Analysis Of Variation Research Articles

PCA, PC-CVA, and Random Forest of GCIB-SIMS Data for the Elucidation of Bacterial Envelope Differences in Antibiotic Resistance Research.

Antibiotic resistance can rapidly spread through bacterial populations via bacterial conjugation. The bacterial membrane has an important role in facilitating conjugation, thus investigating the effects on the bacterial membrane caused by conjugative plasmids, antibiotic resistance, and genes involved in conjugation is of interest. Analysis of bacterial membranes was conducted using gas cluster ion beam-secondary ion mass spectrometry (GCIB-SIMS). The complexity of the data means that data analysis is important for the identification of changes in the membrane composition. Preprocessing of data and several analytical methods for identification of changes in bacterial membranes have been investigated. GCIB-SIMS data from Escherichia coli samples were subjected to principal components analysis (PCA), principal components-canonical variate analysis (PC-CVA), and Random Forests (RF) data analysis with the aim of extracting the maximum biological information. The influence of increasing replicate data was assessed, and the effect of diminishing biological variation was studied. Optimized m/z region-specific scaling provided improved clustering, with an increase in biologically significant peaks contributing to the loadings. PC-CVA improved clustering, provided clearer loadings, and benefited from larger data sets collected over several months. RF required larger sample numbers and while showing overlap with the PC-CVA, produced additional peaks of interest. The combination of PC-CVA and RF allowed very subtle differences between bacterial strains and growth conditions to be elucidated for the first time. Specifically, comparative analysis of an E. coli strain with and without the F-plasmid revealed changes in cyclopropanation of fatty acids, where the addition of the F-plasmid led to a reduction in cyclopropanation.

On a Schrödinger equation involving fractional [formula omitted]-Laplacian with critical growth and Trudinger–Moser nonlinearity

A nonlinear Schrödinger equation of fractional (N/s1,q)-Laplacian is considered with the Rabinowitz potential, critical Sobolev growth and Trudinger–Moser nonlinearity in RN−ΔN/s1s1u+−Δqs2u+V(ɛx)(uNs1−2u+uq−2u)=λfu+uqs2∗−2u.We establish the global existence of nonnegative ground-state solution for suitable parameter values primarily through variational analysis, fractional Trudinger–Moser inequality and mountain pass approach. It is a crucial ingredient to handle three aspects concerning the limiting setting s1p=N, the critical Sobolev growth and Trudinger–Moser nonlinearity.

A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics

As an indispensable component of air-conditioning system, the air handling unit (AHU) always exhibits strong nonlinearity and two-directional dynamics (time-wise and batch-wise dynamics). However, existing monitoring strategies cannot adequately figure out the AHU's two-directional dynamics from the nonlinear data. To boost the AHU's fault detection performance, a novel global modelling scheme integrated dynamic kernel canonical variate analysis (GDKCVA) is developed in this paper. The first contribution is to collect datasets from various days to develop a three-way training dataset and then normalize it along the batch-wise to suppress the batch-wise dynamics and variables' correlations. The second contribution is to establish the dynamic kernel CVA by integrating autoregressive moving average exogenous framework to more effectively confront the time-wise dynamics and variables' nonlinear relationships. The kernel matrices of augmented batch dataset are determined utilizing the kernel trick that transforms nonlinear data into high-dimensional linear space by leveraging kernel function. The third contribution is to propose a novel global modelling strategy to further eliminate the batch-wise dynamics by computing the total average kernel matrices from all the normal batch datasets. Finally, experiments and comparisons on the ASHRAE RP-1312 datasets are carried out to assess the suggested GDKCVA's fault detection capability. Compared with the kernel CVA (KCVA), the three-way training dataset based CVA (TCVA) and the three-way training dataset based KCVA (TKCVA), the proposed GDKCVA has the highest fault detection rates and its average fault detection rates of three monitoring statistics for eleven fault patterns reach 99.38 %, 99.15 % and 100 %, respectively.

Parabolic Regularity of Spectral Functions

This paper is devoted to the study of the second-order variational analysis of spectral functions. It is well-known that spectral functions can be expressed as a composite function of symmetric functions and eigenvalue functions. We establish several second-order properties of spectral functions when their associated symmetric functions enjoy these properties. Our main attention is given to characterize parabolic regularity for this class of functions. It was observed recently that parabolic regularity can play a central rule in ensuring the validity of important second-order variational properties, such as twice epi-differentiability. We demonstrates that for convex spectral functions, their parabolic regularity amounts to that of their symmetric functions. As an important consequence, we calculate the second subderivative of convex spectral functions, which allows us to establish second-order optimality conditions for a class of matrix optimization problems. Funding: The research of A. Mohammadi is funded by a postdoctoral fellowship from Georgetown University. E. Sarabi is partially supported by the U.S. National Science Foundation [Grant DMS 2108546].

