A Study of the Merger History of the Galaxy Group HCG 62 Based on X-Ray Observations and SPH Simulations

We choose the bright compact group HCG 62, which was found to exhibit both excess X-ray emission and high Fe abundance to the southwest of its core, as an example to study the impact of mergers on chemical enrichment in the intragroup medium. We first reanalyze the high-quality Chandra and XMM-Newton archive data to search for the evidence for additional SN II yields, which is expected as a direct result of the possible merger-induced starburst. We reveal that, similar to the Fe abundance, the Mg abundance also shows a high value in both the innermost region and the southwest substructure, forming a high-abundance plateau, meanwhile all the SN Ia and SN II yields show rather flat distributions in $>0.1r_{200}$ in favor of an early enrichment. Then we carry out a series of idealized numerical simulations to model the collision of two initially isolated galaxy groups by using the TreePM-SPH GADGET-3 code. We find that the observed X-ray emission and metal distributions, as well as the relative positions of the two bright central galaxies with reference to the X-ray peak, can be well reproduced in a major merger with a mass ratio of 3 when the merger-induced starburst is assumed. The `best-match' snapshot is pinpointed after the third pericentric passage when the southwest substructure is formed due to gas sloshing. By following the evolution of the simulated merging system, we conclude that the effects of such a major merger on chemical enrichment are mostly restricted within the core region when the final relaxed state is reached.Comment: Accepted for publication in the Astrophysical Journa

SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT

Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for language tasks are too simplistic, and deep architectures have not been fully explored, resulting in a significant performance gap compared to mainstream transformer-based networks such as BERT. To this end, we improve a recently-proposed spiking transformer (i.e., Spikformer) to make it possible to process language tasks and propose a two-stage knowledge distillation method for training it, which combines pre-training by distilling knowledge from BERT with a large collection of unlabelled texts and fine-tuning with task-specific instances via knowledge distillation again from the BERT fine-tuned on the same training examples. Through extensive experimentation, we show that the models trained with our method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve comparable results to BERTs on text classification tasks for both English and Chinese with much less energy consumption

Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index

IntroductionMelanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.MethodsIn this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.ResultsNotably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.DiscussionOur findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types

Establishment and application of a VP3 antigenic domain-based peptide ELISA for the detection of antibody against goose plague virus infection

The detection of antibody against goose plague virus (GPV) infection has never had a commercialized test kit, which has posed challenges to the prevention and control of this disease. In this study, bioinformatics software was used to analyze and predict the dominant antigenic regions of the main protective antigen VP3 of GPV. Three segments of bovine serum albumin (BSA) vector-coupled peptides were synthesized as ELISA coating antigens. Experimental results showed that the VP3-1 (358-392aa) peptide had the best reactivity and specificity. By using the BSA-VP3-1 peptide, a detection method for antibody against GPV infection was established, demonstrating excellent specificity with no cross-reactivity with common infectious goose pathogen antibodies. The intra-batch coefficient of variation and inter-batch coefficient of variation were both less than 7%, indicating good stability and repeatability. The dynamic antibody detection results of gosling vaccines and the testing of 120 clinical immune goose serum samples collectively demonstrate that BSA-VP3-1 peptide ELISA can be used to detect antibody against GPV in the immunized goose population and has higher sensitivity than traditional agar gel precipitation methods. Taken together, the developed peptide-ELISA based on VP3 358-392aa could be useful in laboratory viral diagnosis, routine surveillance in goose farms. The main application of the peptide-ELISA is to monitor the antibody level and vaccine efficacy for GPV, which will help the prevention and control of gosling plague

Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data

IntroductionHumans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size.MethodsThis study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments.ResultsThe results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments.DiscussionThe quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents

Regulation of microglia related neuroinflammation contributes to the protective effect of Gelsevirine on ischemic stroke

Stroke, especially ischemic stroke, is an important cause of neurological morbidity and mortality worldwide. Growing evidence suggests that the immune system plays an intricate function in the pathophysiology of stroke. Gelsevirine (Gs), an alkaloid from Gelsemium elegans, has been proven to decrease inflammation and neuralgia in osteoarthritis previously, but its role in stroke is unknown. In this study, the middle cerebral artery occlusion (MCAO) mice model was used to evaluate the protective effect of Gs on stroke, and the administration of Gs significantly improved infarct volume, Bederson score, neurobiological function, apoptosis of neurons, and inflammation state in vivo. According to the data in vivo and the conditioned medium (CM) stimulated model in vitro, the beneficial effect of Gs came from the downregulation of the over-activity of microglia, such as the generation of inflammatory factors, dysfunction of mitochondria, production of ROS and so on. By RNA-seq analysis and Western-blot analysis, the JAK-STAT signal pathway plays a critical role in the anti-inflammatory effect of Gs. According to the results of molecular docking, inhibition assay, and thermal shift assay, the binding of Gs on JAK2 inhibited the activity of JAK2 which inhibited the over-activity of JAK2 and downregulated the phosphorylation of STAT3. Over-expression of a gain-of-function STAT3 mutation (K392R) abolished the beneficial effects of Gs. So, the downregulation of JAK2-STAT3 signaling pathway by Gs contributed to its anti-inflammatory effect on microglia in stroke. Our study revealed that Gs was benefit to stroke treatment by decreasing neuroinflammation in stroke as a potential drug candidate regulating the JAK2-STAT3 signal pathway

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data