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AbstractPurposeMaterials and methodsResultsConclusionKernel texture plays a principal role in determining technological flour properties and end-use quality of wheat products. Hence, a multi-year mutation induction programme was conducted to isolate advanced wheat mutant lines with agro-morphologically superior performance, higher disease resistance and harder grain texture.Radiation mutagenesis was employed in soft textured wheat variety HPW 89 using gamma rays dose of 250, 300 and 350 Gy (Co60: BARC, Mumbai) and evaluated across M1–5 generations. Promising superior mutants selected were evaluated during M4 and M5 generation for induced variability and trait association for agro-morphological and quality traits. The screened mutants were also determined for induced changes at genetic level using gene specific markers for puroindoline genes.A total of 293 agro-morphologically superior mutants isolated showed significant genetic variation in the M4 generation. Single kernel characterization system categorized 267 mutants (8.79–50.06) with higher grain hardness than the HPW 89 variety (7.39). Among these, 108 mutants were selected for agro-morphological and molecular characterization. Significant variations were found in these mutants in either pina and pinb or both puroindoline genes. Clustering among these mutants led to the formation of five clusters and a total of eleven mutants were found with better set of agro-morphological, disease resistance and quality traits.These mutants can serve as important genetic resource for developing harder texture bread wheat varieties in the future grain quality improvement programmes. These mutants will also bridge the need of bakers and millers’ requirement of varieties with specific texture and quality. [ABSTRACT FROM AUTHOR]

• Results indicated that the F 2 plants had wide variations in most characteristics. • Multivariate methods successfully determined selection criteria. • The decision tree successfully predicted the PN and SW range for superior genotypes. • Selecting PN 87 – 119 per plant and SW ≥ 14.55 g can obtain superior genotypes. The existence of sufficient genetic variability and knowing the suitable selection criterion ranges are indispensable requirements of breeding studies. This study aimed to explore genetic diversity in ten F 2 populations (heterozygous–heterogeneous), five parents, and three commercial check cultivars by multivariate methods and to predict useful selection ranges using a decision tree model for further breeding studies. Hybridizations were made in 2019, F 1 populations were grown, and F 2 seeds were obtained in 2020. Genotypes were grown according to an Augmented Randomized Complete-Block Design in 2021 at Aydın Adnan Menderes University Faculty of Agriculture. Results revealed considerable genetic diversity and transgressive segregation in key characteristics across the F 2 populations. Seed yields of F 2 plants are higher than those of the parents. The seed protein and oil content of the F 2 plants were not different from the parents and commercial checks. Higher phenotypic than genotypic variance indicated a high influence of environment on the inheritance of all studied characteristics. The highest broad heritability was observed in the number of shattered pods per plant (94.81 %) and the lowest in infertile pods per plant (2.16 %). Seed yield showed a significant and positive genotypic correlation with the number of fertile pods per plant (0.61**) and a significant negative association with the number of fertile branches per plant (−0.53**). Principal component analysis (PCA) of 14 quantitative characteristics showed that soybean genotypes separated by the first four components contributed 71.12 % of total genetic variation, with important yield-attributing characteristics present in PC1 and PC3, while important seed quality characteristics were noted in PC2. The decision tree model indicated that genotypes with high yield and optimum quality could be obtained by selecting the number of fertile pods 87–119 per plant and a hundred-seed weight greater than 14.55 g. [ABSTRACT FROM AUTHOR]

Canada‐wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter‐comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio‐economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections. [ABSTRACT FROM AUTHOR]

This study presents a case of processing X‐ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi‐collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X‐ray CT data and robust PLS regression. This method is applicable to X‐ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data. [ABSTRACT FROM AUTHOR]

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions. Variability in population sizes further complicates risk estimation from the counts. This study introduces multivariate Poisson cokriging for predicting and filtering out disease risk. Pairwise correlations between the target variable and multiple ancillary variables are included. By means of a simulation experiment and an application to human immunodeficiency virus incidence and sexually transmitted diseases data in Pennsylvania, we demonstrate accurate disease risk estimation that captures fine-scale variation. This method is compared with ordinary Poisson kriging in prediction and smoothing. Results of the simulation study show a reduction in the mean square prediction error when utilizing auxiliary correlated variables, with mean square prediction error values decreasing by up to 50%. This gain is further evident in the real data analysis, where Poisson cokriging yields a 74% drop in mean square prediction error relative to Poisson kriging, underscoring the value of incorporating secondary information. The findings of this work stress on the potential of Poisson cokriging in disease mapping and surveillance, offering richer risk predictions, better representation of spatial interdependencies, and identification of high-risk and low-risk areas. [ABSTRACT FROM AUTHOR]

