Purifying selection drove the adaptation of mitochondrial genes along with correlation of gene rearrangements and evolutionary rates in two subfamilies of Whitefly (Insecta: Hemiptera).

Insect mitochondrial genomes (mitogenomes) are usually represented by a conserved gene order. Whiteflies exhibit gene rearrangement in their mitogenomes; however, understanding how nucleotide substitution rates shape gene rearrangement in whiteflies is unclear due to the limited number of mitogenomes. Additionally, the mechanisms by which selection pressure drives adaptations in mitochondrial genes in the two subfamilies of whiteflies are not yet known. Here, we analyzed 18 whitefly mitogenomes, including one newly generated mitogenome, to compare nucleotide substitution rates, selection pressure, and gene arrangements. The newly generated mitogenome is reported along with reannotation of Pealius mori and comparisons to other whitefly mitogenomes. Comparative studies on nucleotide composition of 18 whiteflies revealed the positive GC skewness, confirming the reversal of strand asymmetry. We found 11 rearranged gene orders within two subfamilies of whiteflies with 8-18 breakpoints of gene rearrangements. Members of the subfamily Aleyrodinae exhibit more complex pathways in the evolution of gene order as compared to the subfamily Aleurodicinae. Our findings also revealed that the increase or reduction of nucleotide substitution rates does not have an impact on any of the gene rearrangement scenarios depicting neutral correlation. Selection pressure analysis revealed that the mitogenomes from members of both the subfamilies Aleurodicinae and Aleyrodinae are characterized by intense purifying selection pressure.

Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.

Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among children younger than 5 years in sub-Saharan African (sSA) countries. We used the most recent (2012-2022) nationally representative Demographic and Health Surveys data of 33 sSA countries. The air pollution covariates such as global annual surface particulate matter (PM 2.5) and the nitrogen dioxide available in the form of raster images were obtained from the National Aeronautics and Space Administration (NASA). The MLA was used for predicting the symptoms of ARIs among under-five children. We randomly split the dataset into two, 80% was used to train the model, and the remaining 20% was used to test the trained model. Model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve. A total of 327,507 under-five children were included in the study. About 7.10, 4.19, 20.61, and 21.02% of children reported symptoms of ARI, Severe ARI, cough, and fever in the 2 weeks preceding the survey years respectively. The prevalence of ARI was highest in Mozambique (15.3%), Uganda (15.05%), Togo (14.27%), and Namibia (13.65%,), whereas Uganda (40.10%), Burundi (38.18%), Zimbabwe (36.95%), and Namibia (31.2%) had the highest prevalence of cough. The results of the random forest plot revealed that spatial locations (longitude, latitude), particulate matter, land surface temperature, nitrogen dioxide, and the number of cattle in the houses are the most important features in predicting the diagnosis of symptoms of ARIs among under-five children in sSA. The RF algorithm was selected as the best ML model (AUC = 0.77, Accuracy = 0.72) to predict the symptoms of ARIs among children under five. The MLA performed well in predicting the symptoms of ARIs and associated predictors among under-five children across the sSA countries. Random forest MLA was identified as the best classifier to be employed for the prediction of the symptoms of ARI among under-five children.

Population structure and selection signal analysis of indigenous sheep from the southern edge of the Taklamakan Desert.

Analyzing the genetic diversity and selection characteristics of sheep (Ovis aries) holds significant value in understanding their environmental adaptability, enhancing breeding efficiency, and achieving effective conservation and rational utilization of genetic resources. In this study, we utilized Illumina Ovine SNP 50 K BeadChip data from four indigenous sheep breeds from the southern margin of the Taklamakan Desert (Duolang sheep: n = 36, Hetian sheep: n = 74, Kunlun sheep: n = 27, Qira black sheep: n = 178) and three foreign meat sheep breeds (Poll Dorset sheep: n = 105, Suffolk sheep: n = 153, Texel sheep: n = 150) to investigate the population structure, genetic diversity, and genomic signals of positive selection within the indigenous sheep. According to the Principal component analysis (PCA), the Neighbor-Joining tree (NJ tree), and Admixture, we revealed distinct clustering patterns of these seven sheep breeds based on their geographical distribution. Then used Cross Population Extended Haplotype Homozygosity (XP-EHH), Fixation Index (FST), and Integrated Haplotype Score (iHS), we identified a collective set of 32 overlapping genes under positive selection across four indigenous sheep breeds. These genes are associated with wool follicle development and wool traits, desert environmental adaptability, disease resistance, reproduction, and high-altitude adaptability. This study reveals the population structure and genomic selection characteristics in the extreme desert environments of native sheep breeds from the southern edge of the Taklimakan Desert, providing new insights into the conservation and sustainable use of indigenous sheep genetic resources in extreme environments. Additionally, these findings offer valuable genetic resources for sheep and other mammals to adapt to global climate change.

