The Numerical Analysis of Two-Sided Space-Fractional Wave Equation with Improved Moving Least-Square Ritz Method

A numerical analysis of the space-fractional wave equation is carried out by the improved moving least-square Ritz (IMLS-Ritz) method. The trial functions for the space-fractional wave equation are constructed by the IMLS approximation. By the Galerkin weak form, the energy functional is formulated. Employing the Ritz minimization procedure, the final algebraic equations system is obtained. In this numerical analysis, the applicability and efficiency of the IMLS-Ritz method are examined by some example problems. Comparing the numerical results with the analytical solutions, the stability and accuracy of the IMLS-Ritz method are also presented.</jats:p

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems (ITS). According to previous studies, it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy. In order to provide persuasive passenger flow forecast data for ITS, a deep learning model considering the influencing factors is proposed in this paper. In view of the lack of objective analysis on the selection of influencing factors by predecessors, this paper uses analytic hierarchy processes (AHP) and one-way ANOVA analysis to scientifically select the factor of time characteristics, which classifies and gives weight to the hourly passenger flow through Duncan test. Then, combining the time weight, BILSTM based model considering the hourly travel characteristics factors is proposed. The model performance is verified through the inbound passenger flow of Ningbo rail transit. The proposed model is compared with many current mainstream deep learning algorithms, the effectiveness of the BILSTM model considering influencing factors is validated. Through comparison and analysis with various evaluation indicators and other deep learning models, the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968, and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%

Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation

With contributions from the open-source community, a vast amount of instruction tuning (IT) data has emerged. Given the significant resource allocation required by training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data. However, existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. In this paper, we propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). CaR consists of two steps. The first step involves ranking instruction pairs using a scoring model that is well aligned with expert preferences (achieving an accuracy of 84.25%). The second step involves preserving dataset diversity through a clustering process.In our experiment, CaR selected a subset containing only 1.96% of Alpaca's IT data, yet the underlying AlpaCaR model trained on this subset outperforms Alpaca by an average of 32.1% in GPT-4 evaluations. Furthermore, our method utilizes small models (355M parameters) and requires only 11.2% of the monetary cost compared to existing methods, making it easily deployable in industrial scenarios

Marine hydrographic spatial-variability and its cause at the northern margin of the Amery Ice Shelf

Conductivity, temperature and depth(CTD) data collected along a zonal hydrographic section from the northern margin of the Amery Ice Shelf on 25–27 February 2008 by the 24th Chinese National Antarctic Research Expedition (CHINARE) cruise in the 2007/2008 austral summer are analyzed to study thermohaline structures. Analysis reveals warm subsurface water in a limited area around the east end of the northern margin, where the temperature, salinity and density have east-west gradients in the surface layer of the hydrographic section. The localization of the warm subsurface water and the causes of the CTD gradients in the surface layer are discussed. In addition, the results from these CTD data analyses are compared with those from the 22nd CHINARE cruise in the 2005/2006 austral summer. This comparison revealed that the thermoclines and haloclines had deepened and their strengths weakened in the 2007/2008 austral summer. The difference between the two data sets and the cause for it can be reasonably explained and attributed to the change in ocean-ice-atmosphere interactions at the northern margin of the Amery Ice Shelf

TSPAN8 promotes cancer cell stemness via activation of sonic Hedgehog signaling

Cancer stem cells (CSCs) represent a major source of treatment resistance and tumor progression. However, regulation of CSCs stemness is not entirely understood. Here, we report that TSPAN8 expression is upregulated in breast CSCs, promotes the expression of the stemness gene NANOG, OCT4, and ALDHA1, and correlates with therapeutic resistance. Mechanistically, TSPAN8 interacts with PTCH1 and inhibits the degradation of the SHH/PTCH1 complex through recruitment of deubiquitinating enzyme ATXN3. This results in the translocation of SMO to cilia, downstream gene expression, resistance of CSCs to chemotherapeutic agents, and enhances tumor formation in mice. Accordingly, expression levels of TSPAN8, PTCH1, SHH, and ATXN3 are positively correlated in human breast cancer specimens, and high TSPAN8 and ATXN3 expression levels correlate with poor prognosis. These findings reveal a molecular basis of TSPAN8-enhanced Sonic Hedgehog signaling and highlight a role for TSPAN8 in promoting cancer stemness

Diptoindonesin G promotes ERK-mediated nuclear translocation of p-STAT1 (Ser727) and cell differentiation in AML cells.

