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Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time‐varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time‐varying causal effects. However, the proposed approaches oversimplify temporal information and rely on overly restrictive structural assumptions, limiting their reliability in addressing time‐varying causal problems. This article considers a novel approach to estimate time‐varying effects through continuous‐time modelling by combining functional principal component analysis and weak‐instrument‐robust techniques. Our method effectively utilizes available data without making strong structural assumptions and can be applied in general settings where the exposure measurements occur at different timepoints for different individuals. We demonstrate through simulations that our proposed method performs well in estimating time‐varying effects and provides reliable inference when the time‐varying effect form is correctly specified. The method could theoretically be used to estimate arbitrarily complex time‐varying effects. However, there is a trade‐off between model complexity and instrument strength. Estimating complex time‐varying effects requires instruments that are unrealistically strong. We illustrate the application of this method in a case study examining the time‐varying effects of systolic blood pressure on urea levels. [ABSTRACT FROM AUTHOR]

A growing literature seeks to identify the factors that influence bank liquidity creation. While some studies focus on macroeconomic factors, others focus on bank-specific factors. In this paper, we examine the relative importance of different types of factors in driving bank liquidity creation. Using a panel of US banks, we find that fluctuations in bank liquidity creation are mainly driven by macroeconomic factors. Our results hold for banks in different size groups. [ABSTRACT FROM AUTHOR]

A comparison of the interannual (1985, 2004, and 2015) dynamics of nine structural and functional characteristics of macrophytobenthos in the coastal waters of Victory Park (a developing recreational zone of the city of Sevastopol, including the city beach) was carried out using one-way analysis of variance (ANOVA), factor analysis, and principal component analysis (PCA) and taking the depth of macrophyte habitat and concentrations of mineral forms of nitrogen and phosphorus into account. Interannual statistically significant differences have been found for most of the analyzed variables. It has been shown that rearrangement of the macrophytobenthos structure occurs with an increase in the nutrient pressure on the water area. Perennial dominant species are replaced by physiologically active and rapidly growing ephemeral species with a developed surface; the biomass of dominant species decreases against the background of increased biomass of epiphytes and associated species. As the nutrient pressure decreases, the bottom phytocoenosis begins to recover. [ABSTRACT FROM AUTHOR]

A statistical analysis of variability in the amino acid composition of the mitochondrial CYTB protein from Asian mountain voles (genus Alticola) has been carried out. The sequences used in the study (N = 36; 380 amino acid residues), as well as information about their geographic origin, have been taken from GenBank. For each sequence, amino acid frequencies have been calculated, as has the matrix of squared Euclidean distances between them, followed by the calculation of principal components (PCs) by the principal coordinate method. Within the space of the first two PCs (Σλ = 76.89%), all sequences are grouped according to their species membership and form the East Siberian (A. strelzowi, A. olchonensis, A. semicanus, and A. tuvinicus), southern (A. stracheyi, A. argentatus, A. albicaudus, A. stoliczkanus, and A. barakshin), and northeastern (A. lemminus and A. macrotis) groups. For the PC1 direction, a change in the frequencies of Met, Gly, Ile → Ala, Thr occurs; the component strongly correlates with a large subset of AA indices (N = 362 at p < 0.01, with allowance for multiple comparisons according to Bonferroni) and the average evolutionary rank of CYTB sequences. For the PC2 direction, there is a change in the Val and Phe → Leu frequencies. This direction significantly correlates with the latitude (r = 752; p < 0.001) and altitude (r = –717; p < 0.01) of the vole habitats, as well as with 21 climatic variables and 54 AA indices. [ABSTRACT FROM AUTHOR]

Abstract The goal of the current study was to assess genetic variability in Momordica dioica for both qualitative and morphological features to create trait-specific lines. The correlation coefficients among twenty-three different traits of twenty-one Momordica dioica gynoecious accessions calculated and summarized by using the best linear unbiased predictor (BLUPs). The traits contribution to principal components for growth, yield and yield attributing characters positively contributed towards the PC-I were tender fruit weight (FWT), mature fruit weight (FWM), ripen fruit weight (FWR), fruit yield per plant (MFYP), fruit yield per hectare (MFPH), seeds per fruit (NSF), fruit length (FL), fruit diameter (FD), inter-nodal length (IL), fruit pedicel length (FPL), leaf width (LW), leaf length (LL) and vine length (VL). Three groups were identified; group III had the most genotypes (10), followed by cluster II (07), and cluster I (04). Group II and III had the greatest inter-cluster value (8.73811), proceeded by group III and group I (8.738105) which indicates a wider genetic base among the genotypes under study. For the twenty-three traits, different genetic traits were analyzed, including heritability (H2), genetic advance (GA), genetic advance as a percentage of mean (GAM), and phenotypic coefficient of variability and genotypic coefficient of variability (PCV & GCV). High PCV and GCV were noted respectively for the VL (26.93 and 26.24), number of stems per plant (28.12 and 21.03), LL (23.60 and 23.48), LW (21.21 and 21.09), FPL (37.53 and 36.74), FL (22.06 and 21.75), FD (22.65 and 22.60), FWT (39.77 and 49.76), FWM (51.07 and 51.01), FWR (51.97 and 51.79), fruits per plant (51.97 and 51.79), NSF (22.99 and 21.06), 100 Seed weight (141.25 and 141.25), MFYP (54.14 and 53.94) and MFYH (54.13 and 53.93). High GA as per cent grand mean was observed for VL (73.59%), number stem per plant (23.76%), FL (22.01%), FD (23.40%), FWT (24.07%), NFP (99.70%), 100 Seed weight (25.55%), MFYP (21.14%), MFYH (28.55%) and ascorbic acid (115.58%). The study provided a comprehensive of genetic variability of Momordica dioica for morphological and qualitative traits to support the development of gynoecious lines with specific traits.

Evaluating air temperature perturbation is important to know the anthropic activity's effect on the environmental system. The study case was La Malinche volcano, concerning the urban, agricultural, and forest environments. The air temperature data (average, maximum, minimum, standard deviation and range), was analyzed by prin- cipal components analysis (PCA), and the Kruskal-Wallis (K-W) test. Data analyses were made on a diurnal (warming and cooling rates), daily and monthly basis. The K-W test showed that warming and cooling rates are significantly different between the agricultural, urban, and forest zones, despite the north and south sides of La Malinche volcano had significant differences. The PCA indicated more perturbation concerning the cooling rates of air temperature among the environments than the warming rates. The average, maximum, and minimum air temperature of the urban environment and the standard deviation and range of the agricultural environment were the highest. The minimum air temperature changes more than the maximum in the volcano's urban, agricultural, and forest south side. The K-W test showed that the environmental conditions differed sig- nificantly based on average and maximum. The daily air temperature on the north side of La Malinche Volcano was very different from the south side. The PCA with average, maximum, minimum, standard deviation, and range showed that the environments are modified. The average monthly air temperature in the agricultural and forest areas was lower than average. Minimum air temperature increase was more accentuated in urban areas than in agriculture and forest areas and increased more than maximum.

Multivariate relationships and variability of initial and post-harvest soil parameters in the North and South Transects of Bengaluru during 2017 and 2018 are assessed. North (South) Transect soils indicated initial pH of 5.9 (7.1), EC of 0.17 (0.16) dS m−1, N of 376.9 (415.3) kg/ha, P2O5 of 83.18 (64.31) kg/ha, K2O of 169.7 (247.3) kg/ha, Fe of 11.78 (10.86) ppm, Zn of 1.59 (1.13) ppm, Cu of 0.71 (0.6) ppm, Mn of 8.24 (7.74) ppm and B of 0.24 (0.21) ppm; compared to post-harvest pH of 6.1 (6.8), EC of 0.26 (0.19) dS m−1, N of 352.5 (383.4) kg/ha, P2O5 of 68.05 (54.14) kg/ha, K2O of 153.5 (222.1) kg/ha, Fe of 9.91 (8.75) ppm, Zn of 1.35 (0.93) ppm, Cu of 0.58 (0.53) ppm, Mn of 6.88 (6.72) ppm and B of 0.20 (0.19) ppm. Four PCs of post-harvest parameters explained a maximum variance of 59.85% in the North compared to 69.94% in the South Transects. Soil pH (−0.623), Fe (0.648), Zn (−0.603) on PC-1; Cu (0.628) on PC-2; EC (0.613) on PC-3; and B (−0.657) on PC-4 were significant in North, while pH (−0.715), Fe (0.891), Zn (−0.624), Mn (0.835) on PC-1; EC (0.771), N (0.618) on PC-2; K2O (0.718), B (0.774) on PC-3; P2O5 (0.673), Cu (0.647) on PC-4 were significant in South Transect. It may be recommended that the package of practices of crops should be modified by considering variability, relationships, loadings, and buildup of soil fertility parameters under semi-arid Alfisols. [ABSTRACT FROM AUTHOR]