Whole-genome analysis reveals distinct adaptation signatures to diverse environments in Chinese domestic pigs.

BACKGROUND: Long-term natural and artificial selection has resulted in many genetic footprints within the genomes of pig breeds across distinct agroecological zones. Nevertheless, the mechanisms by which these signatures contribute to phenotypic diversity and facilitate environmental adaptation remain unclear. RESULTS: Here, we leveraged whole-genome sequencing data from 82 individuals from 6 domestic pig breeds originating in tropical, high-altitude, and frigid regions. Population genetic analysis suggested that habitat isolation significantly shaped the genetic diversity and contributed to population stratification in local Chinese pig breeds. Analysis of selection signals revealed regions under selection for adaptation in tropical (55.5 Mb), high-altitude (43.6 Mb), and frigid (17.72 Mb) regions. The potential functions of the selective sweep regions were linked to certain complex traits that might play critical roles in different geographic environments, including fat coverage in frigid environments and blood indicators in tropical and high-altitude environments. Candidate genes under selection were significantly enriched in biological pathways involved in environmental adaptation. These pathways included blood circulation, protein degradation, and inflammation for adaptation to tropical environments; heart and lung development, hypoxia response, and DNA damage repair for high-altitude adaptation; and thermogenesis, cold-induced vasodilation (CIVD), and the cell cycle for adaptation to frigid environments. By examining the chromatin state of the selection signatures, we identified the lung and ileum as two candidate functional tissues for environmental adaptation. Finally, we identified a mutation (chr1: G246,175,129A) in the cis-regulatory region of ABCA1 as a plausible promising variant for adaptation to tropical environments. CONCLUSIONS: In this study, we conducted a genome-wide exploration of the genetic mechanisms underlying the adaptability of local Chinese pig breeds to tropical, high-altitude, and frigid environments. Our findings shed light on the prominent role of cis-regulatory elements in environmental adaptation in pigs and may serve as a valuable biological model of human plateau-related disorders and cardiovascular diseases.

Clinical Advances and Current Status in the Treatment of Ureteral Calculi.

Ureteral calculi are a common urological disease with a consistently high incidence and an increasing trend each year. Ureteral calculi treatment is an essential and hot topic in the urology field and holds a vital status in the urological work system. Recently, with rapid advances in urology, there have been continuous updates and developments in treatment modalities, and many new methods and techniques have emerged and are being applied in clinical settings; This has effectively improved the clinical treatment outcomes of individuals with ureteral calculi. However, each treatment modality has its specific indications, and owing to the uneven distribution of medical resources and the effect of the patients' conditions and nature of the stones, standardization and randomness in selecting the treatment regimens for ureteral calculi are lacking. Therefore, selecting the diagnostic and therapeutic plan is vital for improving treatment efficacy. In this review, we summarize the findings of recent domestic and international studies to provide an outline of the progress and current status of ureteral calculi treatment from aspects such as pharmacotherapy, surgery, and minimally invasive treatment to provide a basis for treating this disease in clinical settings.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN: The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS: Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS: We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

A systematic literature review on meta-heuristic based feature selection techniques for text classification.

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

A new integrated rough multi-criteria decision-making model for enterprise resource planning software selection.

Enterprise resource planning (ERP) is widely used to boost the total market power of businesses. The wrong selection is one of the key reasons why ERP installations fail. Due to the complexity of the business environment and the range of ERP systems, choosing an ERP system is a complex and time-consuming procedure. ERP alternatives may be assessed using several criteria, so the ERP selection process may be considered a multi-criteria decision-making (MCDM) problem. In this study, the rough best worst method (BWM) was used to determine criteria weights, while the newly developed rough integrated simple weighted sum product (WISP) was used to rank ERP alternatives. Results suggest that the SFT-4 coded software is regarded as the best option, followed by SFT-5, SFT-6, SFT-2, SFT-3, and SFT-1. Results of the newly developed rough WISP method are compared to those of existing rough techniques in the sensitivity analysis. The differences between them have been found to be negligible. The outcomes show how effectively developed rough BWM and WISP integrated method performs in terms of ERP selection with usability, accuracy, ease of use, and consistency. This study will help decision-makers in a context where ERP is implemented choose the best ERP software for different sectors.