A Minimax-Program-Based Approach for Robust Fractional Multi-Objective Optimization

In this paper, by making use of some advanced tools from variational analysis and generalized differentiation, we establish necessary optimality conditions for a class of robust fractional minimax programming problems. Sufficient optimality conditions for the considered problem are also obtained by means of generalized convex functions. Additionally, we formulate a dual problem to the primal one and examine duality relations between them. In our results, by using the obtained results, we obtain necessary and sufficient optimality conditions for a class of robust fractional multi-objective optimization problems.

Spatio-temporal feature extraction network based multi-performance indicators synergetic monitoring method for complex industrial processes

In the context of smart manufacturing, modern industrial processes are becoming increasingly complex in terms of process flows, product varieties, and performance indicators (PIs). Performance-driven process monitoring attracts extensive attentions in recent years. However, most methods require spatio-temporal correspondence between process variables and PIs, and rarely consider the correlation among various PIs. In this paper, a spatio-temporal feature extraction network-based multi-performance indicators synergetic monitoring framework is presented. Firstly, considering the missing data in PI measurements, a weighted sum of tensor nuclear norm (WSTNN) based batch-process data completion approach is developed, which can adeptly handle local missing and incomplete data issues and establish the spatio-temporal correspondence for subsequent modeling. Secondly, for a specific PI, a new canonical variate analysis embedded spatio-temporal convolutional network (CVA-STCN) is designed to extract the PI-related features with spatio-temporal dependency. Thirdly, considering the dynamic interaction of multiple PIs, a third-order feature tensor is established to perform the future fusion, and the correlations among various PI-related features are explored via tensor decomposition. Finally, a hierarchical multi-performance indicators synergetic monitoring model is developed over several subspaces. The proposed method is verified on Tennessee Eastman process and an actual hot strip mill process. Overall, the monitoring performance of the proposed method outperforms the traditional ones, in terms of higher fault detection rates and lower false alarm rates. Moreover, the information provided by the multi-subspace synergetic monitoring charts can offer valuable guidance to field engineers.

Evaluation of Cardiovascular Risk Profile and Risk Scores of Antiretroviral Therapy-naïve HIV Patients in Eastern India

Abstract Introduction: People living with human immunodeficiency virus (PLHIV) are known to have an increased prevalence of traditional cardiovascular risk factors and are at a higher risk of cardiovascular disease (CVD). This study was done to assess the CVD risk factors in treatment naïve PLHIV in a center of the national program. Methods: In this cross-sectional explorative study, traditional CVD risk factors were assessed, and 10-year Framingham and atherosclerotic cardiovascular disease (ASCVD) risk score were calculated in treatment naïve PLHIV attending the antiretroviral therapy (ART) center, IMS, BHU. Results: The study included 337 ART naïve patients. The prevalence of CVD risk factors in treatment naïve PLHIV - were low high-density lipoprotein cholesterol levels (81.4%), high triglyceride levels (32.7%), smoking (32.3%), obesity (13.6%), hypertension (5%), diabetes (2.7%), and high low-density lipoprotein cholesterol levels (2.1%). Moderate-to-high 10-year Framingham Risk Score and American Heart Association/American College of Cardiology 10-year ASCVD risk score were 10.8% and 8.9%, respectively. In Framingham Risk Score, age ≥40 years (odds ratio [OR] - 131) (95% confidence interval [CI] - 6.5–1043) alcohol intake (OR - 5.14 [95% CI - 1.82–14.46] and presence of tuberculosis (OR - 4.78) (95% CI - 1.48-15.40), while in ASCVD risk score history of alcohol intake (OR - 26.20 [95% CI - 3.1-216.8] were at higher risk of CVD in multivariate variate analysis. Conclusion: CVD risk factors were common among ART naïve patient. Thus, screening, education, and treatment of CVD risk factors should be done in these patients at initiation of care.

Quantile Delta-Mapped Spatial Disaggregation Analysis for Climate Variations over the Yangtze River Basin

Abstract Significant increases in temperature and precipitation due to global warming affect socioeconomics. Accurate analysis is needed for future temperature and precipitation variations across the Yangtze River basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time. The evaluation results show that the QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, while it shows improvement in reproducing precipitation. Projections indicate the annual-mean temperature will increase from 2020 to 2080 under shared socioeconomic pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest range of differences in summer. Conversely, annual-mean temperatures significantly decrease from 2081 to 2100 under SSP2-4.5. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual-mean precipitation will increase from 2020 to 2080 but decrease from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policymaking to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry).

Stability criteria and calculus rules via conic contingent coderivatives in Banach spaces

This paper addresses the study of novel constructions of variational analysis and generalized differentiation that are appropriate for characterizing robust stability properties of constrained set-valued mappings/multifunctions between Banach spaces important in optimization theory and its applications. Our tools of generalized differentiation revolve around the newly introduced concept of ε-regular normal cone to sets and associated coderivative notions for set-valued mappings. Based on these constructions, we establish several characterizations of the central stability notion known as the relative Lipschitz-like property of set-valued mappings in infinite dimensions. Applying a new version of the constrained extremal principle of variational analysis, we develop comprehensive sum and chain rules for our major constructions of conic contingent coderivatives for multifunctions between appropriate classes of Banach spaces.

Spatiotemporal geographically weighted regression analysis for runoff variations in the Weihe River Basin

In order to investigate the effects of vegetation changes on runoff and to obtain recommendations for improving runoff in the Weihe River Basin (. In this study, a spatiotemporal geographic autocorrelation weighted regression analysis (SGAWRA) approach was newly developed based on previous studies. This approach investigates spatial non-stationarity of the dynamic response from vegetation variations to climatic change and human activity. Implications of spatial non-stationarity related to runoff variability were also discussed, which in turn yield the effect that vegetation changes have on runoff. The method systematically analysed the spatial non-stationarity of vegetation variations and its associated effects on runoff. Therefore, more closely related results with less error were produced at each step, and results with more accuracy were obtained. These results indicated that the average trend rates of NDVI in the annual average, each season, and the growing season (Growing season refers to April to September) exceeded 0. Areas where NDVI show a growing trend cover more than 50%, which is greater than the area with a decreasing trend. The GWR regression parameters of precipitation, average temperature, and NDVI are all greater than 0. The GWR regression parameters of human activities and NDVI also have more than 50% of the area greater than 0. Based on the visual analysis of the calculation results, it can be seen that there are obvious spatial trends in the data, and the spatial data are significantly different between different regions. Therefore, WRB can be regarded as spatio-temporally non-stationary. In the WRB, the underlying surface change with vegetation change as the prominent feature is the leading cause (about 60%) of the runoff attenuation. The results showed that WRB has spatial and temporal non-stationarity. The spatial non-stationarity of vegetation has a greater effect on runoff changes. The results of this study support recommendations for improving runoff in the WRB.

Beyond profit margins: Orchestrating social, economic, and environmental sustainability within the Norwegian Salmon Food Supply Chain

Food Supply Chains (FSCs) have become increasingly complex with the average distance between producers and consumers rising considerably in the past two decades. Consequently, FSCs are a major source of carbon emissions and reducing transportation costs a major challenge for businesses. To address this, we present a mathematical model to promote the three core dimensions of sustainability (economic, environmental, and social), based on the Mixed-Integer Linear Programming (MILP) method. The model addresses the environmental dimension by intending to decrease the carbon emissions of different transport modes involved in the logistics network. Several supply chain network characteristics are incorporated and evaluated, with a consideration of social sustainability (job generation from operating various facilities). The mathematical model's robustness is demonstrated by testing and deploying it to a variety of problem instances. A real-life case study (Norwegian salmon supply chain) helps to comprehend the model's applicability. To understand the importance of optimizing food supply networks holistically, the paper investigates the impact of multiple supply chain permutations on total cost, demand fluctuations and carbon emissions. To address fluctuations in retail demand, we undertook sensitivity analysis for variations in demand, enabling the proposed model to revamp Norway's salmon supply chain network. Subsequently, the results are thoroughly examined to identify managerial implications.

GENETIC AND TRAIT VARIABILITY OF GYRINICOLA REVEALS THE EXISTENCE OF AT LEAST FOUR SPECIES WITHIN THE UNITED STATES.

