the maintenance of drinking water quality.

A correlational study of uranium in groundwater with other physicochemical parameters using GIS mapping in Godda district of Jharkhand, India

The present research concentrates on the cumulative use of GPS and GIS technologies, which are excellent resources for analyzing and monitoring divergent physicochemical parameters in groundwater, including pH, TDS, EC, ORP, Ca+2, Mg+2, NO3-, F-, SO4-2, Cl- and PO4-3 with explicit regard to uranium. Garmin GPS is used to record the locations of the sampling points in the Godda study area. The research aims to offer a thorough understanding of the relationship between soil and water, its impact on public health and the extent to which water can be used in various ways based on its quality. Utilizing the inverse distance weighted (IDW) technique, it is examined how these groundwater parameters and the Water Quality Index (WQI) can be estimated spatially. Additionally, a correlation analysis of the water quality parameters is computed to estimate the local population's cancer risk living in the study area. Except for calcium and magnesium, which are present in excess concentrations throughout the study area with the highest values of 325 and 406 mg/l, respectively at Amediha and Meherma, the results showed that the maximum concentration parameters are within limits with the standard. The main reason might be the area's predominance of Alfisol soil type. The radioactive element uranium is found to be in a limited  range. Chemo-toxicity and radiological risk assessment of the whole area lie far below the restricted cancer risk limit i.e., 30 ppb with the highest concentration of 14 ppb in the 'Sunderpahari' region, following the results obtained. The WQI for the area ranges from 'good' to 'very poor.' The results were favorable but a few sites such as 'Boarijor' and its surroundings, require additional attention to enhance groundwater quality. Given uranium's low availability in groundwater the region's cancer risk assessment is below average.

How does the COVID-19-related restriction affect the spatiotemporal variability of ambient air quality in a tropical city?

The ambient air, a significant hazard to human health in most Indian cities, including Rourkela, is something we are strangely neglecting in the age of industrialization and urbanization. High levels of particulate matter released from various anthropogenic sources over the past decade have had a significant negative impact on the city. The COVID-19 lockdown situation brings understanding and realization towards the improvement of air quality and its subsequent effects. The present study investigates the impact of the COVID-19-related lockdown on the spatiotemporal variation of the ambient air quality in Rourkela City with a tropical climatic setup. The concentration and distribution of various pollutants are well explained by the wind rose and Pearson correlation. There is considerable spatiotemporal variation in the city's ambient air quality, as determined by a two-way ANOVA test comparing sampling sites and months. During the COVID-19 lockdown phases, the air quality of Rourkela witnessed an improvement in annual AQI ranging from 12.64 to 26.85% across the city. However, the air quality in the city deteriorated by 13.76-65.79% after the revocation of COVID-19 restrictions. The paired sample T-test justified that the air quality of Rourkela was significantly healthier in 2020 compared to both 2019 and 2021. Spatial interpolation reveals that the ambient air quality of Rourkela ranged from satisfactory to moderate categories throughout the entire study period. 31.93% area of the city has experienced an improvement in AQI from the Moderate to the satisfying category from 2019 to 2020, whereas about 68.78% area of the city has witnessed a decline in AQI from satisfactory to moderate category from 2020 to 2021.

Multi-geochemical background comparison and the identification of the best normalizer for the estimation of PTE contamination in agricultural soil

Identifying a suitable geochemical background level (GBL) and an appropriate normalizer is imperative for ensuring soil quality, health, and security. The objective of this study was to identify the appropriate normalizer and suitable GBL for determining PTE enrichment levels in agricultural soils and investigate if there are any statistical differences due to the GBL [World Average Value (WAV) European Average Value (EAV)] used. Forty-nine topsoil samples were obtained from seven agricultural communities in the Frdek-Mstek District (Czech Republic). Portable X-ray fluorescence was used to determine the total PTEs (Cr, Ni, Cu, Y, Ba, Th, As, Pb, and Zn) concentration levels in the soil. Correlation matrix analysis was used to determine the metallic relationship between the PTEs and the normalizers (Al, Fe, Ti, Zr, Sr and Rb). Pollution indices such as contamination factor (CF), geoaccumulation index (Igeo) and enrichment factor (EF) analysis were used to determine the most suitable GBL. Al, Fe, Sr, Ti and Rb strongly correlated with the CF, Igeo and EF, whereas WAV performed better than the other geochemical background (EAV). The results indicated that Rb was the suitable normalizer and WAV was the appropriate GBL for agricultural soil and provided a foundation for evaluating and surveilling soil quality and health in agricultural soil.