Background: Chickpea being a winter season crop, often experiences high temperature during its reproductive phase resulting in yield losses due to the direct effect of it, on different physiological processes. Therefore, identification and development of heat stress tolerant genotypes is an important aspect in chickpea breeding especially in view of the changing climate scenario. Methods: An experiment was conducted during Rabi season 2017-18 at pulses research field, CCS HAU, Hisar, Haryana comprising 60 genotypes in randomized block design with three replications under two environmental conditions namely, normal and late sown to find out heat stress tolerant genotypes. A total of eleven morpho-physiological yield attributes noted down under both normal and late sown conditions and various multivariate statistical methods were used to find out tolerant chickpea genotypes. Result: The present study has led the understanding of many inter-related traits involved in the genetic variation of chickpea seed yield under normal as well as late sown conditions. This would certainly provide guidelines for selection of parents as well as effective selection of promising chickpea genotypes and also have paramount importance in formulating plant model for selection of segregating generations in chickpea breeding programmes for development of high yielding varieties. [ABSTRACT FROM AUTHOR]

Following recent developments of dimension reduction algorithms for a multivariate time series, we propose in this work the adaptation of sliced inverse mean difference algorithm, an algorithm which was previously proposed in a standard multiple regression setting, to develop an algorithm appropriate to perform dimension reduction for a multivariate time series. The resulting algorithm called time series sliced inverse mean difference (TSIMD) is shown to be able to identify important directions and important lags using less significant pairs than previously proposed algorithms for dimension reduction in multivariate time series. We demonstrate the competitive performance of our algorithms through a number of experiments. [ABSTRACT FROM AUTHOR]

Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain—global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data. Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population. • Small sample sizes have led to divergent findings of functional connectivity differences in borderline personality disorder. • We evaluated reliability and generalizability of functional connectivity differences with whole brain classification at various scales with nested cross-validation in the largest sample to date. • Moderate and generalizable classification accuracies were achieved despite heterogeneity of the patient population, but classification accuracy varied with random splits of training and test data. • Midline and posterior temporal regions were most consistently associated with clinical differences. • Careful and up-to-date clinical evaluation was critical to detecting consistent connectivity differences. [ABSTRACT FROM AUTHOR]

This research proposes a novel arbitrage approach in multivariate pair trading, termed the Optimal Trading Technique (OTT). We present a method for selectively forming a "bucket" of fiat currencies anchored to cryptocurrency for monitoring and exploiting trading opportunities simultaneously. To address quantitative conflicts from multiple trading signals, a novel bi-objective convex optimization formulation is designed to balance investor preferences between profitability and risk tolerance. We understand that cryptocurrencies carry significant financial risks. Therefore this process includes tunable parameters such as volatility penalties and action thresholds. In experiments conducted in the cryptocurrency market from 2020 to 2022, which encompassed a vigorous bull run followed by a bear run, the OTT achieved an annualized profit of 15.49%. Additionally, supplementary experiments detailed in the appendix extend the applicability of OTT to other major cryptocurrencies in the post-COVID period, validating the model's robustness and effectiveness in various market conditions. The arbitrage operation offers a new perspective on trading, without requiring external shorting or holding the intermediate during the arbitrage period. As a note of caution, this study acknowledges the high-risk nature of cryptocurrency investments, which can be subject to significant volatility and potential loss. [ABSTRACT FROM AUTHOR]

Hydrochemical studies, involving groundwater analysis, multivariate statistics, and geospatial element distributions, were carried out to understand the factors influencing groundwater geochemistry and potential pollution in an arid area (Eastern Desert, Egypt). A total of 104 groundwater samples were analyzed for twenty-three physical and chemical variables. Spatial mapping was conducted to depict variations in the concentration of several variables. A spatial pattern revealed an increasing water table depth from south to north, correlating with a rise in groundwater salinity. The correlation coefficients for various variables were found to align with aquifer mineral saturation indices. Hydrochemical facies identified three main zones with six facies in the first sub-area, five in the second, and the third sub-areas. Specific processes, including evaporite/carbonate rock dissolution and ion exchange, dominate. The results support meteoric groundwater recharge in the first sub-area, influenced by the Nile water and rainfall. The second and third sub-areas indicated groundwater mixing from diverse sources, emphasizing evaporite/carbonate rock dissolution and ion exchange. Negative saturation indices for anhydrite, halite, and gypsum suggested under-saturation conditions, while dolomite and calcite indicated upper saturation, implying secondary mineral precipitation. Factor analysis demonstrated that six interrelated factors elucidate the chemical attributes of the groundwater. These factors arise from interactions between rock and water, blending of waters from diverse sources, and, to some extent, anthropogenic influences. Utilizing cluster analysis, notable and distinct groundwater zones were identified, where the original groundwater was uniquely influenced by mixing processes. Therefore, integration of hydrochemical and geospatial statistical analysis methods can be employed to enhance water resource management on a regional scale and in areas with similar conditions. [ABSTRACT FROM AUTHOR]

Abstract Canada‐wide, statistically downscaled simulations of global climate models from the Sixth Coupled Model Inter‐comparison Project (CMIP6) have been made available for 26 models using a new multivariate approach and an improved observational target dataset. These new downscaled scenarios comprise daily simulations of precipitation, maximum temperature, and minimum temperature at 1/12° resolution across Canada. Simulations from each of the 26 downscaled global climate models span a historical period (1950–2014), and three future Shared Socio‐economic Pathways (SSPs) representing low (SSP1 2.6), moderate (SSP2 4.5) and high (SSP5 8.5) future emissions from 2015 to 2100. Results from an evaluation of the multivariate downscaling method over Canada yield improved performance in replicating multivariate and compound climate indices compared to previously used univariate downscaling methods. This Multivariate Canadian Downscaled Climate Scenarios for CMIP6 (CanDCS‐M6) dataset is intended to facilitate climate impacts assessments, hydrologic modelling, and analysis tools for presenting climate projections.

