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The advancement of lithium-ion batteries (LIBs) towards larger structures is considered the most efficient approach to enhance energy density in clean energy storage systems. However, this advancement poses significant challenges in terms of the filling and wetting processes of battery electrolytes. The intricate interplay between electrode microstructure and electrolyte wetting process still requires further investigation. This study aims to systematically investigate the primary mechanisms influencing electrolyte wetting on porous electrode structures produced through different manufacturing processes. Using advanced X-ray computed tomography, three-dimensional electrode structures are reconstructed, and permeability and capillary action are evaluated as key parameters. It is observed that increasing calendering pressure and active material content reduces electrode porosity, thereby decreasing permeability and penetration rate; however, it simultaneously enhances capillary action. The interplay between these indicators contributes to the complexity of wetting behavior. Incomplete wetting of electrolytes arises from two primary factors elucidated by further simulations: partial closure of pores induced by the calendering process impedes complete wetting, while non-wetting phase gases become trapped within the electrolyte during the wetting process hindering their release and inhibiting full penetration of the electrolyte. These findings have significant implications for designing and optimizing LIBs while offering profound insights for future advancements in battery technology.

Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs.

Tumor mutation burden (TMB), defined as the number of somatic mutations of tumor DNA, is a well-recognized immunotherapy biomarker endorsed by regulatory agencies and pivotal in stratifying patients for clinical decision-making. However, measurement errors can compromise the accuracy of TMB assessments and the reliability of clinical outcomes, introducing bias into statistical inferences and adversely affecting TMB thresholds through cumulative and magnified effects. Given the unavoidable errors with current technologies, it is essential to adopt modeling methods to determine the optimal TMB-positive threshold. Therefore, we proposed a universal framework, TMBocelot, which accounts for pairwise measurement errors in clinical data to stabilize the determination of hierarchical thresholds. TMBocelot utilizes a Bayesian approach based on the stationarity principle of Markov chains to implement an enhanced error control mechanism, utilizing moderately informative priors. Simulations and retrospective data from 438 patients reveal that TMBocelot outperforms conventional methods in terms of accuracy, consistency of parameter estimations, and threshold determination. TMBocelot enables precise and reliable delineation of TMB-positive thresholds, facilitating the implementation of immunotherapy. The source code for TMBocelot is publicly available at https://github.com/YixuanWang1120/TMBocelot.

Background: The purpose of this study was to investigate the protective effect of xylosma congesta extract on kidney injury in hyperuricemic rats. Methods: The rats were fed yeast extract and intraperitoneal injections of potassium oxonate for 3 weeks to establish the hyperuricemia model. And then the rats were treated with allopurinol and different doses of oak extract. The contents of uric acid in urine and serum, creatinine, and urea nitrogen in serum were detected by biochemical methods. TUNEL was used to detect cell apoptosis in renal tissue. The protein expression of TLR4 and NF- kappa B (NF-κB) p65 and the proportion of CD68 and CD206 positive cells in renal tissue were detected by pathological method. Results: The xylosma congesta group showed decreased renal tubular dilatation, decreased renal interstitial inflammatory cell infiltration, decreased serum creatinine content, and decreased apoptotic cell count as compared to the model group. And positive expression of TLR4 and NF-κB decreased with each dose. Additionally, the xylosma congesta groups showed a significant rise in CD206 and a considerable decrease in CD68. Conclusion: The extract from xylosma congesta has the ability to lower serum uric acid and creatinine levels while also providing protection against kidney damage caused by hyperuricemia.