A reduced‐rank mixed‐effects model is developed for robust modelling of sparsely observed paired functional data. In this model, the curves for each functional variable are summarized using a few functional principal components, and the association of the two functional variables is modelled through the association of the principal component scores. A multivariate‐scale mixture of normal distributions is used to model the principal component scores and the measurement errors in order to handle outlying observations and achieve robust inference. The mean functions and principal component functions are modelled using splines, and roughness penalties are applied to avoid overfitting. An EM algorithm is developed for computation of model fitting and prediction. A simulation study shows that the proposed method outperforms an existing method, which is not designed for robust estimation. The effectiveness of the proposed method is illustrated through an application of fitting multiband light curves of Type Ia supernovae. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciencia y Tecnología Agropecuaria is the property of Agrosavia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper applied the fuzzy function approach, combined with the ridge regression model, to produce daily rainfall projections from large-scale climate variables. This study developed a statistical downscaling model based on principal components, c-means fuzzy clustering, Volterra series, and ridge regression. The model is known, hereafter as SDC2R2. In the developed downscaling model, the use of ridge regression, instead of multiple linear regression, is proposed to downscale daily rainfall with wide range (WR) predictors. The WR predictors were applied to sufficiently incorporate climate change signals. The developed model also captured the non-linear interactions of the climate variables by applying the transformation of Volterra series realization over WR predictors. This transformation was performed by applying principal components as orthogonal filters. Further, these principal components were clustered by using c-means clustering and non-linear transformations were applied on these membership functions, to improve the prediction ability of the model. The reanalysis of climate data from the National Centres for Environmental Prediction (NCEP) was used to develop the model and was validated by using the Global Climate Model (GCM) for four locations in the Manawatu River basin. The developed model was used to obtain future daily rainfall projections from three Representative Concentrative Pathways (RCP 2.6, RCP 4.5, and RCP 8.5) scenarios from the Canadian Earth System Model (CanESM2) GCM. The performance of the model was compared with a widely used statistical downscaling model (SDSM). It was observed that the model performed better than SDSM in downscaling rainfall on a daily basis. Every scenario indicated that there is a probability of obtaining high future rainfall frequency. The results of this study provide valuable information for decision-makers since climate change may potentially impact the Manawatu basin. [ABSTRACT FROM AUTHOR]

• Driving simulator variables form factors aligning with driving behavior theories. • Driving simulator variables factor into operational and tactical driving behaviors. • Operational driving is informed by simulator variables quantifying variability. • Tactical driving is most informed by simulator variables representing speed. Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments. One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments. The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment. These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen. [ABSTRACT FROM AUTHOR]

A method is proposed for analyzing the tremor of the earth's surface, measured by GPS, in order to highlight prognostic effects. The method is applied to the analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of the principal components from the clusters, with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes, averaged over the number of clusters and the times of earthquakes in the vicinity of Japan with minimum magnitude thresholds of 5.5 for the time interval 2012–2023. It is shown that the sequence of the largest local maxima of instantaneous amplitudes significantly more often precedes the moments of time of earthquakes (roughly speaking, has an "influence") than the reverse "influence" of earthquakes on the maxima of amplitudes. [ABSTRACT FROM AUTHOR]

Vamsadhara is a prominent river in north-western Andhra Pradesh. Without enough and reliable knowledge on the quality of the water, people utilize it for home and agricultural purposes quite a lot. This study aims to evaluate the quality of river water for agricultural and domestic consumption. Water samples from 10 sampling stations were collected during the pre and post-monsoon seasons of 2021, and the water quality index and agricultural indices were computed. The Water Quality Index was found to be satisfactory in both the seasons. Correlation and regression analyses are performed to understand the relationship between various hydrochemical parameters. The pre-season and post-season principal components were found to explain 81.3% and 78.9% of the total variance, respectively. The variables in both seasons are solely hydro-chemical and are believed to result from geological processes, suggesting geogenic sources. Water quality parameters for irrigation are within the limits. In irrigated soils, a higher growth of exchangeable Na is typically encouraged by elevated Mg levels. Water with a magnesium hazard level below fifty is considered suitable for irrigation. The Mg hazard was slightly excessive in most of the examined locations of the Vamsadhara River. The river water quality for irrigation is good to excellent, with exemptions from MH. Prioritizing effective sewage water treatment facilities before release into rivers. [ABSTRACT FROM AUTHOR]