An appraisal of the snail (Mollusca: Gastropoda) assemblage of the Sundarbans mangrove region, West Bengal, India, was made through consistent monitoring of three selected sites. In a span of 3 years, 18 species of snails under 11 families, and 9 orders were collected in 304 samples with the Shanon-Weiner diversity index (H') ranging between 0.8445 and 1.6909. In terms of numerical abundance, Stenothyra deltae (34.07 ± 5.71SE) and Cerithidia cingulata (26.01 ± 1.33SE) were dominant, while Nassarius stolatus (0.01 ± 0.01 SE) qualified as a rare species in the three sites. Significant differences (p < 0.05) in abundance in the sites were observed for Cerithidia cingulata, C. alata, Gangetica miliacea, and Stenothyra deltae. A canonical correspondence analysis reflected that the abundance of G. miliacea is shaped by the conductivity, C. cingulata is linked with temperature and the phosphate concentration is a better predictor of the abundance of S. deltae. The pattern of the diversity of the snails may probably reflect the variations in the environmental quality including the food resources. Thus, the snail assemblage pattern may be used as a basis for the biological monitoring of the Sundarbans mangroves and other similar sites. [ABSTRACT FROM AUTHOR]

This study demonstrates a new approach to process hyperspectral images where both the contextual spatial information as well as the spectral information are used to predict sample properties. The deep contextual spatial information is extracted using the deep feature extraction from pretrained resnet‐18 deep learning architecture, while the spectral information was readily available as the average pixel values. To fuse the information in a complementary way, a multiblock modeling approach called sequential orthogonalized partial least squares was used. The sequential model guarantees that the information learned is complementary from spatial and spectral domains. The potential of the approach is demonstrated to predict several physical and chemical properties in pork bellies. The fusion of spatial and spectral information allowed better prediction of physical properties of pork samples. The spatial feature alone was able to predict both the chemical and physical properties. Deep features can be extracted for any type of images and can be fused with spectral data to enhance data modeling. [ABSTRACT FROM AUTHOR]

Some of the collected data by wireless sensor networks (WSNs) often digress from the normal pattern of the set and are called the 'outliers' or 'anomalies'. These outlier data are usually derived from an error that can be due to the constraints of sensor nodes such as problems in the processing unit, power supply, and component failure or an event occurring at the development site of sensor nodes, including the earthquake or flood, affecting the quality of the collected data and their reliability for decision making. In this work, a method is proposed for detecting outlier data in networks based on the hidden Markov model and the copula theory. Copula was used because of its ability to deal with multivariate sets and its unlimitedness in such a way that it required no assumption about data distribution, and Markov has been applied due to its good performance in forecasting. The results of evaluations on the actual data from the Intel laboratory represent the efficiency of the proposed method. Bases on the performance evaluations of the proposed method, in comparison with a relatively new work leading to good results. [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Chaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data. Through successful prediction studies conducted on the generated chaotic time series, forecasts can be made about events displaying unpredictable behavior in nature, which have not yet been modeled. This enables preparation for both favorable and unfavorable situations that may arise. In this study, chaotic time series were generated using Lorenz, Chen, and Rikitake multivariate chaotic systems. To enhance prediction accuracy on the generated data, GRU, LSTM and RNN models were trained with different hyperparameters. Subsequently, comprehensive test studies were conducted to evaluate their performance. Predictions were calculated using evaluation metrics, including MSE, RMSE, MAE, MAPE, and R2. In the experimental study, each chaotic system was trained with different hyperparameter combinations on six network models. The experimental results indicate that the utilized models exhibited greater success in predicting chaotic time series compared to some other models in the literature. [ABSTRACT FROM AUTHOR]

Water property parameters were analyzed over 9 months across six stations within the Estero Salado. The parameters under evaluation included nitrite (NO2−), nitrate (NO3−), phosphate (PO43−), ammonium (NH4+), temperature, pH, biochemical oxygen demand (BOD), conductivity, salinity, color, turbidity, suspended solids, hardness, and alkalinity. Additionally, the TRIX index (which measures chlorophyll, oxygen saturation, nitrogen, and phosphorus) was considered. The multivariate technique employed was partial triadic analysis (PTA), a specialized variant developed from STATIS, enabling the examination of the common structure's stability across months and the positioning of stations and variables within a compromise space. This analysis elucidated a variability of 69% and 96%, respectively. Stations could be characterized based on their associations with specific variables, while the analysis also facilitated the identification of months impacting the common structure of pollution indicators. [ABSTRACT FROM AUTHOR]