Connect attack in IoT-WSN detect through cyclic analysis based on forward and backward elimination.

IoT-wireless sensor networks (WSN) have extensive applications in diverse fields such as battlegrounds, commercial sectors, habitat monitoring, buildings, smart homes, and traffic surveillance. WSNs are susceptible to various types of attacks, such as malicious attacks, false data injection attacks, traffic attacks, and HTTP flood attacks. CONNECT attack is a novel attack in WSN. CONNECT attack plays a crucial role through disrupting packet transmission and node connections and significantly impacts CPU performance. Detecting and preventing CONNECT attacks is imperative for enhancing WSN efficiency. During a CONNECT attack, nodes fail to respond to legitimate requests, resulting in connectivity delays, acknowledgment delays, and packet drop attacks in IoT-WSN nodes. This article introduces an Intrusion Detection Algorithm based on the Cyclic Analysis Method (CAM), which incorporates a forward selection approach and backward elimination method. CAM analyzes routing information and behavior within the WSN, facilitating the identification of malicious paths and nodes. The proposed approach aims to pinpoint and mitigate the risks associated with CONNECT attacks, emphasizing the identification of malevolent pathways and nodes while establishing multiple disjoint loop-free routes for seamless data delivery in the IoT-WSN. Furthermore, the performance of CAM is assessed based on metrics such as malicious node detection accuracy, connectivity, packet loss, and network traffic. Simulation results using Matlab software demonstrate superior accuracy in malicious node detection, achieving accuracy in attack detection of approximately 99%, surpassing traditional algorithms accuracy of attack detection.

Adaptive multi-source domain collaborative fine-tuning for transfer learning.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning (AMCF) to address this issue. AMCF utilizes multiple source domain models for collaborative fine-tuning, thereby improving the feature extraction capability of model in the target task. Specifically, AMCF employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models, aiming to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of AMCF, it is applied to seven public visual classification datasets commonly used in transfer learning, and compared with the most widely used single-source domain fine-tuning methods. Experimental results demonstrate that, in comparison with the existing fine-tuning methods, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, thereby significantly improving the fine-tuning performance.

Developmental perturbation in human embryos: Clinical and biological significance learned from time-lapse images.

Background: Time-lapse technology (TLT) has gained widespread adoption worldwide. In addition to facilitating the undisturbed culture of embryos, TLT offers the unique capability of continuously monitoring embryos to detect spatiotemporal changes. Although these observed phenomena play a role in optimal embryo selection/deselection, the clinical advantages of introducing TLT remain unclear. However, manual annotation of embryo perturbation could facilitate a comprehensive assessment of developmental competence. This process requires a thorough understanding of embryo observation and the biological significance associated with developmental dogma and variation. This review elucidates the typical behavior and variation of each phenomenon, exploring their clinical significance and research perspectives. Methods: The MEDLINE database was searched using PubMed for peer-reviewed English-language original articles concerning human embryo development. Main findings: TLT allows the observation of consecutive changes in embryo morphology, serving as potential biomarkers for embryo assessment. In assisted reproductive technology laboratories, several phenomena have not revealed their mechanism, posing difficulties such as fertilization deficiency and morula arrest. Conclusion: A profound understanding of the biological mechanisms and significance of each phenomenon is crucial. Further collaborative efforts between the clinical and molecular fields following translational studies are required to advance embryonic outcomes and assessment.

Integrating fNIRS and machine learning: shedding light on Parkinson's disease detection.

The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson's disease (PD) by classifying functional near-infrared spectroscopy (fNIRS) studies as PD positive or negative. fNIRS is a non-invasive optical signal modality that conveys the brain's hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for classification. A set of 792 temporal and spectral features were extracted from each participant's fNIRS study. In the two best performing implementations, an ensemble of feature-ranking techniques was used to select a reduced feature subset, which was subsequently reduced with a genetic algorithm. Achieving optimal detection performance, our approach reached 100 % accuracy, precision, and recall, with an F1 score and area under the curve (AUC) of 1, using 14 features. This significantly advances PD diagnosis, highlighting the potential of integrating fNIRS and machine learning for non-invasive PD detection.

Fluctuating selection in a Monkeyflower hybrid zone.