Multivariate classification of cannabis chemovars based on their terpene and cannabinoid profiles

Cannabis is used to treat various medical conditions, and lines are commonly classified according to their total concentrations of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Based on their ratio of total THC to total CBD, cannabis cultivars are commonly classified into high-THC, high-CBD, and hybrid classes. While cultivars from the same class have similar compositions of major cannabinoids, their levels of other cannabinoids and their terpene compositions may differ substantially. Therefore, a more comprehensive and accurate classification of medicinal cannabis cultivars, based on a large number of cannabinoids and terpenes is needed. For this purpose, three different chemometric-based classification models were constructed using three sets of chemical profiles. We examined those models to determine which provides the most accurate "chemovar" classification. This was done by analyzing profiles of cannabinoids, terpenes, and the combination of these substances using the partial least square-discriminant analysis multivariate (PLS-DA) technique. The chemical profiles were selected from the three major classes of medicinal cannabis that are most commonly prescribed to patients in Israel: high-THC, high-cannabigerol (CBG), and hybrid. We studied the correlations between cannabinoids and terpenes to identify major bio-indicators representing the plant's terpene and cannabinoid content. All three PLS-DA models provided highly accurate classifications, utilizing six to nine latent variables with an overall accuracy ranging from 2 to 11% CV. The PLS-DA model applied to the combined cannabinoid-and-terpene profile did the best job of differentiating between the chemovars in terms of misclassification error, sensitivity, specificity, and accuracy. The combined cannabinoid-and-terpene PLS-DA profile had cross-validation and prediction misclassification errors of 4% and 0%, respectively. This is the first study to demonstrate the highly accurate classification of samples of medicinal cannabis based on their cannabinoid and terpene profiles, as compared to cannabinoid profiles alone. Furthermore, our correlation analysis indicated that 11 cannabinoids and terpenes might serve as bio-indicators for 32 different active compounds. These findings suggest that the use of multivariate statistics could assist in breeding studies and serve as a tool for minimizing the mislabeling of cannabis inflorescences.

SepNet: A neural network for directionally correlated data

Multi-dimensional tensor data appear in diverse settings, including multichannel signals, spectrograms, and hyperspectral data from remote sensing. In many cases, these data are directionally correlated, i.e. the correlation between variables from different dimensions is significantly weaker than the correlation between variables from the same dimension. Convolutional neural networks are readily applicable to directionally correlated data but are often inefficient, as they impose many unnecessary connections between neurons. Here we propose a novel architecture, SepNet, specifically for directionally correlated datasets. SepNet uses directional operators to extract directional features from each dimension separately, followed by a linear operator along the depth to generate higher-level features from the directional features. Experiments on two representative directionally correlated datasets showed that SepNet improved network efficiency up to 100-fold while maintaining high accuracy comparable with state-of-the-art convolutional neural network models. Furthermore, SepNet can be flexibly constructed with minimal restriction on the output shape of each layer. These results reveal the potential of data-specific architecting of neural networks.

Isotopic signatures, hydrochemical and multivariate statistical analysis of seawater intrusion in the coastal aquifers of Chennai and Tiruvallur District, Tamil Nadu, India

In coastal aquifers, seawater intrusion is a significant groundwater issue. The research paper contributes to the understanding of the consequences of seawater intrusion in the Chennai coastal aquifer from Foreshore Estate to Thirunilai along the coastline. 110 groundwater samples were collected and analyzed for physicochemical parameters such as pH, (EC), (TDS), (TH), major anions (Cl^-, NO₃^-, HCO₃^2-, and SO₄^2-), and cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺) during the pre-monsoon (June 2014) and post-monsoon (January 2015) seasons. Stable isotopic analyses of ¹⁸O were performed on 24 groundwater samples collected from various locations throughout the research region based on EC, TDS, Na, and Cl^- concentrations for both seasons. The stable isotopic composition of ¹⁸O and Deuterium in groundwater samples was determined for the study region. According to the Correlation matrix and Factor analysis, the main contributors to groundwater salinity as a result of seawater intrusion into the coastal aquifer are EC, TDS, Na⁺, and Cl^-. GMWL exhibits a similar pattern, and the samples have been classified into various molar ratio diagrams to identify seawater intrusions for better evaluation. The result revealed that seasonal, geogenic, and anthropogenic factors always make a significant contribution to the heterogeneous chemistry of groundwater.