The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations are governed by conjunctive neural representations of the features. But, support for this view comes from paradigms arbitrarily combining task features and affordances, thus requiring representations in working memory. Therefore, the present study used electroencephalography and a well-rehearsed task with features that afford minimal working memory representations to investigate the temporal evolution of feature representations and their potential integration in the brain. Female and male human participants grasped objects or touched them with a knuckle. Objects had different shapes and were made of heavy or light materials with shape and weight being relevant for grasping, not for “knuckling.” Using multivariate analysis showed that representations of object shape were similar for grasping and knuckling. However, only for grasping did early shape representations reactivate at later phases of grasp planning, suggesting that sensorimotor control signals feed back to the early visual cortex. Grasp-specific representations of material/weight only arose during grasp execution after object contact during the load phase. A trend for integrated representations of shape and material also became graspspecific but only briefly during the movement onset. These results suggest that the brain generates action-specific representations of relevant features as required for the different subcomponents of its action computations. Our results argue against the view that goaldirected actions inevitably join all features of a task into a sustained and unified neural representation. [ABSTRACT FROM AUTHOR]

【Objective】The use of artificial inoculation to ferment cigar tobacco leaves is an inevitable trend in the industrial production of cigar tobacco leaves, and the neutral aroma components are the important quality components of cigar tobacco leaves. The study aims to investigate the effect of Bacillus safensis GL2 on the neutral aroma components of cigar tobacco leaves.【Method】GL2 was screened from Cuban cigar Cohiba Robert and used to ferment cigar leaves. The neutral aroma components in tobacco leaves were determined by distillation extraction-gas chromatography-mass spectrometry, and multivariate statistical methods were used for analysis and statistics.【Result】The total aroma and the contents of most neutral aroma components in cigar tobacco leaves were significantly increased after GL2 fermentation (P<0.05), and the increased aroma substances mainly included olefins, ketones and alcohols. A total of 13 differentially neutral aroma components were selected by VIP>1 and P<0.05, and 9 of them were significantly up-regulated and positively correlated with tobacco leaf aroma. There were 7 species with significant positive correlation (P<0.05), which were the key differential aroma components of 2,6,6-trimethyl-2- cyclohexene-1,4-dione, 2,6,6-trimethyl-1,4-cyclohexanedione, geraniyl acetone, acanthyl acetone, neophydiene, solanone and phytoalcohol. In addition, 4 different aroma components were significantly decreased and negatively correlated with tobacco leaf aroma, among which 2,4-di-tert-butyl-6-nitrophenol was significantly negatively correlated with cigar tobacco aroma (P<0.05). 2,6,6-trimethyl-2-cyclohexene-1,4-dione, geranyl acetone, solanone and phytol contributed significantly to the aroma of fermented cigar tobacco leaves, resulting in a stronger overall dry and floral fragrances. As a result, they enriched the aroma and improved the sensory quality of cigar tobacco leaves.【Conclusion】Inoculation of B. safensis during cigar tobacco fermentation is beneficial for improving the quality of cigar tobacco leaves, which provide theoretical support for improving the fermentation quality of cigar tobacco leaves with biotechnology. [ABSTRACT FROM AUTHOR]

Great Lakes coastal wetlands are under considerable stress from numerous anthropogenic threats, including the introduction of invasive species. Common Carp, an invasive species in North America, has been well documented as an influential wetland stressor. This study documents the impact of Common Carp reintroduction on a restored wetland fish community and abiotic and vegetation variables. Oshawa Second Marsh was restored using an exclusion berm with a fish grate and manual water-level drawdown to re-establish vegetation. Five years into restoration monitoring, Common Carp regained access to the wetland after the fish grate was vandalized. Fish community health was monitored over time using multimetric and multivariate approaches based on abiotic and vegetation variables. Improvements in fish community health were observed during restoration monitoring, but after Common Carp reintroduction, fish community health decreased and the community homogenized. Seven of the ten abiotic and vegetation variables monitored changed significantly after Common Carp reintroduction. This study highlights the impact that Common Carp has on the functional integrity of coastal wetlands and the significance of its management for restoration. [ABSTRACT FROM AUTHOR]

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described. [ABSTRACT FROM AUTHOR]

In many situations of daily life, when we start statistical analysis, we encounter data that we suffer from inaccuracy in its measurement, either due to observation or measurement errors or the lack of appropriate conditions for collecting this data, such as failure times of machines, equipment, electrical devices, medical, engineering and economic data. In many medical studies, the researcher may have a strong conviction that the regression function used to describe the relationship between two variables has a certain form or formula that can be characterized by certain ordering restrictions such as monotonicity; monotonic regression has important applications in statistics, operations research, and signal processing, and these applications are often characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not increase rapidly with increasing n. It is a nonparametric regression technique that preserves order. In this thesis, the proposed fuzzy autoregressive monotonic method, fuzzy autoregressive monotonic least squares (FILSR), fuzzy autoregressive M estimator (FIM), and fuzzy autoregressive maximum likelihood estimator (FIML) were proposed. The best method for estimating the autoregressive function is the fuzzy autoregressive maximum likelihood method with a mean square error of (0.51455). [ABSTRACT FROM AUTHOR]