Based on the evolutionary resilience approach and using Principal Component Analysis, this paper proposes a composite weighted index to measure levels of rural resilience and, in addition, to unravel why some rural areas are less resilient than others. Taking the region of Cantabria (northern Spain) at the municipal level as a sort of ‘laboratory’, the overall results unveil poor levels of resilience and suggest that the main factors limiting resilience are related to cultural interest, rural potential, natural endowment, and connectivity. Our findings also reveal considerable spatial heterogeneity across the different domains of resilience. As a specific contribution, the study brings together rural resilience levels and population growth (as population decline is the first direct consequence of a lack of resilience) in order to define different categories of rural territories, among which laggard rural areas are singled out as areas to be targeted for political intervention. When focusing on such areas, a crucial finding of the paper is that some municipalities that are lagging behind have not been included by the Regional Government as being at severe risk of depopulation, meaning that they cannot benefit from close public monitoring and fiscal support. [ABSTRACT FROM AUTHOR]

Intensive cultivation coupled with scrupulous use of fertilizers could be detrimental to soil health and quality of the produce in the long run. Therefore, this study aimed to assess the predominant protected/polyhouse and open field vegetable production on soil quality. The changes in soil quality in comparison to control (fallow) indicate, whether the management practices being adopted under both the systems are sustainable or not. Thus, a study was conducted to identify the most appropriate soil quality indicators and finally to know about the status of soil quality both under polyhouse and open field system of cultivation in mid hills of North western Himalayas. Principal component analysis (PCA), a data reduction tool was employed to get the minimum data set for the assessment of soil quality. Geo-referenced soil samples were collected from 25 progressive farmers' cultivating vegetables under polyhouse, as well as open field conditions and analyzed for various physical, chemical and biological indicators of soil quality. The PCA was performed which screened out the three principal components (PCs) with eigen value > 1. Average soil quality index (SQI) for different sites was observed to be on higher under polyhouse conditions (0.815) as compared to open field cultivation for vegetables (0.760) and control (0.703). Where, soil organic carbon contributed maximum to soil quality (75.6%) followed by pH (13.9%), water holding capacity (6.8%) and EC (3.6%). Conclusively, polyhouse cultivation system revealed high category of soil quality. Whereas, open field cultivation system revealed medium category of soil quality. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study explores the intricate interplay between sentiment extracted from Board of Directors (BOD) statements and the traditional financial metrics of banks. It investigates the relationship between a bank's financial performance, measured by various financial ratios, and the sentiment expressed in BOD statements. Design/methodology/approach: Drawing from a dataset encompassing BOD statements from 15 Jordanian banks spanning 2017 to 2021, sentiment analysis techniques are employed to derive sentiment scores. These scores are then juxtaposed with principal components distilled from financial metrics to examine their predictive relationship. Findings: Preliminary findings reveal significant non-linear patterns between Hu and Liu's (2004) sentimental analysis (Lui&lowbar;sen), the Data Science Lab's (n.d.) multilingual sentiment tool (multi&lowbar;sen), and the principal components. A subsequent random-effects panel regression further elucidates these relationships, highlighting the significant influence of specific principal components on sentiment scores. This study provides a compelling case for integrating sentiment analysis with traditional financial indicators, offering a more comprehensive toolkit for evaluating bank performance. Research limitations/implications: Future research should explore broader datasets across multiple sectors, to validate the generalisability of the findings beyond the banking sector in Jordan. Practical implications: This research holds implications for investors, analysts, and stakeholders by combining sentiment analysis with traditional financial metrics. It offers deeper insights into the health of banks and the intentions of management. This integrated approach allows stakeholders to make informed decisions and mitigate risks based on a more comprehensive view of banks' financial situations. Additionally, the possibility of real-time sentiment analysis enables the dynamic monitoring of banking institutions' financial health as updated data becomes available. Social implications: The study's results are valuable to users of financial information, aiding the Association of Banks and the Central Bank of Jordan in formulating policies for the banking sector. Auditors, consultants, and regulatory bodies in Jordan can also benefit from these insights. Originality/value: This is the first study exploring the relationship between sentiment extracted from BOD statements and traditional financial metrics in Jordan. It provides novel evidence of this relationship, contributing to the literature on the integration of textual data analysis and financial performance. [ABSTRACT FROM AUTHOR]