Dataset on the evaluation of hydrochemical properties and groundwater suitability for irrigation purposes: South-western part of Jashore, Bangladesh

The data herein presented concerns the article entitled “Evaluation of hydrochemical properties and groundwater suitability for irrigation uses in southwestern zones of Jashore, Bangladesh”. Data was collected during 2018-2019 in the southwestern zones of Jashore, Bangladesh. One hundred groundwater samples (boreholes and tube wells) were collected to evaluate groundwater quality, using the irrigation water quality index (IWQI) as an indicator. Fourteen hydrochemical parameters (pH, EC, TDS, NO₃N, pH, EC, Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl^−, HCO₃⁻, SO₄²⁻ and Fe²⁺) were used to calculate irrigation water quality indices (KI, Na%, PI, SAR, SSP, MH, and TH). Statistical methods such as Viper diagrams, USSL, and Wilcox diagrams were used to visualize datasets. The attained data can be used to assess the hydrogeochemistry of the sampled sites and groundwater quality for irrigation purposes. The findings of this work can be used in the optimization of management and treatment procedures and in the implementation of sustainable water development.

Genetic evaluation for latent variables derived from factor analysis in broilers

1. The aim of this study was to investigate the associations between several carcass, performance and meat quality traits in broilers through factor analysis and use the latent variables (i.e. factors) as pseudo-phenotypes in genetic evaluations.2. Factors were extracted using the principal components method and varimax rotation algorithm. Genetic parameters were estimated via Bayesian inference under a multiple-trait animal model.3. All factors taken together explained 71% of the original variance of the data. The first factor, denominated as 'weight', was associated with carcass and body weight traits; and the second factor, defined as 'tenderness', represented traits related to water-holding capacity and shear force. The third factor, 'colour', was associated with traits related to meat colour, whereas the fourth, referenced as 'viscera', was related to heart, liver and abdominal fat.4. The four biological factors presented moderate to high heritability (ranging from 0.35 to 0.75), which may confer genetic gains in this population.5. In conclusion, it seems possible to reduce the number of traits in the genetic evaluation of broilers using latent variables derived from factor analysis.

Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures

Combining individual p-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher's combination test. In modern large-scale data analysis, correlation and sparsity are common features and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. Our test statistic has a simple form and is defined as a weighted sum of Cauchy transformation of individual p-values. We prove a non-asymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the p-value calculation of our proposed test is not only accurate, but also as simple as the classic z-test or t-test, making our test well suited for analyzing massive data. We further show that the power of the proposed test is asymptotically optimal in a strong sparsity setting. Extensive simulations demonstrate that the proposed test has both strong power against sparse alternatives and a good accuracy with respect to p-value calculations, especially for very small p-values. The proposed test has also been applied to a genome-wide association study of Crohn's disease and compared with several existing tests.

Data for factor analysis of hydro-geochemical characteristics of groundwater resources in Iranshahr

Detection of Hydrogeological and Hydro-geochemical changes affecting the quality of aquifer water is very important. The aim of this study was to determine the factor analysis of the hydro-geochemical characteristics of Iranshahr underground water resources during the warm and cool seasons. In this study, 248 samples (two-time repetitions) of ground water resources were provided at first by cluster-random sampling method during 2017 in the villages of Iranshahr city. After transferring the samples to the laboratory, concentrations of 13 important chemical parameters in those samples were determined according to o water and wastewater standard methods. The results of this study indicated that 45.45% and 55.55% of the correlation between parameters has had a significant decrease and increase, respectively with the transition from warm seasons to cold seasons. According to the factor analysis method, three factors of land hydro-geochemical processes, supplying resources by surface water and sewage as well as human activities have been identified as influential on the chemical composition of these resources.The highest growth rate of 0.37 was observed between phosphate and nitrate ions while the lowest trend of − 0.33 was seen between fluoride ion and calcium as well as chloride ions. Also, a significant increase in the correlation between magnesium ion and nitrate ion from warm seasons to cold seasons indicates the high seasonal impact of the relation between these two parameters.