Sexual dimorphism, the phenotypic differences between males and females within a species, is widespread in the animal kingdom. This study investigates the extent of sexual dimorphism and species divergence in external morphology in two closely related freshwater fish species, Devario malabaricus and D. micronema, in Sri Lanka. Devario malabaricus, a habitat generalist, inhabits a wide range of aquatic environments, while D. micronema, a habitat specialist, resides in shaded rainforest streams. The study reveals size differences between the two species: D. malabaricus is consistently larger than D. micronema. However, there are no size differences between the sexes within each species. Several additional morphological traits, such as head length, interorbital width, predorsal length and postdorsal length display divergence between the species as well as between the sexes within each species; these patterns are also consistent across populations. The sexually dimorphic traits are similar between the two species despite their distinct ecological habitats. However, Devario malabaricus exhibit stronger sexual dimorphism compared to D. micronema, supporting the prediction of a positive relationship between the extent of sexual dimorphism and ecological generalism. The study highlights the importance of considering sexual dimorphism in morphometric comparisons in taxonomic studies of Devario and suggests that divergent selection between the sexes contributes to morphological variation in this group. [ABSTRACT FROM AUTHOR]

Improvement in genetic gains of crops could be achieved by phenomics' characterization of agronomic, physiological and stress-related traits. Molecular and strategic breeding programmes require broad range of foreground and background phenotypic information for crop improvement. The current experiment was performed on 123 advanced soybean (Glycine max L.) genotypes including seven local lines belongs to four different maturity groups (000-lV) to estimate the endogenous potential of various yield-related traits. The experimental trial was repeated for two cropping seasons. Four traits out of six, yield per plant (YPP), number of seeds per plant, number of pods per plant and plant height (PH), showed maximum variation (CV%) that directly correlate with variability in the subjected population. PH, number of pods, 100-seed weight and YPP showed strong positive correlation in both years. Among the principal components, factors 1 and 2 showed maximum contribution in phenotypic variability ranges from 19 to 48.5% and 26 to 47.7% in the first and second years, respectively. Number of pods showed significant positive correlation with genotypes in both years. Dendrogram showed two distinct groups of soybean genotypes. Genetic variation and association among the accessions is indispensable for effective conservation and utilization of germplasm. Principal component analysis helps to identify the diverse genotypes that will be used as a parent for various breeding programmes. These phenotypic data will be used for detection of heat stress-related quantitative trait loci with genotypic data in genome-wide association studies experiments. [ABSTRACT FROM AUTHOR]

In this study, Enzyme-Induced Carbonate Precipitation (EICP) combined with polypropylene fiber technology was used to solidify desert sand, the orthogonal theory was used to design the different levels of the five test variables of cementation solution concentration, urease concentration, enzyme gel ratio, fiber length and fiber content, and the curing effect of EICP combined with polypropylene fiber was studied by macroscopic test of unconfined compressive strength and calcium carbonate content and microscopic test of scanning electron microscope (SEM) and nuclear magnetic resonance (NMR). The results show that the primary and secondary order of the influence of the five test variables on the unconfined compressive strength and calcium carbonate content is: fiber content >fiber length >urease concentration >enzyme gel ratio >cementation solution concentration, in which the fiber length and fiber content had a significant effect on it, and the urease concentration had a little significant effect on it. The incorporation of polypropylene fiber promotes the deposition of calcium carbonate, restricts the displacement and deformation of sand particles, effectively fills the pores between particles, significantly reduces the pore size, and improves the pore structure, which provides a reference for subsequent research and practical geotechnical applications. [ABSTRACT FROM AUTHOR]

Real-life time series datasets exhibit complications that hinder the study of time series forecasting (TSF). These datasets inherently exhibit non-stationarity as their distributions vary over time. Furthermore, the intricate inter- and intra-series relationships among data points pose challenges for modeling. Many existing TSF models overlook one or both of these issues, resulting in inaccurate forecasts. This study proposes a novel TSF model designed to address the challenges posed by real-life data, delivering accurate forecasts in both multivariate and univariate settings. First, we propose methods termed "weak-stationarizing" and "non-stationarity restoring" to mitigate distributional shift. These methods enable the removal and restoration of non-stationary components from individual data points as needed. Second, we utilize the spectral decomposition of weak-stationary time series to extract informative features for forecasting. To learn features from the spectral decomposition of weak-stationary time series, we exploit a mixer architecture to find inter- and intra-series dependencies from the unraveled representation of the overall time series. To ensure the efficacy of our model, we conduct comparative evaluations against state-of-the-art models using six real-world datasets spanning diverse fields. Across each dataset, our model consistently outperforms or yields comparable results to existing models. [ABSTRACT FROM AUTHOR]