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteristics of cfDNA fragments and their sequencing coverage, researchers can differentiate OCRs from non-OCRs. However, the presence of noise and variability in cfDNA-seq data poses challenges for the training data used in the noise-tolerance learning-based OCR estimation approach, as it contains numerous noisy labels that may impact the accuracy of the results. For current methods of detecting OCRs, they rely on statistical features derived from typical open and closed chromatin regions to determine whether a region is OCR or non-OCR. However, there are some atypical regions that exhibit statistical features that fall between the two categories, making it difficult to classify them definitively as either open or closed chromatin regions (CCRs). These regions should be considered as partially open chromatin regions (pOCRs). In this paper, we present OCRClassifier, a novel framework that combines control charts and machine learning to address the impact of high-proportion noisy labels in the training set and classify the chromatin open states into three classes accurately. Our method comprises two control charts. We first design a robust Hotelling T2 control chart and create new run rules to accurately identify reliable OCRs and CCRs within the initial training set. Then, we exclusively utilize the pure training set consisting of OCRs and CCRs to create and train a sensitized T2 control chart. This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.

Abstract Aims Cardiac dysfunction is commonly observed in patients with subarachnoid haemorrhage (SAH). However, the specific timeline of cardiac remodelling and the underlying mechanisms responsible for this effect following SAH remain unknown. This study aims to explore the impact of SAH on cardiac dysfunction and its potential mechanisms over time. Methods and results In Protocol 1, we investigated cardiac function and potential mechanisms in a Sprague‐Dawley rat model of SAH at six time points (baseline and Days 1, 3, 7, 14, and 28) while exploring the underlying mechanisms. Our assessments included the haemodynamic profile, echocardiography, and the concentrations of plasma biomarkers at various time points post‐SAH. We determined neuropeptide Y (NPY) 1–5 receptor protein expression levels through western blotting. In Protocol 2, we administered an NPY1 receptor antagonist to evaluate the effects of cardiac dysfunction induced by SAH on Day 3. In Protocol 1, SAH gradually provoked cardiac systolic dysfunction during the acute phase, reaching its peak on Day 3 without concurrent alterations in wall thickness. However, no significant changes were observed from Days 14 to 28 compared with Day 0. The changes in cardiac dysfunction were consistent with myocardial injury, inflammatory biomarkers, and NPY levels. SAH resulted in a heightened heart rate and systolic blood pressure, correlating with elevated epinephrine and norepinephrine levels. In Protocol 2, the administration of the NPY1 receptor antagonist effectively ameliorated cardiac dysfunction. Conclusions SAH induces transient cardiac dysfunction in the acute phase, and the underlying mechanisms for this response involve the NPY–NPY1 receptor pathway, otherwise known as catecholamines.