The tadpole-dwelling pinworm, Gyrinicola batrachiensis (Walton, 1929) Adamson, 1981 was recognized as the sole representative of the genus across Canada and the United States. However, evaluation of the morphology of these parasites across their range revealed considerable morphological variability that suggested diagnosable morphotypes. These morphotypes were associated with different species of anurans, several of which occurred in sympatry. Herein we use an extensive geographic sampling across the United States to obtain the morphotypes, screen their genetic diversity, and analyze this information using an integrative approach. We reconstructed their phylogeny using nuclear ribosomal partial genes 18S and 28S, ITS1, 5.8S, and ITS2, as well as 5 mitochondrial genes generated with Next-Generation sequencing technology. This phylogeny reveals 3 well-resolved lineages, which upon the use of a statistical approach (bPTP [Bayesian implementation of the Poisson tree processes]) supports the delimitation of 4 distinct groups equivalent to species. These putative species groups were tested using morphological characteristics paired with a MANOVA and canonical variate analysis. Results suggest that at least 4 species of Gyrinicola are present within North America, resulting in the resurrection of G. armatus (Walton, 1933) and the description of 2 new species.

Geometric morphometric analysis of beak shape of Columbimorphae (Columbas, Van, Mardin and Dönek).

Selective breeding over centuries has led to the emergence of numerous pigeon breeds from the single species Columba livia, showcasing unparalleled phenotypic diversity. In Eastern Turkey, Van pigeons originate, while Tumbler pigeon varieties thrive countrywide, reflecting local traditions. The avian beak, vital for survival, offers insights into domestication and traits influenced by natural selection. Geometric morphometrics, a shape analysis method, allows for a comprehensive examination of beak shapes among domestic pigeons, providing a nuanced understanding of their complexity. Understanding beak diversity in pigeons, especially those found in Turkey, enhances our knowledge of avian evolution and adaptation processes. The study utilized 48 skulls from 4 pigeon breeds, including wild rock pigeons and domestic pigeons of Tumbler, Mardin and Van breeds, all free from pathological lesions and adults. Geometric morphometric analyses of beak shape were conducted using dorsal and lateral photographs, with landmarks placed using tpsDig software. MorphoJ software facilitated procrustes analysis and principal component analysis (PCA) to assess morphological variability and differentiate pigeon breeds based on shape patterns, with significance set at p < 0.05. PCA revealed significant shape variations among pigeon breeds, with 47 principal components identified for lateral beak views and 36 principal components for dorsal views. Canonical variates analysis further distinguished morphological patterns among breeds, indicating distinct shape variations in both dorsal and lateral views, elucidating the unique characteristics of each breed's beak morphology. Our results demonstrate statistically significant differences in pigeon beak shape, particularly in the lateral view, confirming the importance of these variations (p < 0.05) and suggesting that such differences occur less than 5% of the time under the null hypothesis.

Variation in the Floral Morphology of Prosthechea karwinskii (Orchidaceae), a Mexican Endemic Orchid at Risk.

Prosthechea karwinskii is an orchid endemic to Mexico, threatened by the destruction of its habitat and the extraction of specimens to meet its demand for ornamental and religious use. Most of its populations, including the most locally abundant ones, are found in Oaxaca state. Variations in some floral traits have been observed in these populations. We implemented a morphometric analysis to assess their floral variation and identify the most significant characters in the morphological patterns of this orchid. Floral samples were collected from 17 populations of P. karwinskii in Oaxaca, as well as from specimens used as ornaments during Easter in an Oaxacan community (Zaachila), whose origin is unknown. Sampling of natural populations covered the environmental, geographic, and morphological variation of the species. We performed an analysis of variance (ANOVA), principal component analysis (PCA), canonical variate analysis (CVA), and cluster analysis, including 185 individuals and 45 variables (12 of them were discarded in the multivariate analyses due to high correlation). Characters of the column, lateral sepal, and labellum were most informative for the observed morphological patterns. Albarradas showed the greatest morphological differentiation, mainly due to the column. In general, individuals from the same locality tended to overlap more, especially the populations of Jaltianguis and Yahuiche, which were different from the geographically close population of Etla. Teposcolula presented the highest values in perianth characters, unlike Sola_Rancho Viejo. The specimens recovered from religious ornaments were morphologically more similar to those from Yanhuitlan and Etla. This morphometric analysis identified characters as potential taxonomic markers for P. karwinskii and related species, showing its potential to associate specimens of unknown origin with their probable geographical region. Our work encourages working on collaborative conservation strategies to ensure the long-term permanence of both the species and its traditional uses.