Background: The leaf area index (LAI) quantifies the total one-sided green leaf area per unit of soil area, making it a crucial parameter in models that simulate carbon, nutrient, water, and energy fluxes within forest ecosystems. This study enhances LAI estimation techniques by employing a multivariate linear regression (MVLR) approach specifically tailored to tropical vegetation. We integrated field-collected LAI data with spectral indices and multispectral bands to develop a robust predictive empirical model. The LAI estimates derived from the MVLR approach are rigorously compared with those obtained from the Sentinel Application Platform (SNAP), a widely utilized tool for remote sensing analysis. Results: In developing the MVLR model, nine multispectral bands, seven vegetation indices (VIs), and two biophysical variables derived from Sentinel-2 multispectral image were tested to identify efficient predictors for LAI estimation. To determine significant multispectral bands and VIs (ensuring no multicollinearity, high coefficient of determination (R2), low root mean square error (RMSE), and a p-value < 0.05) for the best representative model, stepwise multiple linear regression (SMLR) was employed. Multispectral bands 7 and 8, along with the VIs soil adjusted vegetation index and normalized difference vegetation index, and the fraction of vegetation cover biophysical variable, produced superior outcomes and serve as strong predictor variables for LAI. The accuracy of the MVLR model was validated using 17 directly measured LAI sample plots with the leave-one-out cross-validation method. The estimated LAI using the MVLR model achieved higher accuracy, with an R2 of 0.94, compared to the SNAP toolbox (R2 = 0.71). The RMSE and bias of the MVLR model were 0.18 and 0.006, respectively, while for SNAP-derived LAI, the RMSE and bias were 0.53 and 0.31, respectively. Conclusions: The improved accuracy and reduced error of the MVLR model are attributed to its adjustment for tropical vegetation types. Future research should focus on comparing the MVLR model with other global LAI products to further validate and enhance its applicability.

The simultaneous infection of organisms with two or more co-occurring pathogens, otherwise known as co-infections, concomitant infections or multiple infections, plays a significant role in the dynamics and consequences of infectious diseases in both humans and animals. To understand co-infections, ecologists and epidemiologists rely on models capable of accommodating multiple response variables. However, given the diversity of available approaches, choosing a model that is suitable for drawing meaningful conclusions from observational data is not a straightforward task. To provide clearer guidance for statistical model use in co-infection research, we conducted a systematic review to (i) understand the breadth of study goals and host–pathogen systems being pursued with multi-response models and (ii) determine the degree of crossover of knowledge among disciplines. In total, we identified 69 peer-reviewed primary studies that jointly measured infection patterns with two or more pathogens of humans or animals in natural environments. We found stark divisions in research objectives and methods among different disciplines, suggesting that cross-disciplinary insights into co-infection patterns and processes for different human and animal contexts are currently limited. Citation network analysis also revealed limited knowledge exchange between ecology and epidemiology. These findings collectively highlight the need for greater interdisciplinary collaboration for improving disease management.

ObjectivePrevious research has established a connection between Type 2 Diabetes Mellitus (T2DM), glycemic traits, dietary habits, and the risk of Pressure Ulcers (PUs). The aim of our study is to disentangle any potential causal relationship between T2DM, glycemic traits, and dietary factors, and the risk of PUs.MethodsThe exposure and outcome datasets were sourced from the IEU Open GWAS project, the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), and the FinnGen biobank, respectively. The primary MR analysis method employed was the inverse variance-weighted method. Furthermore, we employed multivariable MR (MVMR) adjusting for BMI. Then, we investigated the possibility of a reverse association between glycemic traits and PUs through bidirectional MR. Finally, Heterogeneity and pleiotropic analysis were conducted to ensure the accuracy and robustness of the results.ResultsThe findings revealed that T2DM (OR = 1.282, 95% CI: 1.138–1.445, p

Vitamin D3(VD3), also known as cholecalciferol plays a crucial role in maintaining calcium and phosphate homeostasis and has the potential to suppress bone malformations in fish larvae. However, external factors during growth also significantly influence the development of fish larval bones. Understanding the complex interaction between VD3 treatment and these external factors is crucial for optimizing therapeutic potential and effectively treating bone anomalies. SEM (Structural Equation Modeling) offers a comprehensive framework to explore these interactions by considering multiple variables simultaneously and examining their interrelationships. This study aimed to determine the impact of VD3 and its relationship with other external factors on the incidence of bone malformations in hybrid grouper larvae (Epinephelus fuscoguttatus X Epinephelus polyphekadion, EFEP). The treatments included a dose of 1.9 IU/g of VD3 in the feed and control group without VD3. All treatments received standard enrichment ingredients including vitamin C, taurine, and commercial enrichment (*SELCO). The enrichment was applied to rotifers (3 – 25 days after hatching, DAH) and artificial feed (25 – 35 DAH). The treatment was conducted in two batches, during the dry season (batch 1) and the rainy season (batch 2), to explore the seasonal impact on bone malformations. Results indicated that VD3 reduced the incidence of bone malformations in EFEP hybrid grouper fry by 1.14–1.65 times. SEM analysis revealed that spawning timing, egg quality, and water quality significantly impacted the occurrence of bone deformities, influencing malformations directly and indirectly through bone mineral content. This study demonstrated that bone malformations can be result from deficiencies in calcium, excess potassium and sulfur, and the presence of bromine in bone.

Abstract Background The analysis of dental caries has been a major focus of recent work on modeling dental defect data. While a dental caries focus is of major importance in dental research, the examination of developmental defects which could also contribute at an early stage of dental caries formation, is also of potential interest. This paper proposes a set of methods which address the appearance of different combinations of defects across different tooth regions. In our modeling we assess the linkages between tooth region development and both the type of defect and associations with etiological predictors of the defects which could be influential at different times during the tooth crown development. Methods We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application. Results The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results. Conclusion We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model.