To prepare for the heavy rainfall resulting from future climate change in the upper reaches of the Yangtze River， this paper analyzed daily precipitation data from 687 meteorological stations in the region between 1990 and 2014， as well as simulation results from 24 global climate models （GCMs） provided by the Coupled Model Intercomparison Project Phase 6 （CMIP6）.The spatio-temporal characteristics and uncertainties of the mean annual rainstorm volume， rainstorm days and rainstorm intensity in the upper reaches of the Yangtze River during 2021 -2099 under different Shared Socioeconomic Pathways （SSPs） scenarios are studied.The results show： （1） Compared to the reference period of 1995 -2014， the mean annual volume， days and intensity of rainstorm in the upper reaches of the Yangtze River are projected to increase and strengthen during the whole projection period of 2021 -2099 and the end of the 21st century （2080 -2099） under the SSP1-2.6， SSP2-4.5， and SSP5-8.5 scenarios.The largest increase is observed under the SSP5-8.5 scenario.The predicted direction is consistent and the certainty of the projection among models increases with higher emissions.The distribution of rainstorm volume， days and intensity during 2021 -2099 is similar under SSP1-2.6 and SSP2-4.5 scenarios， but differs from that of the SSP5-8.5 scenario.By the end of the 21st century， the distribution of volume and days of rainstorm is similar under the three scenarios.However， the distribution of rainstorm intensity under the SSP5-8.5 scenario differs from that under the SSP1-2.6 and SSP2-4.5 scenarios.（2） Relative to the reference period， the rainstorm volume in the upper reaches of the Yangtze River increases by 3.5 mm·（10a）-1， 5.4 mm·（10a）-1 and 14.7 mm·（10a）-1 under SSP1-2.6， SSP2-4.5， SSP5-8.5， respectively.The rainstorm days increases by 0.045 d·（10a）-1， 0.07 d·（10a）-1 and 0.18 d·（10a）-1 under the three scenarios， respectively.The rainstorm intensity increases by 0.37 mm·d-1·（10a）-1， 0.78 mm·d-1·（10a）-1 and 1.94 mm·d-1·（10a）-1 under the three scenarios， respectively.All of the trends pass the 99% confidence test and the same-sign rate test.The period with a high level of prediction certainty is expected to occur in the late 21st century under the SSP5-8.5 scenario.（3） The analysis of the three scenarios indicates an increasing trend in the volume， days and intensity of rainstorm throughout the whole prediction period.The region of southeast Tibet has experienced the highest growth rate in terms of volume and days of rainstorms.The region with the largest increasing trend of rainstorm intensity under the SSP1-2.6 scenario is in northern Sichuan， while under the SSP2-4.5 and SSP5-8.5 scenarios， it is in northern Yunnan.（4） During the early 21st century（2021 -2040）， there is no significant change in the volume， days or intensity of rainstorm in the upper reaches of the Yangtze River， as compared to the reference period， under the three scenarios.While during the middle（2041 -2060）and the end of the 21st century， the volume， days and intensity of rainstorm increase significantly under the SSP2-4.5 and SSP5-8.5 scenarios.This effect is particularly pronounced during the end period of the 21st century under the SSP5-8.5 scenario， as well as the consistency of predicted direction among modes is the highest.

Introduction Although ovarian hyperstimulation syndrome (OHSS) is a common complication primarily seen in patients undergoing in vitro fertilisation-embryo transfer, there is no recognised effective treatment to manage it, especially for pregnant patients. As an alternative non-pharmacological treatment, acupuncture presents itself as a promising, safe and effective intervention for those suffering from OHSS. Despite its potential, there is a noticeable absence of randomised controlled trials providing solid evidence on the efficacy of acupuncture in the management of pregnancy with OHSS. Therefore, the primary objective of this study is to conduct an initial investigation into the efficacy of acupuncture in pregnant patients with OHSS, hoping to contribute valuable insights from a medical-scientific perspective.Methods and analysis This is a three-arm, single-blind, parallel-design, randomised controlled clinical trial. A total of 384 patients will be enrolled and randomised in a ratio of 1:1:1 into acupuncture group (MA group), sham acupuncture group (SA group) and control group (C group) for a 20-day treatment period. The primary outcomes include 24-hour urine output, amount of ascites shown on ultrasound, abdominal circumference, degree of abdominal distension and chest tightness. The secondary outcomes are weight, ovarian size, rate of ascites drainage by tube placement and serum hCG. Besides, the study will consider changes in blood counts and hormonal levels as additional outcomes.Ethics and dissemination This study received authorisation from the Institutional Review Board of the Sixth Affiliated Hospital of Sun Yat-sen University on 4 February 2024 (Approval No. 2024ZSLYFEC-084). Study findings will be disseminated through presentations at scientific conferences or publications in peer-reviewed journals.Trial registration number Chinese Clinical Trial Registry ChiCTR2400081751.