Beans usually have similar physical attributes; thus, it is difficult to distinguish them manually. Size, shape, and mass attributes of seeds help in breeding, selection, classification, separation, and machine design. This study was conducted to determine physical attributes of 20 bean genotypes with the use of image processing techniques. Color characteristics of the present genotypes were also determined. Then, four different machine learning algorithms (MLP, RF, SVR, and k-NN) were employed to predict seed mass. Among the present genotypes, Güzelöz and Özdemir genotypes had the highest size, shape, and color characteristics. Highly significant positive correlations were encountered between projected area-equivalent diameter (r = 1.00), between geometric mean diameter—surface area and volume (r = 1.00). On the other hand, highly significant negative correlations were seen between sphericity—elongation in vertical orientation (r = − 0.98). In hierarchical cluster analysis for physical attributes, Alberto–Aslan and Aras 98–Şahin genotypes were identified as the closest genotypes. According to PCA analysis, the first two principal components (PC1 and PC2) were able to explain 73% of total variation among the genotypes. While PC1 axis included projected area (vertical), equivalent diameter (vertical), and length, PC2 axis included L*, a*, b*, sphericity, roundness (vertical), and elongation (vertical). Among the present machine learning algorithms, RF yielded the best performances in mass estimation of bean seeds. It was concluded that machine learning techniques increased the efficiency of related machinery and helped to save time and labor. [ABSTRACT FROM AUTHOR]

ABSTRACT The objective was to verify the existence of lineages in the Mangalarga Marchador breed using microsatellites and to phenotypically characterize the lineages found using linear morphometric measurements taken at the time the animals were registered to be used as a tool for selecting individuals according to each breeder’s selection objective. The genotyping database contained 255,257 Mangalarga Marchador horses born between 2005 and 2021. In addition, a database was used with pedigree information on 622,299 animals born between 1952 and 2020 and a database of phenotypes assessed at the time of definitive registration of these animals, containing 303,248 animals born between 1950 and 2018. The databases used came from the Associação Brasileira dos Criadores do Cavalo Mangalarga Marchador. To verify the existence of genetic differences according to the lineages of the breed, a discriminant analysis of principal components (DAPC) and the Kruskall Wallis test were carried out to perform phenotypic characterization to compare the means of the morphometric measurements, both using the R software. The DAPC analysis showed the genetic differentiation of the Angaí and Herdade lineages in relation to the others. The Herdade lineage showed greater phenotypic differentiation when compared with the three separate genetic groups in this study, demonstrating that the animals of this lineage are smaller and more gathered and have higher scores for gait and morphology. When compared to the historically described Mangalarga Marchador lineages, the breed does not have so many genetic groups, separating it into just three groups (Angaí, Herdade, and General), and when compared to phenotypic characteristics, only the Herdade lineage stood out.

This paper primarily aims to address aspects related to regional development in Romania, which has become a challenge both at the national and global level. The way regional development is approached is different, depending on the particularities of each country, as well as the governing ideologies in each state. However, the goal is common, one aims at shaping a competitive and strong economy, a high standard of living, access to education and medical assistance. The purpose of the paper is to compare socio-economic indicators from two time periods, to highlight similarities and differences and to observe their evolution within a 10-year timespan. The method used is Principal Component Analysis (PCA) to identify groups of statistical indicators (variables) that explain the level of development within a sample, in our case the development regions of Romania. In the context of regional development, PCA can be applied to extract relevant information about economic, social or demographic variability in different regions of the country. Several objectives of regional development analyzed through the lens of this method were pursued: identification of regional disparities, optimization of resource allocation, evaluation of the determining factors of economic development, monitoring of the impact of regional policies. Following the PCA analysis, it was observed that regional development cannot be evaluated by means of variables belonging to a single sector. This method allowed the identification of the variables that have the greatest weight describing economic, social, educational and infrastructure aspects. PCA was applied for two time periods and similar situations were obtained, the first main component called by the authors the economic statistics includes for both periods the indicators: Gross average nominal salary gain, GDP by development regions, Employees from research and development activity at the end of the year, Beds in health facilities (state and private). The second component was called social statistics and includes the indicator Employment rate in working age (15-64 years), and the third component called agricultural statistics includes the indicator Population served by the public water supply system. As a conclusion, we consider that the situations are not overlapping because there are indicators that have undergone changes during the 10 years, which is expected and normal in regional development. For example, the number of graduates decreased due to the continuously decreasing birth rate, the vacancy rate decreased in 2018-2019 compared to 2008-2009 because Romania went through the financial economic crisis.

This study investigated genotype × environment interactions for the stability of expression of four productivity traits (cobs yield, cobs I class trade share, lend of cobs and fulfilment of cobs) of sweet maize hybrids (Zea mays L.). The additive main effects and multiplicative interaction (AMMI) model was employed to assess genotype × environment interaction. AMMI stability value was used to evaluate both stability and genotype. The genotype selection index was calculated for each hybrid, incorporating both the average trait value and the stability index. Ten sweet maize hybrids were evaluated: Golda, GSS 1453, GSS 3071, GSS 5829, GSS 8529, Overland, Noa, Shinerock, Sindon, and Tessa. Trials were ran conducted over four vegetative seasons at a single location in the Wielkopolska region using replicated field experiments. The AMMI model revealed significant genotypic and environmental effects for all analyzed traits. Based on their superior stability and favorable average trait values, both the Golda cultivar and the GSS 3071 hybrid are recommended for further breeding program inclusion. [ABSTRACT FROM AUTHOR]

A total of 108 diverse sorghum (Sorghum bicolor) accessions were characterized for quantitative and qualitative fodder-related traits and zonate leaf spot (ZLS) (Gloeocercospora sorghi) disease during two successive wet seasons of 2019 and 2020 in augmented randomized block design. The Shannon's diversity index and analysis of variance showed the existence of significant variability among qualitative and quantitative traits. K -mean clustering showed strong relationship between green fodder yield (GFY) and other yield-contributing traits. The dendrogram constructed based on morphological traits classified accessions into four diverse groups and most of genotype fall under cluster II. The principal component analysis bi-plot analysis showed a total variation of 68.96%, where GFY, stem weight per plant, panicle length and dry matter yield (DMY) contributed significantly. From the experimental results, three sorghum genotypes viz., IG-03-424, IG-01-436 and IG-03-438 were identified as promising for higher GFY (808.66 g/plant) and DMY (238.0 g/plant), respectively. Further, based on disease reactions under natural condition, five genotypes viz. , EC-512397, EC512393, EC512394, EC512399 and IG-02-437 were identified as potential donor for resistance to ZLS disease. These selected lines could be used as promising sources for high biomass and disease resistance in forage sorghum breeding programme. [ABSTRACT FROM AUTHOR]

First-order basin morphology is important for the functioning of the whole hydrologic system. This study examines the geomorphology of three areas in the tectonically active Siwalik Hills, Nepal, using morphometric variables. Twenty variables for 11,200 first-order basins were obtained from maps and Digital Elevation Models (DEMs) of three areas (the Machheli, Banganga and Binai river basins). Principal component analysis of each area condensed these into 6 major components, which accounted for 78–82% of the total variance in each data set. Rotated principal components were remarkably consistent between the three areas. The average slope, area, elevation range, valley angle, shape index, hypsometric integral, basin asymmetry factor and drainage density were identified as the eight most important morphometric variables defining these components. Ordinal grouping and k-means clustering led to recognition of eight basin types, distinguished predominantly by elevation range, average slope, area and valley angle. Shallow and steep basins are predominant in all three areas. The spatial distribution of first-order basin types exhibits a distinct pattern of geomorphic regions. The study demonstrated that a first-order basin is a suitable unit to demonstrate the morphological characteristics of a geologically complex area, and the study approach can be applied more broadly. [ABSTRACT FROM AUTHOR]

Objective: Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable. Methods: To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume. Results: Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature. Conclusions: In summary, our results support accurate multiclass ML classification regarding brain metastases distribution. [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]