The aim of the study was to evaluate physiological variables invasively and non-invasively and heat exchange in Nordestina breed horses, and to determine which variables have the greatest discriminating power in terms of adaptive profile, using canonical and stepwise discriminant analysis. Seven adult horses were used, 3 females and 4 stallion males, which were housed from birth in the property's paddocks with ad libitum access to native pasture, concentrate in the trough and water, remaining in these conditions during the experimental period. The thermal comfort index (TCI) values ​​above 65 are considered severe heat stress for horses. The rectal temperature, respiratory rate and heart rate of the females were similar (P > 0.05) between the periods and these variables remained within the limit for the equine species, even with the rise in air temperature in the afternoon and the high TCI, demonstrating the animals' adaptive capacity. The Nordestina breed showed physiological values within the standard for the species, even with the ambient temperature above the thermal comfort zone in both periods (30.09 and 35.05 °C), being efficient at dissipating heat through the skin and respiratory system, keeping the rectal temperature within the standard for the species

Monte Carlo simulations and theoretical analyses have repeatedly demonstrated the impact of outliers on statistical analysis. Most simulation studies generate outliers using one of two general approaches: by multiplying an arbitrary point by a constant or through a finite mixture. The latter can be extended to multivariate settings by defining the Mahalanobis distance between the centroids of two clusters of points. Nevertheless, when researchers aim to simulate individual data points with population-level Mahalanobis distances, the number of available procedures is very limited. This article generalizes one of the few existing methods to simulate an arbitrary number of outliers in an arbitrary number of dimensions, for both multivariate normal and non-normal data. A small simulation demonstration showcases how this methodology enables new simulation designs that were either unpopular or not possible due to the lack of a data-generating algorithm. A discussion of potential implications highlights the importance of considering multivariate outliers in simulation settings.

The presence of Rare Earth Elements (REEs) is often correlated with tin (Sn), particularly at placer deposits in the Bangka Belitung Islands. However, there is limited information on the correlation between Sn and REEs. Therefore, this study aimed to investigate the correlation between REEs and Sn using data analysis of Sn-W, REEs, Y, Th, and U in granite, quartz veins, and weathered granite samples from South Bangka. The statistical analysis carried out using the Pearson correlation coefficient and multivariate processing showed that Sn-REEs have a weakly negative correlation. The examination of genetic aspects through chemical analysis, mineralogy, and Scanning Electron Microscope (SEM) data, showed that Sn and REE originated from the same magmatic fluids but different depositional phases due to magmatichydrothermal processes. The results show that REEs were formed during the early magmatic crystallization as mineral and stannite (Cu-Sn) in granite, while Sn was enriched as cassiterite in the deposition of late-stage hydrothermal fluid in parental granite. Moreover, the variation in deposition stage and temperature during the formation process is depicted in statistically uncorrelated values for Sn and REEs. [ABSTRACT FROM AUTHOR]

Buffalo bull semen traits are economically important traits that influence farm fertility and profitability. Genetic improvement of semen characteristics is an important detail of the genetic improvement. This study was conducted to assess the relationship between the breeding values as well as the phenotypic values for semen traits (VOL, MM, LS, AS and CONC) of the Egyptian buffalo bulls. A total of 7761 normal semen ejaculates were collected and characterized at ILMTC laboratory from 26 bulls from 2009 to 2019. For VOL, MM, LS, AS, and CONC, the actual means were 3.89 mL, 62.37%, 60.64%, 3.94%, and 0.67 × 109 sperm/mL, respectively. The prediction of breeding values for semen traits was estimated using a Bayesian procedure. The estimated standardized EBVs and phenotypic values were used in the principal component analysis (PCA). Of five PCs, one PC (PC1) had > 1 eigenvalues that was responsible for 87.19% of the total variation of SEBV, and two PCs had > 1 eigenvalues that were responsible for 59.61% and 21.35% of the total variation of the phenotypic values. Together, PC1 and PC2 accounted for 97.97% of the total variance of SEBV and 80.96% of the total variance of phenotypic values. A graphs of the first two components showed the traits separated into two different directions by group. This indicates each group was under similar genetic influence. Therefore, selection can be done separately for each group without influencing the other. Principal component analysis reduced variables to describe the key information in buffalo semen data. [ABSTRACT FROM AUTHOR]

The goal of lipidomic studies is to provide a broad characterization of cellular lipids present and changing in a sample of interest. Recent lipidomic research has significantly contributed to revealing the multifaceted roles that lipids play in fundamental cellular processes, including signaling, energy storage, and structural support. Furthermore, these findings have shed light on how lipids dynamically respond to various perturbations. Continued advancement in analytical techniques has also led to improved abilities to detect and identify novel lipid species, resulting in increasingly large datasets. Statistical analysis of these datasets can be challenging not only because of their vast size, but also because of the highly correlated data structure that exists due to many lipids belonging to the same metabolic or regulatory pathways. Interpretation of these lipidomic datasets is also hindered by a lack of current biological knowledge for the individual lipids. These limitations can therefore make lipidomic data analysis a daunting task. To address these difficulties and shed light on opportunities and also weaknesses in current tools, we have assembled this review. Here, we illustrate common statistical approaches for finding patterns in lipidomic datasets, including univariate hypothesis testing, unsupervised clustering, supervised classification modeling, and deep learning approaches. We then describe various bioinformatic tools often used to biologically contextualize results of interest. Overall, this review provides a framework for guiding lipidomic data analysis to promote a greater assessment of lipidomic results, while understanding potential advantages and weaknesses along the way. [ABSTRACT FROM AUTHOR]