IntroductionPredicting TCR–peptide binding is a complex and significant computational problem in systems immunology. During the past decade, a series of computational methods have been developed for better predicting TCR–peptide binding from amino acid sequences. However, the performance of sequence-based methods appears to have hit a bottleneck. Considering the 3D structures of TCR–peptide complexes, which provide much more information, could potentially lead to better prediction outcomes.MethodsIn this study, we developed TCRcost, a deep learning method, to predict TCR–peptide binding by incorporating 3D structures. TCRcost overcomes two significant challenges: acquiring a sufficient number of high-quality TCR–peptide structures and effectively extracting information from these structures for binding prediction. TCRcost corrects TCR 3D structures generated by protein structure tools, significantly extending the available datasets. The main and side chains of a TCR structure are separately corrected using a long short-term memory (LSTM) model. This approach prevents interference between the chains and accurately extracts interactions among both adjacent and global atoms. A 3D convolutional neural network (CNN) is designed to extract the atomic features relevant to TCR–peptide binding. The spatial features extracted by the 3DCNN are then processed through a fully connected layer to estimate the probability of TCR–peptide binding.ResultsTest results demonstrated that predicting TCR–peptide binding from 3D TCR structures is both efficient and highly accurate with an average accuracy of 0.974 on precise structures. Furthermore, the average accuracy on corrected structures was 0.762, significantly higher than the average accuracy of 0.375 on uncorrected original structures. Additionally, the average root mean square distance (RMSD) to precise structures was significantly reduced from 12.753 Å for predicted structures to 8.785 Å for corrected structures.DiscussionThus, utilizing structural information of TCR–peptide complexes is a promising approach to improve the accuracy of binding predictions.

Lung cancer remains the leading cause of cancer-related mortality globally, with environmental pollutants identified as significant risk factors, especially for nonsmokers. The intersection of these pollutants with epigenetic mechanisms has emerged as a critical area of interest for understanding the etiology and progression of lung cancer. Epigenetic changes, including DNA methylation, histone modifications, and non-coding RNAs, can induce alterations in gene expression without affecting the DNA sequence and are influenced by environmental factors, contributing to the transformation of normal cells into malignant cells. This review assessed the literature on the influence of environmental pollutants on lung cancer epigenetics. A comprehensive search across databases such as PubMed, Web of Science, Cochrane Library, and Embase yielded 3,254 publications, with 22 high-quality papers included for in-depth analysis. These studies demonstrated the role of epigenetic markers, such as DNA methylation patterns of genes like F2RL3 and AHRR and alterations in the miRNA expression profiles, as potential biomarkers for lung cancer diagnosis and treatment. The review highlights the need to expand research beyond homogenous adult male groups typically found in high-risk occupational environments to broader population demographics. Such diversification can reduce biases and enhance the relevance of findings to various clinical contexts, fostering the development of personalized preventive and therapeutic measures. In conclusion, our findings underscore the potential of innovative epigenetic therapies, such as DNA demethylating drugs and histone modification agents, to counter environmental toxins’ carcinogenic effects. The growing interest in miRNA therapies and studies aiming to correct aberrant methylation patterns indicate significant strides toward better lung cancer management and a healthier future for global communities.

This study summarizes the significance of microRNA-134 (miRNA-134) in the pathophysiology, diagnosis, and treatment of depression, a disease still under investigation due to its complexity. miRNA-134 is an endogenous short non-coding RNA that can bind to the 3′ untranslated region (3′UTR) of miRNA-134, inhibiting gene translation and showing great potential in the regulation of mood, synaptic plasticity, and neuronal function. This study included 15 articles retrieved from four English-language databases: PubMed, Embase, The Cochrane Library, and Web of Science, and three Chinese literature databases: CNKI, Wanfang, and Chinese Science and Technology Periodical Database (VIP).We evaluated each of the 15 articles using the Critical Appraisal Skills Program (CASP) tool.The standard integrates analyzes of genomic, transcriptomic, neuroimaging, and behavioral data analyses related to miRNA-134 and depression. A multidimensional framework based on standardized criteria was used for quality assessment. The main findings indicate that miRNA-134 significantly affects synaptic plasticity and neurotransmitter regulation, in particular the synthesis and release of serotonin and dopamine. miRNA-134 shows high sensitivity and specificity as a biomarker for the diagnosis of depression and has therapeutic potential for the targeted treatment of depression. miRNA-134 plays a crucial role in the pathogenesis of depression, providing valuable insights for early diagnosis and the development of targeted therapeutic strategies. This work highlights the potential of miRNA-134 as a focal point for advancing personalized medicine approaches for depression.