Craniometric Relationships of Migrant Victims of the April 18, 2015 Shipwreck Off the Coast of Libya

One of the most tragic events involving African migrants’ attempts to get to Europe was the 2015 shipwreck off the coast of Libya. More than 300 crania were recovered and are currently in the Laboratory of Forensic Anthropology and Odontology, Milan, Italy, where attempts are being made to identify them. This paper analyzes the cranial morphometrics in relation to what is known of African cranial variation. It also addresses questions of population subdivision on the ship as well as secular changes that may be reflected in cranial morphology.Crania were digitized using the 3skull software, which also computes Howells measurements from the coordinates. Migrant crania were compared to African reference samples consisting of both 19th-century sub-Saharan West Africans and East Africans. Statistical procedures were discriminant and canonical variate analysis and Mahalanobis distances. K-means unsupervised clustering was also used.Results showed that the migrant samples differed from the 19th-century samples systemically; the differences consisted mainly of lower facial projections and higher cranial vaults and bases. Position on the ship, whether on the deck or below in the holds, showed subdivision. Holds had a higher proportion of West Africans, and the deck had a higher proportion of East Africans. K-means clustering also found groups contrasting between the deck and the holds. Comparing migrant cranial morphology to 19th-century Africans using variables that respond to secular change showed that migrants reflect changes that have occurred in Africa over the past 200 years.We conclude that morphometric analysis can provide useful information concerning the composition of unidentified victims of tragic events such as the 2015 shipwreck.

Osteon shape variation in the femoral diaphysis: A geometric-morphometric approach on human cortical bone microstructure in an elderly sample.

Geometric morphometrics (GMM) have been applied to understand morphological variation in biological structures. However, research studying cortical bone through geometric histomorphometrics (GHMM) is scarce. This research aims to develop a landmark-based GHMM protocol to depict osteonal shape variation in the femoral diaphysis, exploring the role of age and biomechanics in bone microstructure. Proximal, midshaft, and distal anatomical segments from the femoral diaphysis of six individuals were assessed, with 864 secondary intact osteons from eight periosteal sampling areas being manually landmarked. Observer error was tested using Procrustes ANOVA. Average osteonal shape and anatomical segment-specific variation were explored using principal component analysis. Osteon shape differences between segments were examined using canonical variate analysis (CVA). Sex differences were assessed through Procrustes ANOVA and discriminant function analysis (DFA). The impact of osteonal size on osteonal shape was investigated. High repeatability and reproducibility in osteon shape landmarking were reported. The average osteon shape captured was an elliptical structure, with PC1 reflecting more circular osteons. Significant differences in osteon shape were observed between proximal and distal segments according to CVA. Osteon shape differed between males and females, with DFA showing 52% cross-validation accuracies. No effect of size on shape was reported. Osteonal shape variation observed in this study might be explained by the elderly nature of the sample as well as biomechanical and physiological mechanisms playing different roles along the femoral diaphysis. Although a larger sample is needed to corroborate these findings, this study contributes to the best of our knowledge on human microanatomy, proposing a novel GHMM approach.

Canonical variate residual analysis for industrial processes fault detection

AbstractWith the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data.

Using a machine learning algorithm and clinical data to predict the risk factors of disease recurrence after adjuvant treatment of advanced-stage oral cavity cancer

ABSTRACT Objectives: Head-and-neck cancer is a major cancer in Taiwan. Most patients are in the advanced stage at initial diagnosis. In addition to primary surgery, adjuvant therapy, including chemotherapy and radiotherapy, is also necessary to treat these patients. We used a machine learning tool to determine the factors that may be associated with and predict treatment outcome. Materials and Methods: We retrospectively reviewed 187 patients diagnosed with advanced-stage head-and-neck cancer who received surgery and adjuvant radiotherapy with or without chemotherapy. We used eXtreme Gradient Boosting (XGBoost) – a gradient tree-based model – to analyze data. The features were extracted from the entries we recorded from the electronic health-care system and paper medical record. The patient data were categorized into training and testing datasets, with labeling according to their recurrence status within the 5-year follow-up. The primary endpoint was to predict whether the patients had recurrent disease. The risk factors were identified by analyzing the feature importance in the model. For comparison, we also used regression to perform the variate analysis to identify the risk factors. Results: The accuracy, sensitivity, and positive predictive value of the model were 57.89%, 57.14%, and 44.44%, respectively. Pathological lymph node status was the most important feature, followed by whether the patient was receiving chemotherapy. Fraction size, early termination, and interruption were the important factors related to radiotherapy and might affect treatment outcome. The area under the curve of the receiver operating characteristic curve was 0.58. The risk factors identified by XGBoost were consistent with those found by regression. Conclusion: We found that several factors were associated with treatment outcome in advanced-stage head-and-neck cancer. In future, we hope to collect the data according to the features introduced in this study and to construct a stronger model to explain and predict outcomes.

GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.

Polygenic risk scores (PRS) are tools used to evaluate an individual's susceptibility to polygenic diseases based on their genetic profile. A considerable proportion of people carry a high genetic risk but evade the disease. On the other hand, some individuals with a low risk of eventually developing the disease. We hypothesized that unknown counterfactors might be involved in reversing the PRS prediction, which might provide new insights into the pathogenesis, prevention, and early intervention of diseases. We built a novel computational framework to identify genetically-regulated pathways (GRPas) using PRS-based stratification for each cohort. We curated two AD cohorts with genotyping data; the discovery (disc) and the replication (rep) datasets include 2722 and 2854 individuals, respectively. First, we calculated the optimized PRS model based on the three recent AD GWAS summary statistics for each cohort. Then, we stratified the individuals by their PRS and clinical diagnosis into six biologically meaningful PRS strata, such as AD cases with low/high risk and cognitively normal (CN) with low/high risk. Lastly, we imputed individual genetically-regulated expression (GReX) and identified differential GReX and GRPas between risk strata using gene-set enrichment and variational analyses in two models, with and without APOE effects. An orthogonality test was further conducted to verify those GRPas are independent of PRS risk. To verify the generalizability of other polygenic diseases, we further applied a default model of GRPa-PRS for schizophrenia (SCZ). For each stratum, we conducted the same procedures in both the disc and rep datasets for comparison. In AD, we identified several well-known AD-related pathways, including amyloid-beta clearance, tau protein binding, and astrocyte response to oxidative stress. Additionally, we discovered resilience-related GRPs that are orthogonal to AD PRS, such as the calcium signaling pathway and divalent inorganic cation homeostasis. In SCZ, pathways related to mitochondrial function and muscle development were highlighted. Finally, our GRPa-PRS method identified more consistent differential pathways compared to another variant-based pathway PRS method. We developed a framework, GRPa-PRS, to systematically explore the differential GReX and GRPas among individuals stratified by their estimated PRS. The GReX-level comparison among those strata unveiled new insights into the pathways associated with disease risk and resilience. Our framework is extendable to other polygenic complex diseases.

Molecular identification and morphological variations of Amblyomma lepidum imported to Egypt, with notes about its potential distribution under climate change

The tick Amblyomma lepidum is an ectoparasite of veterinary importance due to its role in transmitting livestock diseases in Africa, including heartwater. This study was conducted in 2023 to monitor Amblyomma spp. infestation in dromedary camels imported from Somalia, Ethiopia, and Sudan to Egypt. This study inspected 200 camels at the Giza governorate’s camel market that had been imported from Somalia, 200 from Ethiopia, and 200 from Sudan for tick infestation. Specimens were identified using morphological characteristics and phylogenetic analyses of the 12S and 16S rRNA genes. Clusters were calculated using an unweighted pair-group method with arithmetic averages (UPGMA) dendrogram to group the specimens according to their morphometric characteristics. The morphometric analysis compared the body shape of ticks collected from different countries by analyzing dorsal features. Principal component analysis (PCA) and canonical variate analysis (CVA) were performed to obtain body shape variation among specimens from different countries. Results indicated that camels were infested by 57 males Amblyomma lepidum, and no female specimens were observed; among these specimens, one may have a morphological abnormality. The results suggest that A. lepidum specimens collected from camels imported to Egypt from African countries exhibit locally adapted morphology with variations among specimens, particularly variations in body size. This adaptation suggests minimal potential for genetic divergence. Ecological niche modeling was used to predict the areas in Africa with suitable climates for A. lepidum. The study confirmed that East African countries might have the most favorable climatic conditions for A. lepidum to thrive. Interestingly, the amount of rain during the wettest quarter (Bio16) had the strongest influence on the tick’s potential distribution, with suitability decreasing sharply as rainfall increased. Future predictions indicate that the climatic habitat suitability for A. lepidum will decrease under changing climate conditions. However, historical, current, and future predictions indicate no suitable climatic habitats for A. lepidum in Egypt. These findings demand continuous surveillance of A. lepidum in camel populations and the development of targeted strategies to manage tick infestations and prevent the spread of heartwater disease.