Exploration of a new differentiation therapy that extends the range of differentiation for treating acute myeloid leukemia (AML) is attractive to researchers and clinicians. Here we report that diptoindonesin G (Dip G), a natural resveratrol aneuploid, exerts antiproliferative activity by inducing G2/M phase arrest and cell differentiation in AML cell lines and primary AML cells. Gene-profiling experiments showed that treating human leukemia HL-60 cells with Dip G was associated with a remarkable upregulation of STAT1 target gene expression, including IFIT3 and CXCL10. Mechanistically, Dip G activated ERK, which caused phosphorylation of STAT1 at Ser727 and selectively enhanced the interaction of p-STAT1 (Ser727) and p-ERK, further promoting their nuclear translocation. The nuclear translocation of p-STAT1 and p-ERK enhanced the transactivation of STAT1-targeted genes in AML cells. Furthermore, in vivo treatment of HL-60 xenografts demonstrated that Dip G significantly inhibited tumor growth and reduced tumor weight by inducing cell differentiation. Taken together, these results shed light on an essential role for ERK-mediated nuclear translocation of p-STAT1 (Ser727) and its full transcriptional activity in Dip G-induced differentiation of AML cells. Furthermore, these results demonstrate that Dip G could be used as a differentiation-inducing agent for AML therapy, particularly for non-acute promyelocytic leukemia therapy

Based on bioinformatics, SESN2 negatively regulates ferroptosis induced by ischemia reperfusion via the System Xc−/GPX4 pathway

IntroductionCerebral ischemia–reperfusion (IR) causes severe secondary brain injury. Previous studies have demonstrated that ferroptosis is involved in IR-induced brain injury. However, whether IR induces ferroptosis in brain microvascular endothelial cells (BMVECs) is not fully understood.Materials and methodsOxygen–glucose deprivation/reoxygenation (OGDR) was performed in bEND.3 cells to mimic IR injury in vitro, and a focal cerebral IR model was created in C57BL/6 mice. Transcriptomic sequencing of the cells was performed first, followed by bioinformatics analysis. Differentially expressed gene (DEG) enrichment analysis highlighted ferroptosis-related pathways.ResultsUsing Venn analysis, nine ferroptosis-related DEGs were identified, namely, Slc3a2, Slc7a11, Ccn2, Tfrc, Atf3, Chac1, Gch1, Lcn2, and Sesn2. Protein–protein interaction (PPI) analysis combined with molecular complex detection (MCODE) identified six hub genes, namely, Ddit3, Atf3, Sesn2, Trib3, Ppp1r15a, and Gadd45a. Spearman’s correlation analysis revealed a significant correlation between the hub genes and ferroptosis-related DEGs. After reperfusion, the levels of ferroptosis indicators were elevated, and the expression of the ferroptosis-related proteins Xc− and GPX4 decreased. SESN2 is a hub gene and key antioxidant regulator. SESN2 silencing reduced the expression of System Xc− and GPX4, whereas overexpression of SESN2 promoted the expression of System Xc− and GPX4.DiscussionThese results suggest that SESN2 is a negative regulator of ferroptosis. Enhancing the expression of SESN2 can alleviate ferroptosis through the activation of the System Xc−/GPX4 pathway. By integrating bioinformatics analysis with mechanistic exploration, this study revealed that ferroptosis plays a crucial role in IR-induced BMVECs injury, with SESN2 acting as a negative regulator via the System Xc−/GPX4 pathway

Cytomegalovirus infection in infants with biliary atresia in China: a multi-center investigation study

Background and objectivesBiliary atresia (BA) with concurrent cytomegalovirus (CMV) is a distinct subtype that is linked to a poorer prognosis. Currently, there are no standardized criteria for the diagnosis or antiviral treatment (AVT) of this condition. It has a high prevalence in China. The aim was to investigate the infection, diagnosis and treatment of CMV infection in infants with BA through a multicenter questionnaire survey conducted in China.MethodsA multicenter investigation was performed through online questionnaire survey. It investigated the diagnosis and treatment of infants with CMV-infected BA in tertiary-level pediatric centers from January 1st, 2018, to January 1st, 2020. The centers were categorized into low and high-volume groups based on number of infants with BA (≤50 or &gt;50) and were also grouped geographically into south and north groups. Afterward, 100 cases were randomly selected from these infants for a retrospective analysis.ResultsA total of 22 questionnaires were collected, and 20 were included in the analysis. The questionnaire survey encompassed 1,276 infants with type III BA. 31.3% of the infants of BA had CMV detected. According to the survey results, a large proportion of centers preferred using CMV-DNA (75.0%) and CMV-IgM (95.0%) as their preferred methods for CMV detection. In the high-volume group, more centers opted for CMV-DNA detection (100.0% vs. 66.7%) and administered AVT (87.5%). In the retrospective analysis of 100 infants with BA, 39 were found to be CMV-positive and among these, 74.4% received AVT.ConclusionAmong the 1,276 infants with BA in this cohort, 31.3% (399 cases) had concomitant CMV infection, representing a decrease compared to previous data. CMV-IgM played a crucial role in the detection of the infection. The retrospective analysis indicated that AVT had a beneficial impact on the prognosis of infants with BA who were infected with CMV