Unbiased and robust quantification of synchronization between spikes and local field potential

BACKGROUND

In neuroscience, relating the spiking activity of individual neurons to the local field potential (LFP) of neural ensembles is an increasingly useful approach for studying rhythmic neuronal synchronization. Many methods have been proposed to measure the strength of the association between spikes and rhythms in the LFP recordings, and most existing measures are dependent upon the total number of spikes.

NEW METHOD

In the present work, we introduce a robust approach for quantifying spike-LFP synchronization which performs reliably for limited samples of data. The measure is termed as spike-triggered correlation matrix synchronization (SCMS), which takes LFP segments centered on each spike as multi-channel signals and calculates the index of spike-LFP synchronization by constructing a correlation matrix.

RESULTS

The simulation based on artificial data shows that the SCMS output almost does not change with the sample size. This property is of crucial importance when making comparisons between different experimental conditions. When applied to actual neuronal data recorded from the monkey primary visual cortex, it is found that the spike-LFP synchronization strength shows orientation selectivity to drifting gratings.

COMPARISON WITH EXISTING METHODS

In comparison to another unbiased method, pairwise phase consistency (PPC), the proposed SCMS behaves better for noisy spike trains by means of numerical simulations.

CONCLUSIONS

This study demonstrates the basic idea and calculating process of the SCMS method. Considering its unbiasedness and robustness, the measure is of great advantage to characterize the synchronization between spike trains and rhythms present in LFP.

Hypothesis testing for differentially correlated features

In a multivariate setting, we consider the task of identifying features whose correlations with the other features differ across conditions. Such correlation shifts may occur independently of mean shifts, or differences in the means of the individual features across conditions. Previous approaches for detecting correlation shifts consider features simultaneously, by computing a correlation-based test statistic for each feature. However, since correlations involve two features, such approaches do not lend themselves to identifying which feature is the culprit. In this article, we instead consider a serial testing approach, by comparing columns of the sample correlation matrix across two conditions, and removing one feature at a time. Our method provides a novel perspective and favorable empirical results compared with competing approaches.

Ecotoxicological risk assessment of trace metals in humid subtropical soil

In this work, several physicochemical properties of sub-tropical soil (up to 20 cm depth) like water holding capacity, organic carbon content, cation exchange capacity, texture, pH, and electrical conductivity were determined along with the trace metals, Co, Cr, Cu, Mn, Ni, Pb and Zn, in order to evaluate inter-relations among the trace metals and the soil properties. The contribution of the trace metals to ecotoxicological risk was assessed using various tools. Cr, Cu, Mn and Zn contents were found to be lower than the world average, but Co, Ni, and Pb had higher contents. The trace metal concentrations were utilized to obtain the pollution index and the potential ecotoxicological aspects. The trace metals were shown to have come from similar origin and their retention in the soil was contributed by properties like organic carbon, cation exchange capacity, clay content and water holding capacity of the soil. The pollution index showed that the trace metals had the sequence of Pb (considerably polluted) > Co, Ni (moderately polluted) > Cr, Cu, Mn and Zn (unpolluted). The composite ecological risk index was the highest in agricultural land with irrigation and fertilizer use, and was the lowest in the forest land.

Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

For longitudinal data, the modeling of a correlation matrix R can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocorrelations (PACs), each of which vary independently over [-1,1]. The first prior shrinks each of the PACs toward zero with increasingly aggressive shrinkage in lag. The second prior (a selection prior) is a mixture of a zero point mass and a continuous component for each PAC, allowing for a sparse representation. The structure implied under our priors is readily interpretable for time-ordered responses because each zero PAC implies a conditional independence relationship in the distribution of the data. Selection priors on the PACs provide a computationally attractive alternative to selection on the elements of R or R^-1 for ordered data. These priors allow for data-dependent shrinkage/selection under an intuitive parameterization in an unconstrained setting. The proposed priors are compared to standard methods through a simulation study and a multivariate probit data example. Supplemental materials for this article (appendix, data, and R code) are available online.