While hybridization was viewed as a hindrance to adaptation and speciation by early evolutionary biologists, recent studies have demonstrated the importance of hybridization in facilitating evolutionary processes. However, it is still not well-known what role spatial and temporal variation in natural selection play in the maintenance of naturally occurring hybrid zones. To identify whether hybridization is adaptive between two closely related monkeyflower species, Mimulus guttatus and Mimulus laciniatus, we performed repeated reciprocal transplants between natural hybrid and pure species' populations. We planted parental genotypes along with multiple experimental hybrid generations in a dry (2021) and extremely wet (2023) year in the Sierra Nevada, CA. By taking fine scale environmental measurements, we found that the environment of the hybrid zone is more similar to M. laciniatus's seasonally dry rocky outcrop habitat than M. guttatus's moist meadows. In our transplants hybridization does not appear to be maintained by a consistent fitness advantage of hybrids over parental species in hybrid zones, but rather a lack of strong selection against hybrids. We also found higher fitness of the drought adapted species, M. laciniatus, than M. guttatus in both species' habitats, as well as phenotypic selection for M. laciniatus-like traits in the hybrid habitat in the dry year of our experiment. These findings suggest that in this system hybridization might function to introduce drought-adapted traits and genes from M. laciniatus into M. guttatus, specifically in years with limited soil moisture. However, we also find evidence of genetic incompatibilities in second generation hybrids in the wetter year, which may balance a selective advantage of M. laciniatus introgression. Therefore, we find that hybridization in this system is both potentially adaptive and costly, and that the interaction of positive and negative selection likely determines patterns of gene flow between these Mimulus species.

Genetic analysis of the circumsporozoite gene in Plasmodium falciparum isolates from Cameroon: Implications for efficacy and deployment of RTS,S/AS01 vaccine.

Current understanding of genetic polymorphisms and natural selection in Plasmodium falciparum circumsporozoite (PfCSP), the leading malaria vaccine, is crucial for the development of next-generation vaccines, and such data is lacking in Africa. Blood samples were collected among Plasmodium-infected individuals living in four Cameroonian areas (Douala, Maroua, Mayo-Oulo, Pette). DNA samples were amplified using nested PCR protocols, sequenced, and BLASTed. Single nucleotide polymorphisms (SNPs) were analysed in each PfCSP region, and their impact on PfCSP function/structure was predicted in silico. The N-terminal region showed a limited polymorphism with four haplotypes, and three novel SNPs (N68Y, R87W, K93E) were found. Thirty-five haplotypes were identified in the central region, with several variants (e.g., NVNP and KANP). The C-terminal region was also highly diverse, with 25 haplotypes and eight novel SNPs (N290D, N308I, S312G, K317A, V344I, D356E, E357L, D359Y). Most polymorphic codon sites were mainly observed in the Th2R subregion in isolates from Douala and Pette. The codon site 321 was under episodic positive selection. One novel (E357L) and three known (K322I, G349D, D359Y) SNPs show an impact on function/structure. This study showed extensive genetic diversity with geographical patterns and evidence of the selection of Cameroonian PfCSP central and C-terminal regions.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Positive selection analysis of Cyt proteins from Bacillus thuringiensis: a conservative trend driven by negative (purifying) selection.

Bacillus thuringiensis is a Gram-positive entomopathogenic bacterium that produces different pesticidal proteins: vegetative insecticidal proteins (Vpb1/Vpa2, Vip3, and Vpb4) during vegetative growth and δ-endotoxins (Cry and Cyt) during sporulation, which accumulate into parasporal crystals. Cyt proteins are the smaller subset of δ-endotoxins targeting Diptera species. While Cry and Vip3 proteins undergo positive selection, our analysis suggests that Cyt proteins evolve following a conservative trend driven negative (purifying) selection.

Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved.

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

Medical School Characteristics Associated with Matched Otolaryngology Residency Applicants.

OBJECTIVE: Identify which medical schools produce the most otolaryngology residents, and associated characteristics which may contribute to this productivity. DESIGN: The medical school and residency program of each otolaryngology-matched student was identified. Various characteristics for each medical school and residency were compared in univariate and multivariate analysis after adjusting for class size. Percentage of matched students relative to class size was identified and compared for each geographic region. SETTING: Cross-sectional study of publicly available match data from otomatch.com and otolaryngology residency program websites from 2020-2023. PARTICIPANTS: 1411 students from 174 medical schools matched into 126 otolaryngology residencies were identified. RESULTS: Private medical schools (ß = 0.50, p = 0.03), larger otolaryngology departments (ß = 0.01, p = 0.04), and higher U.S. News and World Report (USNWR) ranking (ß = -0.01, p = 0.02) was associated with a greater percentage of otolaryngology-matched students while schools in the Mountain region were associated with a lower percentage of matched students (ß = -1.08, p = 0.02). A difference in percentage of matched students was observed when comparing across all regions (p < 0.01) but no significant differences were observed between any individual regions. The East North Central Region and the Middle Atlantic regions were more likely to match students from their respective regions compared to the Mountain region (OR: 4.98, 95% CI: 1.18, 21.01; OR: 8.20, 95% CI: 1.92, 34.99, respectively). Additionally, the Mountain region was less likely to match students from their own region compared to the Pacific (OR: 0.21, 95% CI: 0.05, 0.90), South Atlantic (OR: 0.20, 95% CI: 0.05, 0.85), and West South Central (OR: 0.15, 95% CI: 0.03, 0.67) regions. CONCLUSIONS: Medical school characteristics such as private vs public status, size of otolaryngology department, higher USNWR ranking, and geographic region impact the number of otolaryngology-matched students. Applicants should consider the impact of their geographic region when allocating signals during the residency application process.

Fishers (Pekania pennanti) are forest structure specialists when resting and generalists when moving: behavior influences resource selection in a northern Rocky Mountain fisher population.

BACKGROUND: Studies of animal habitat selection are important to identify and preserve the resources species depend on, yet often little attention is paid to how habitat needs vary depending on behavioral state. Fishers (Pekania pennanti) are known to be dependent on large, mature trees for resting and denning, but less is known about their habitat use when foraging or moving within a home range. METHODS: We used GPS locations collected during the energetically costly pre-denning season from 12 female fishers to determine fisher habitat selection during two critical behavioral activities: foraging (moving) or resting, with a focus on response to forest structure related to past forest management actions since this is a primary driver of fisher habitat configuration. We characterized behavior based on high-resolution GPS and collar accelerometer data and modeled fisher selection for these two behaviors within a home range (third-order selection). Additionally, we investigated whether fisher use of elements of forest structure or other important environmental characteristics changed as their availability changed, i.e., a functional response, for each behavior type. RESULTS: We found that fishers exhibited specialist selection when resting and generalist selection when moving, with resting habitat characterized by riparian drainages with dense canopy cover and moving habitat primarily influenced by the presence of mesic montane mixed conifer forest. Fishers were more tolerant of forest openings and other early succession elements when moving than resting. CONCLUSIONS: Our results emphasize the importance of considering the differing habitat needs of animals based on their movement behavior when performing habitat selection analyses. We found that resting fishers are more specialist in their habitat needs, while foraging fishers are more generalist and will tolerate greater forest heterogeneity from past disturbance.

Differential gene expression of salt-tolerant alfalfa in response to salinity and inoculation by Ensifer meliloti.

BACKGROUND: Alfalfa (Medicago sativa L.) experiences many negative effects under salinity stress, which may be mediated by recurrent selection. Salt-tolerant alfalfa may display unique adaptations in association with rhizobium under salt stress. RESULTS: To elucidate inoculation effects on salt-tolerant alfalfa under salt stress, this study leveraged a salt-tolerant alfalfa population selected through two cycles of recurrent selection under high salt stress. After experiencing 120-day salt stress, mRNA was extracted from 8 random genotypes either grown in 0 or 8 dS/m salt stress with or without inoculation by Ensifer meliloti. Results showed 320 and 176 differentially expressed genes (DEGs) modulated in response to salinity stress or inoculation x salinity stress, respectively. Notable results in plants under 8 dS/m stress included upregulation of a key gene involved in the Target of Rapamycin (TOR) signaling pathway with a concomitant decrease in expression of the SNrK pathway. Inoculation of salt-stressed plants stimulated increased transcription of a sulfate-uptake gene as well as upregulation of the Lysine-27-trimethyltransferase (EZH2), Histone 3 (H3), and argonaute (AGO, a component of miRISC silencing complexes) genes related to epigenetic and post-transcriptional gene control. CONCLUSIONS: Salt-tolerant alfalfa may benefit from improved activity of TOR and decreased activity of SNrK1 in salt stress, while inoculation by rhizobiumstimulates production of sulfate uptake- and other unique genes.