Rotaviruses (RV) are a major cause of severe gastroenteritis, particularly in neonatal piglets. Despite the availability of effective vaccines, the development of antiviral therapies for RV remains an ongoing challenge. Retinoic acid (RA), a metabolite of vitamin A, has been shown to have anti-oxidative and antiviral properties. However, the mechanism by which RA exerts its intestinal-protective and antiviral effects on RV infection is not fully understood. The study investigates the effects of RA supplementation in Duroc × Landrace × Yorkshire (DLY) piglets challenged with RV. Thirty-six DLY piglets were assigned into six treatments, including a control group, RA treatment group with two concentration gradients (5 and 15 mg/d), RV treatment group, and RV treatment group with the addition of different concentration gradients of RA (5 and 15 mg/d). Our study revealed that RV infection led to extensive intestinal architecture damage, which was mitigated by RA treatment at lower concentrations by increasing the villus height and villus height/crypt depth ratio (P

The coordinated optimization of maintenance and generation schedules of multiple offshore wind farms (OWFs) bring great benefits in reducing the maintenance and operation cost of OWF cluster. Firstly, a two-stage coordinated risk-averse distributionally robust optimization (CRDRO) model for maintenance and generation schedules of wind turbines (WTs) in OWF cluster is proposed. This model takes into account uncertainties related to the prediction errors in WTs’ maximum available active power outputs, the travel time of maintenance vessels, and the high operational cost risks associated with these uncertainties. A feasible route generating algorithm is designed to obtain all feasible maintenance combinations for WTs and the corresponding routes of maintenance vessels for the CRDRO model. The risk-averse term, which is a weighted sum of expected operation costs and GlueVaR value, is used to measure the tail risk of the worst probability distribution. To solve the two-stage CRDRO model, the risk-averse term is simplified into the form of expected value calculation, which can be further transformed into the tractable form by using the strong duality theorem. Finally, the two-stage CRDRO model is solved by the column-and-constraint generation algorithm, with the nonconvex sub-problem being addressed by the outer approximation algorithm. Case studies on an real-world cluster comprising three OWFs verify the correctness and efficiency of the proposed method, and demonstrate that the CRDRO model enhances the overall economic efficiency and reduces the high operational cost risk under extreme scenarios.

Immune inflammation is one of the main factors in the pathogenesis of depression. It is an effective and active way to find more safe and effective anti-inflammatory depressant drugs from plant drugs. The purpose of this study is to explore the potential of marine plant Sargassum pallidum (Turn).C.Ag. (Haihaozi, HHZ) in the prevention and treatment of depression and to explain the related mechanism. Phytochemical analysis showed that alkaloids, terpenes, and organic acids are the main constituents. In vitro and in vivo activity studies showed the anti-neuroinflammatory and antidepressant effect of Sargassum pallidum, furthermore, confirmed that 7-Hydroxycoumarin, Scoparone, and Kaurenoic Acid are important plant metabolites in Sargasum pallidum for anti-neuroinflammation. Mechanism exploration showed that inhibition of ERK1/2/p38 inflammatory signaling pathway contributing to the antidepressant effect of Sargassum pallidum in reducing intestinal inflammatory levels. This study confirmed the value of Sargassum pallidum and its rich plant metabolites in anti-inflammatory depression, providing a new choice for the follow-up research and development of antidepressant drugs.

Abstract Extracellular vesicles (EVs) have emerged as promising biomaterials for the treatment of different disease. However, only handful types of EVs with clinical transformation potential have been reported to date, and their preparation on a large scale under biosafety‐controlled conditions is limited. In this study, we characterize a novel type of EV with promising clinical application potential: dehydration‐induced extracellular vesicles (DIMVs). DIMV is a type of micron‐diameter cell vesicle that contains more bioactive molecules, such as proteins and RNA, but not DNA, than previously reported cell vesicles. The preparation of DIMV is extraordinarily straightforward, which possesses a high level of biosafety, and the protein utilization ratio is roughly 600 times greater than that of naturally secreted EVs. Additional experiments demonstrate the viability of pre‐ or post‐isolation DIMV modification, including gene editing, nucleic acid encapsulation or surface anchoring, size adjustment. Finally, on animal models, we directly show the biosafety and immunogenicity of DIMV, and investigate its potential application as tumour vaccine or drug carrier in cancer treatment.

