Speed of Sound and Phase Transitions in Neutron Stars Indicated by the Thick Neutron Skin of 208^{208}Pb

The speed of sound is a novel probe of equation of state and phase transitions in dense cores of neutron stars. Recently nuclear experiments extracted a surprising thick neutron skin of $^{208}$Pb, causing tensions to reproduce the tidal deformability in gravitational-wave observations. This work finds that exotic structures in the speed of sound with a small softening slope followed by a steep-rising peak are required to reconcile the thick neutron skin of $^{208}$Pb with astronomical observations of neutron stars. Furthermore, the peak of speed of sound is narrowly constrained around two times the nuclear saturation density with the thick neutron skin. Consequently early and strong first-order phase transitions are comparatively more favorable.Comment: 5 pages 4 figures, submitte

Relationship Between Regional Pectoralis Major Muscle Size and Peak Power During Incline Bench Press Strength Testing: A Pilot Study

The peak power generated by skeletal muscle during strength training is influenced by several factors, including body weight, maximal force, and muscle fiber orientation, as well as the function affecting excitation patterns. However, there is limited research on the relationship between regional muscle size and peak power output. PURPOSE: This pilot study seeks to explore the associations between muscle thickness (MT) and cross-sectional area (CSA) of the pectoralis major and peak power output during an incline bench (IB) press one-repetition maximum (1RM) strength bout. METHODS: Seven participants (males = 3; females = 4; age: 24.3±2.7 years; height: 168.2±3.3 cm; weight 68.71±4.86 kg;) had ultrasound measures completed of the pectoralis major at 10% and 25% of the distal to the supra sternal notch. Following this, participants performed 1RM strength tests on an IB press using a Smith machine set to 45 degrees. The barbell was fitted with a linear velocity transducer to measure bar displacement, bar velocity, and power output during the 1RM session. Ultrasound measures of MT and CSA were collected prior to the IB 1RM strength bout. Spearman’s rho (rs) correlations, and 95% confidence intervals, were performed to assess the relationship between MT and CSA measures and peak power output. RESULTS: The results indicated a strong, negative relationship between 25% MT of the pectoralis major and peak power output (rs=-0.71 [-0.957, 0.113]). However, there were weak, negative associations between peak power and CSA at both 10% (rs=-0.36 [-0.88, 0.56]) and 25% (rs=-0.21, [-0.84, 0.66]. Lastly, there was a negligible, negative relationship between peak power and 10% MT (rs=-0.07, [-0.79, 0.73]). CONCLUSION: These findings suggest that MT at 25% of the pectoralis major may be an important predictor of peak power output during IB 1RM strength testing. Further research is warranted to confirm whether muscle size can predict peak power during 1RM testing. Developing an algorithm or formula using CSA or MT as a predictor could be the next logical step, which would allow for a better understanding of the relationship between muscle characteristics and aid in developing more targeted training programs to optimize performance and reduce injury risk

Trends in forensic microbiology: From classical methods to deep learning

Forensic microbiology has been widely used in the diagnosis of causes and manner of death, identification of individuals, detection of crime locations, and estimation of postmortem interval. However, the traditional method, microbial culture, has low efficiency, high consumption, and a low degree of quantitative analysis. With the development of high-throughput sequencing technology, advanced bioinformatics, and fast-evolving artificial intelligence, numerous machine learning models, such as RF, SVM, ANN, DNN, regression, PLS, ANOSIM, and ANOVA, have been established with the advancement of the microbiome and metagenomic studies. Recently, deep learning models, including the convolutional neural network (CNN) model and CNN-derived models, improve the accuracy of forensic prognosis using object detection techniques in microorganism image analysis. This review summarizes the application and development of forensic microbiology, as well as the research progress of machine learning (ML) and deep learning (DL) based on microbial genome sequencing and microbial images, and provided a future outlook on forensic microbiology

STAT1 as a downstream mediator of ERK signaling contributes to bone cancer pain by regulating MHC II expression in spinal microglia

Major histocompatibility class II (MHC II)-specific activation of CD4+ T helper cells generates specific and persistent adaptive immunity against tumors. Emerging evidence demonstrates that MHC II is also involved in basic pain perception; however, little is known regarding its role in the development of cancer-induced bone pain (CIBP). In this study, we demonstrate that MHC II expression was markedly induced on the spinal microglia of CIBP rats in response to STAT1 phosphorylation. Mechanical allodynia was ameliorated by either pharmacological or genetic inhibition of MHC II upregulation, which was also attenuated by the inhibition of pSTAT1 and pERK but was deteriorated by intrathecal injection of IFNγ. Furthermore, inhibition of ERK signaling decreased the phosphorylation of STAT1, as well as the production of MHC II in vivo and in vitro. These findings suggest that STAT1 contributes to bone cancer pain as a downstream mediator of ERK signaling by regulating MHC II expression in spinal microglia

Iron induces two distinct Ca²⁺ signalling cascades in astrocytes.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-05-01, epub 2021-05-05Publication status: PublishedFunder: National Natural Science Foundation of China (National Science Foundation of China); Grant(s): 81871852Iron is the fundamental element for numerous physiological functions. Plasmalemmal divalent metal ion transporter 1 (DMT1) is responsible for cellular uptake of ferrous (Fe2+), whereas transferrin receptors (TFR) carry transferrin (TF)-bound ferric (Fe3+). In this study we performed detailed analysis of the action of Fe ions on cytoplasmic free calcium ion concentration ([Ca2+]i) in astrocytes. Administration of Fe2+ or Fe3+ in μM concentrations evoked [Ca2+]i in astrocytes in vitro and in vivo. Iron ions trigger increase in [Ca2+]i through two distinct molecular cascades. Uptake of Fe2+ by DMT1 inhibits astroglial Na+-K+-ATPase, which leads to elevation in cytoplasmic Na+ concentration, thus reversing Na+/Ca2+ exchanger and thereby generating Ca2+ influx. Uptake of Fe3+ by TF-TFR stimulates phospholipase C to produce inositol 1,4,5-trisphosphate (InsP3), thus triggering InsP3 receptor-mediated Ca2+ release from endoplasmic reticulum. In summary, these findings reveal the mechanisms of iron-induced astrocytic signalling operational in conditions of iron overload

Cancer-associated fibroblast related gene signature in Helicobacter pylori-based subtypes of gastric carcinoma for prognosis and tumor microenvironment estimation in silico analysis

IntroductionGastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.MethodsHP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.Results and discussionIn this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies

Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning