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Endogenous retrovirus loci and induced changes in gene expression in Japanese indigenous chickens
Developing a Primary Care Workforce for Underserved Communities — The UC Davis TEACH Program
Efficient and easy gene expression and genetic variation data analysis and visualization using exvar
Abstract RNA sequencing data manipulation workflows are complex and require various skills and tools. This creates the need for user-friendly and integrated genomic data analysis and visualization tools. We developed a novel R package using multiple Cran and Bioconductor packages to perform gene expression analysis and genetic variant calling from RNA sequencing data. Multiple public datasets were analyzed using the developed package to validate the pipeline for all the supported species. The developed R package, named “exvar”, includes multiple data analysis functions and three data visualization shiny apps integrated as functions. Also, it could be used to analyze several species’ data. The exvar package is available in the project’s GitHub repository (https://github.com/omicscodeathon/exvar).
A Comparison of Peripherally Inserted Central Catheter Materials
Relationship between optical properties and internal quality of Orah Mandarins during storage
Postexercise Facilitation of Reflexes in the Lambert–Eaton Myasthenic Syndrome
Magnitude and determinants of isolated systolic hypertension among type 2 diabetes patients in selected referral hospitals in Amhara Region, Ethiopia
Medium- and Distal-Vessel Occlusion — The Limit of Thrombectomy?
The efficacy of hyaluronic acid treatment on induced periodontitis in rats exposed to gamma radiation
Abstract The periodontium is one of the radiation-sensitive tissues; the periodontal membrane’s vascularity and cellularity were reduced, and the danger of losing periodontal attachment was raised. This study was performed to evaluate the efficacy of hyaluronic acid treatment on induced periodontitis in rats exposed to gamma radiation radiographically and histopathologically. A total number of 30 adult male Albino rats were divided randomly into five groups (n = 6). Group 1 (C): received neither irradiation nor treatment. Group 2 (P): was subjected to induced periodontitis. Group 3 (PT): subjected to induced periodontitis with hyaluronic acid treatment. Group 4 (RP): received a single dose of total cranium irradiation 20 Gy with induced periodontitis. Group 5 (RPT): received a single dose of total cranium irradiation 20 Gy with induced periodontitis and hyaluronic acid treatment. All animals were euthanized, and the outcomes of treatment were evaluated radiographically by cone beam computed tomography (CBCT) and histopathologically. Results: Comparison of the five groups about bone density by one-way ANOVA showed a significant difference among groups (P < 0.001). The highest bone density values were measured in Group (PT) (1245 ± 22.86), while the lowest bone density values were measured in Group (RP) (926 ± 31.47). Using post hoc analysis for pairwise comparisons showed that Group (PT) and Group (RPT) have significantly higher values than Group (P) and Group (RP) (P < 0.001). Histologically, the group (RPT) shows a new formation of irregular connective tissue fibers of the periodontal ligament (PDL) with an area of distortion, fibrous marrow spaces with wide osteocyte lacunae without nuclei, and Haversian canals with empty blood vessels. The radiographic and histopathological findings of using HA as a topical application in rats subjected to induced periodontitis and exposed to gamma radiation revealed enhanced healing ability of the periodontal tissue with restoration of the bone density. Depending on these results, HA could be used as an adjunct local delivery agent for periodontal-affected patients receiving radiotherapy.
Solar cells made of Moon dust could power up a lunar base
CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection
Optimizing capacitor bank placement in distribution networks using a multi-objective particle swarm optimization approach for energy efficiency and cost reduction
The interference of baicalein with uric acid detected by the enzymatic method and its correction method
Campus microenvironmental factors and their effects on people’s outdoor thermal perceptions under different conditions
Enhancing counterfactual detection in multilingual contexts using a few shot clue phrase approach
Abstract This research paper introduces an innovative counterfactual detection system, designed to tackle the complexities of identifying hypothetical statements that describe non-occurring events in diverse fields such as NLP, psychology, medicine, politics, and economics. Counterfactual statements, often encountered in product reviews, pose significant challenges in multilingual contexts due to the linguistic variations, and counterfactual statements are also less frequent in natural language texts. Our proposed system transcends these challenges by using a domain-independent, multilingual few-shot learning model, which significantly improves detection accuracy. Using clues as key innovation, the model demonstrates a 5–10% performance improvement over traditional few-shot techniques. Few-shot learning is a machine learning approach in which a model is trained to make accurate predictions with only a small amount of labeled data, which is particularly beneficial in counterfactual detection where annotated examples are scarce.The system’s efficacy is further validated through extensive testing on multilingual and multidomain datasets, including SemEval2020-Task5, with results underscoring its superior adaptability and robustness in various linguistic scenarios. The incorporation of clue-phrases during training not only addresses the issue of limited data but also significantly boosts the model’s capability in accurately identifying counterfactual statements, thereby offering a more effective solution in this challenging area of natural language processing.