Serological pattern of simultaneous positivity for hepatitis B surface antigen (HBsAg) and antibody against HBsAg (anti-HBs) is considered a specific and atypical phenomenon among patients with chronic hepatitis B virus (HBV) infection, especially in pediatric patients. Unfortunately, there is limited understanding of the clinical and virological characteristics among children having chronic HBV infection and the coexistence of HBsAg and anti-HBs. Hence, our objective was to determine the prevalence of coexistent HBsAg and anti-HBs and to explore the associated clinical and virological features in this patient population. The researchers conducted a retrospective cohort study on the 413 pediatric patients with chronic HBV infection from December 2011 to June 2022. The patients were stratified into two groups based on their anti-HBs status. Demographic, serum biochemical and virological parameters of two group were compared. Of the total 413 enrolled subjects, 94 (22.8%) were tested positive for both HBsAg and anti-HBs. Patients with anti-HBs were younger and demonstrated significantly higher ratio of albumin to globulin (A/G), elevated serum levels of alanine transaminase (ALT), lower ratio of aspartate transaminase (AST)/ALT (AST/ALT) and reduced serum levels of globulin, HBsAg and HBV DNA, Additionally, these patients were more likely to show coexistent HBeAg and anti-HBe when compared to patients without anti-HBs. The results of multivariate logistical analysis revealed that AST/ALT, serum levels of globulin and HBsAg were negatively associated with coexistence of HBsAg and anti-HBs. Our data demonstrated a considerable prevalence of coexisting HBsAg and anti-HBs in pediatric patients. Children with this specific serological pattern were commonly of a younger age, seemly predisposing them to early liver impairment and lower HBV replication activity.

Abstract Background The coexistence of hepatitis B surface antigen (HBsAg) and hepatitis B surface antibody (HBsAb) represents an uncommon serological pattern observed in patients with hepatitis B virus (HBV) infection, and its underlying mechanism and clinical significance have not been well established. The aim of this study was to investigate the association between this serological profile and clinical treatment outcomes in children with chronic hepatitis B (CHB). Methods This retrospective cohort study included 372 treatment-naïve CHB children from the Hunan Children’s Hospital. The participants were categorized into HBsAb-positive group and HBsAb-negative group. The associations between HBsAb positive status to clinical outcomes were assessed using Cox proportional hazard regression. Receiver operating characteristic curve was conducted to evaluate the prediction ability in HBsAg loss. Results The coexistence of HBsAg and HBsAb accounted for 23.39% (87/372) of the participants. The crude incidence rates of HBsAg loss, hepatitis B e antigen (HBeAg) clearance, and HBV-DNA undetectability were higher in the HBsAb-positive group compared with the HBsAb-negative group (37.46 vs. 17.37, 49.51 vs. 28.66, 92.11 vs. 66.54 per 100 person-years, respectively, all P