Chocolate spot and heat stress devastatingly impact the production of faba bean, particularly under prevailing climatic changes and rising drastic environmental conditions. Hence, the adaptability of faba bean performance is a decisive objective of plant breeders to ensure its sustainable production. The present study aimed to evaluate the agronomic performance and stability of diverse eleven faba bean genotypes for yield characters, chocolate spot, and heat stress in eight different growing environments. The faba bean genotypes were evaluated at two sowing dates in two different locations during two growing seasons. The evaluated eleven faba bean genotypes were sown timely in autumn (25 October) and late sowing in early winter (25 November) in Bilbeis and Elkhatara during 2020 and 2021 growing seasons. The results exhibited substantial differences among the evaluated sowing dates, locations, and faba bean genotypes for all studied characters. The genotypes Sakha-3, Nubaria-3, Nubaria-5, Misr-3, and Wadi-1 were able to produce acceptable yield and quality characters under timely sowing in autumn and late sowing in early winter in all tested environments. Moreover, the genotypes Nubaria-3, Nubaria-4, Nubaria-5, Sakha-4, Giza-3, and Triple White exhibited better resistance to chocolate spot. The assessed faba bean genotypes were evaluated under late sowing to expose the plants to high temperature stress at flowering and throughout the anthesis and seed-filling stages. The genotypes Nubaria-5, Nubaria-3, Nubaria-4, Sakha-3, Sakha-4, Wadi-1, and Misr-3 possessed tolerance to heat stress more than the other genotypes. Different statistical methods were applied to study the stability of assessed genotypes such as joint regression, Additive Main Effect and Multiplicative Interaction (AMMI) analysis, AMMI stability value, Wricke's and Ecovalence values. The estimated stability parameters were consistent in depicting the stability of the assessed faba bean genotypes. The findings revealed that Sakha-1, Misr-3, Nubaria-4, and Nubaria-5 demonstrated stable and desirable performance across all tested environments. The heatmap was employed to classify the assessed faba bean genotypes into different groups based on agronomic performance, chocolate spot resistance and heat stress tolerance. Nubaria-3, Nubaria-4, Nubaria-5, and Misr-3 had the best performance for agronomic performance, chocolate spot resistance, and heat stress tolerance. The obtained results provide evidence of employing promising faba bean genotypes for improving the stability of agronomic performance, chocolate spot resistance, and heat stress tolerance in breeding programs principally under unprecedented climate fluctuations. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether a novel signal derived from tumor motion allows more precise sorting of 4D‐magnetic resonance (4D‐MR) image data than do signals based on normal anatomy, reducing levels of stitching artifacts within sorted lung tumor volumes. Methods: (4D‐MRI) scans were collected for 10 lung cancer patients using a 2D T2‐weighted single‐shot turbo spin echo sequence, obtaining 25 repeat frames per image slice. For each slice, a tumor‐motion signal was generated using the first principal component of movement in the tumor neighborhood (TumorPC1). Signals were also generated from displacements of the diaphragm (DIA) and upper and lower chest wall (UCW/LCW) and from slice body area changes (BA). Pearson r coefficients of correlations between observed tumor movement and respiratory signals were determined. TumorPC1, DIA, and UCW signals were used to compile image stacks showing each patient's tumor volume in a respiratory phase. Unsorted image stacks were also built for comparison. For each image stack, the presence of stitching artifacts was assessed by measuring the roughness of the compiled tumor surface according to a roughness metric (Rg). Statistical differences in weighted means of Rg between any two signals were determined using an exact permutation test. Results: The TumorPC1 signal was most strongly correlated with superior‐inferior tumor motion, and had significantly higher Pearson r values (median 0.86) than those determined for correlations of UCW, LCW, and BA with superior‐inferior tumor motion (p < 0.05). Weighted means of ratios of Rg values in TumorPC1 image stacks to those in unsorted, UCW, and DIA stacks were 0.67, 0.69, and 0.71, all significantly favoring TumorPC1 (p = 0.02–0.05). For other pairs of signals, weighted mean ratios did not differ significantly from one. Conclusion: Tumor volumes were smoother in 3D image stacks compiled using the first principal component of tumor motion than in stacks compiled with signals based on normal anatomy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indian bean is a underutilized leguminous crop with immense potential to play an essential role in sustainable agriculture, nutrition, and financial security for India's small and marginal farmers. The present research study was conducted at the Central Horticultural Experiment Station, ICAR-CIAH Godhra, Gujarat, for three consecutive growing seasons in 2020, 2021, and 2022 to evaluate the 60 diverse Indian bean types for nine quantitative traits, including plant height, number of primary branches, number of pods per plant, pod length, pod girth, pod weight, days to first flower, days to first harvest, and pod yield per plant. The four principal components PC-I through PC-IV with eigenvalues greater than one account for 77.33% of total variance among 60 genotypes. The first principal component (PC-I) eigenvalues accounted for 34.84% of total variance, followed by PC-II (18.24%), PC-III (13.73%), and PC-IV (10.52%). Cluster V and VIII had the greatest inter-cluster D2 value (1123.38), followed by Cluster IV and VIII (1051.66). Days to first flowering (39.55%) contributed the most to total genetic diversity, followed by pod length (29.44%), pod weight (8.42%), pod yield (4.58%), and main branches (4.35%). According to the clustering pattern, the bulk of the features required for high pod production were found in cluster II, followed by cluster I. The genotypes belonging to the cluster with the greatest inter-cluster distance are genetically more diverse and might be employed in a hybridization programme to get potential segregants. [ABSTRACT FROM AUTHOR]

Copyright of Acta Veterinaria Brasilica is the property of Acta Veterinaria Brasilica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PURPOSE: Brain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. METHODS: We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma- knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER, PR, Herceptin). We used kernel density estimators (KDE) to highlight clustering and distribution regions in the brain, and we used the metric of mutual information (MI) to tease out subtle differences in the BM distributions associated with different molecular subtypes of BC. BM location maps according to vascular and anatomical distributions using Cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. RESULTS: We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, the mutual information metric reveal that the ER-PR-Her2+ and ER-PR-Her2- subtypes show the smallest amount of mutual information and are most molecularly distinct. The kernel density contour plots show a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. CONCLUSIONS: We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment. Further characterization of these patterns with larger multi-institutional data-sets may have major impacts on treatment or management of cancer patients.

The purpose of this study is to examine the elements that strengthened market attractiveness in Romania, between 2000 and 2022. To fulfil this, we considered a selection of variables upon which we carried out a Principal Component Analysis that allowed to underline the factors associated with Romanian market attractiveness. After exploring the correlation between Foreign Direct Investment, Gross Domestic Product, Labor Force Participation Rate, Logistics Performance Index and Political Stability, the PCA unveiled five components, with the first two accounting for 83.01% of the total variance in the data. Formulated on the initial variables' loadings on these components, FDI, GDP, LFPR, LPI, and POLSTABILITY appear to be significant factors that promoted market attractiveness in Romania. Still, a more in-depth analysis and interpretation of the data is required to fully comprehend the factors that make attractive the Romanian market.

Abstract The computation of a similarity measure for genomic data is a standard tool in computational genetics. The principal components of such matrices are routinely used to correct for biases due to confounding by population stratification, for instance in linear regressions. However, the calculation of both a similarity matrix and its singular value decomposition (SVD) are computationally intensive. The contribution of this article is threefold. First, we demonstrate that the calculation of three matrices (called the covariance matrix, the weighted Jaccard matrix, and the genomic relationship matrix) can be reformulated in a unified way which allows for the application of a randomized SVD algorithm, which is faster than the traditional computation. The fast SVD algorithm we present is adapted from an existing randomized SVD algorithm and ensures that all computations are carried out in sparse matrix algebra. The algorithm only assumes that row-wise and column-wise subtraction and multiplication of a vector with a sparse matrix is available, an operation that is efficiently implemented in common sparse matrix packages. An exception is the so-called Jaccard matrix, which does not have a structure applicable for the fast SVD algorithm. Second, an approximate Jaccard matrix is introduced to which the fast SVD computation is applicable. Third, we establish guaranteed theoretical bounds on the accuracy (in $$L_2$$ L 2 norm and angle) between the principal components of the Jaccard matrix and the ones of our proposed approximation, thus putting the proposed Jaccard approximation on a solid mathematical foundation, and derive the theoretical runtime of our algorithm. We illustrate that the approximation error is low in practice and empirically verify the theoretical runtime scalings on both simulated data and data of the 1000 Genome Project.

This paper reviews popular signature and anomaly-based intrusion detection systems (IDS). Dimensionality reduction techniques (DRT) are used to increase the efficiency of such systems for real-time operation. Autoencoder-based IDS is rapidly gaining in popularity, primarily due to its inherent ability to denoise data and reduce dimensionality. In addition to the efficiency, we also look at the classification techniques used by various authors, and the overall impact of a model in terms of performance metrics. This article is written for novices in cyber security to get a jumpstart on the latest IDS algorithms. The purpose is to give useful insights into the broad and progressive view of various techniques in wide use, expose high-impact future research areas and to summarize classic IDS methods and feature selection techniques. [ABSTRACT FROM AUTHOR]

Rice grains represent a significant source of carbohydrates and contain trace amounts of other essential nutritional components, including proteins and lipids. The aromatic attributes of rice germplasm typically linked to the existence of aromatic amino acids which are often identified through conventional sensory time intensive assessment methods with statistical significance of p-value of 0.518 × 10−7 and SD of 1.74. Similarly, Raman spectroscopy is explored for the rapid analysis of intact brown rice kernels. In this study, we have examined the efficacy of Raman spectroscopy in conjunction with chemometric approaches for the identification of rice germplasm. Additionally, the assessment of aromatic attributes yielded an accuracy rate of 78%. Notably, the spectral features at ~1603 cm−1 and ~1633 cm−1 within the fingerprint region played a role in detecting the presence of fibre and amide content. Moreover, low standard error of spectra confirms accuracy as statistical significance. The Raman spectroscopy facilitated the revelation of the nutritional profile of rice; certain Raman bands could have been obstructed due to the opaque thick outer layer characteristic of brown rice kernels. [ABSTRACT FROM AUTHOR]