Background: The analysis of dental caries has been a major focus of recent work on modeling dental defect data. While a dental caries focus is of major importance in dental research, the examination of developmental defects which could also contribute at an early stage of dental caries formation, is also of potential interest. This paper proposes a set of methods which address the appearance of different combinations of defects across different tooth regions. In our modeling we assess the linkages between tooth region development and both the type of defect and associations with etiological predictors of the defects which could be influential at different times during the tooth crown development. Methods: We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application. Results: The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results. Conclusion: We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model. [ABSTRACT FROM AUTHOR]

This study investigated multivariate relations between (a) lifetime experiences of adversity and (b) past year substance use involvement among cisgender sexual minority emerging adult men, ages 18–29 years. Ethnically and racially diverse samples of gay (N = 205, ageM = 24.33 years) and bisexual men (N = 201, ageM = 23.31 years) were recruited from the United States using the internet-based CloudResearch platform to complete 20-minute online surveys. Canonical correlation analyses (CCAs) documented a higher canonical correlation between canonical variates among gay (.471) compared to bisexual (.346) men, as well as differing amounts of shared variance (22.2% vs. 12.0%). Examination of the standardized canonical coefficients and structure coefficients highlighted differences between gay and bisexual men in variables contributing most to the synthetic predictor and criterion variables. Information about these group differences may be used to tailor selected prevention initiatives for substance use problems among emerging adult sexual minority men. [ABSTRACT FROM AUTHOR]

Sustainable wheat production and higher genetic gains can be realized by broadening the genetic base and improving the well adapted varieties. In the present study, a multi-year experiment involving induced mutagenesis was conducted to create genetic variation, assess trait associations and genetic divergence in four wheat varieties with differential grain texture treated with six doses of gamma rays and ethyl methane sulfonate using ten agro-morphological traits. Healthy selfed seeds of four bread wheat varieties with differential texture were irradiated using six doses ranging from 175 Gy-300 Gy of gamma rays (Co60: BARC, Mumbai) and six concentrations of ethyl methanesulfonate (0.3–1.3%) (Sigma-Aldrich, Bangalore, India) to evaluate variability, character association and degree of genetic diversity induced among the mutagenic treatments of wheat varieties with differential grain texture. Significant inter-population differences were observed for almost all the traits. The sample mean of twelve mutant populations in each of the cultivar exhibited superior quantitative phenotypic traits and increased values of the genetic parameters. Based on association and variability studies, plant height, spike length, grain filling period, biological yield per plant and harvest index can be used as early generation criteria for maximum genetic improvement. Multivariate studies indicated the contribution of various traits towards divergence and indicated the efficiency of mutagens in generating variability. Gamma-irradiation dosages between 200–250 Gy and 0.5–1.1% EMS for soft-textured varieties, whereas doses between 225–275 Gy and 0.5–0.9% EMS were found to be most potent for semi-hard-textured varieties. Assessment of mutagen sensitivity showed that semi-hard wheat varieties were responsive to both mutagens, particularly EMS and generated higher variability and divergence than the soft textured varieties. Hence, gamma rays were proved to be more effective in generating higher variability than ethyl methanesulfonate. A total of 117 putative mutants were identified with desirable agro-morphological attributes. Among these, mutants with higher inter-cluster distance can be used as parents in hybridization programs and serve as important genetic resources in future wheat improvement programs. [ABSTRACT FROM AUTHOR]

A popular approach to the simulation of multivariate, non-normal data in the social sciences is to define a multivariate normal distribution first, and then alter its lower-dimensional marginals to achieve the shape of the distribution intended by the researchers. A consequence of this process is that the correlation structure is altered, so further methods are needed to specify an intermediate correlation matrix in the multivariate normal distribution step. Most of the techniques available in the literature estimate this intermediate correlation matrix bivariately (i.e., correlation by correlation), risking the possibility of generating a non-positive definite matrix. The present article addresses this issue by offering an algorithm that estimates all elements of the intermediate correlation matrix simultaneously, through stochastic approximation. A small simulation study demonstrates the feasibility of the present method to induce the correlation structure both in simulated and empirical data. [ABSTRACT FROM AUTHOR]

Traditional techniques for calculating outstanding claim liabilities such as the chain-ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck & Debruyne (2011, Insurance: Mathematics and Economics , 48 , 85–98) and Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49 , 188–193). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49 , 188–193). The first technique is based on Adjusted Outlyingness (Hubert & Van der Veeken, 2008. Journal of Chemometrics, 22 , 235–246) and explicitly incorporates skewness into the analysis while providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016. Statistics: Methodology , 1–23) which is derived from the bagplot; however; it is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure. Furthermore, we extend our robust bivariate chain-ladder approach to an N-dimensional framework. The implementation of the methods, especially beyond bivariate, is not trivial. This is illustrated on a trivariate data set from Australian general insurers and results under the different outlier detection and treatment mechanisms are compared. [ABSTRACT FROM AUTHOR]