IntroductionT cell receptor (TCR) repertoires provide valuable insights into complex human diseases, including cancers. Recent advancements in immune sequencing technology have significantly improved our understanding of TCR repertoire. Some computational methods have been devised to identify cancer-associated TCRs and enable cancer detection using TCR sequencing data. However, the existing methods are often limited by their inadequate consideration of the correlations among TCRs within a repertoire, hindering the identification of crucial TCRs. Additionally, the sparsity of cancer-associated TCR distribution presents a challenge in accurate prediction.MethodsTo address these issues, we presented DeepLION2, an innovative deep multi-instance contrastive learning framework specifically designed to enhance cancer-associated TCR prediction. DeepLION2 leveraged content-based sparse self-attention, focusing on the top k related TCRs for each TCR, to effectively model inter-TCR correlations. Furthermore, it adopted a contrastive learning strategy for bootstrapping parameter updates of the attention matrix, preventing the model from fixating on non-cancer-associated TCRs.ResultsExtensive experimentation on diverse patient cohorts, encompassing over ten cancer types, demonstrated that DeepLION2 significantly outperformed current state-of-the-art methods in terms of accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the curve (AUC). Notably, DeepLION2 achieved impressive AUC values of 0.933, 0.880, and 0.763 on thyroid, lung, and gastrointestinal cancer cohorts, respectively. Furthermore, it effectively identified cancer-associated TCRs along with their key motifs, highlighting the amino acids that play a crucial role in TCR-peptide binding.ConclusionThese compelling results underscore DeepLION2's potential for enhancing cancer detection and facilitating personalized cancer immunotherapy. DeepLION2 is publicly available on GitHub, at https://github.com/Bioinformatics7181/DeepLION2, for academic use only.

We study a reaction-diffusion system which models the pre-mixed isothermal autocatalytic chemical reaction of order m (m > 1) between two chemical species, a reactant A and an auto-catalyst B, A + m B --> (m+1) B, and a linear decay B -->C, where C is an inert product. The special case of m = 2 is the much studied Gray-Scott model, but without feeding. We prove existence of multiple traveling waves which have distinctive number of local maximaor peaks. It shows a new and very distinctive feature of Gray-Scott type of models in generating rich and structurally different traveling pulses than related models in literature such as isothermal autocatalysis without decay, or a bio-reactor model with isothermal autocatalysis of order m + 1 with m-th order of decay.

Abstract Background Programmed cell death-1 (PD-1) inhibitor was proven to be useful for the recurrent/metastatic head and neck squamous carcinoma (R/M HNSCC) patients. Though both PD-1 inhibitor alone and combination with chemotherapy showed some benefit for PFS and OS, the survival outcome was still not satisfactory. Some studies showed the possible benefit for PD-1 inhibitors combination with radiation for head and neck squamous carcinoma, however there was few studies concerned about synergy of concurrent PD-1 inhibitor combination with chemoradiotherapy for R/M HNSCC. So, we aimed to explore the potential effect and toxicity of the concurrent PD-1 inhibitor and chemoradiotherapy for R/M HNSCC. Methods We consecutively enrolled the R/M HNSCC patients treated with concurrent PD-1 inhibitor and chemoradiotherapy from August 2018 to April 2022 in Sichuan Cancer hospital. All the patients received the combination of PD-1 inhibitor and chemotherapy, and followed with synergy of concurrent PD-1 inhibitor and chemoradiotherapy, then maintenance PD-1 inhibitor. ORR and DCR was calculated by immune-related Response Evaluation Criteria in Solid Tumors (irRECIST-1.1), and Common terminology criteria for adverse events (CTCAE-4.0) was used to evaluate the toxicity.The Kaplan–Meier method was used to analyze OS and PFS. Results 40 R/M HNSCC patients were enrolled in our stuty. The median follow up time was 14 months. 22 patients had recurrent disease only, 16 patients had metastatic disease only, and 2 patients had both recurrence and metastasis disease. For the recurrent lesions, 23 patients received a median radiation dose of 64 Gy (range 50–70 Gy). 18 patients received a median dose of 45 Gy (range 30–66 Gy) for metastatic lesions. The median courses of PD-1 inhibitors and chemotherapy were 8 and 5 respectively. After the treatment, the ORR and DCR were 70.0% and 100%. The median OS was 19 months (range 6.3–31.7 months), with 1 and 2-years OS rates of 72.8% and 33.3%. The median PFS was 9 months (range 3.1–14.9 months), with 6 and 12 months PFS rates of 75.5% and 41.4% respectively. The PFS had no statistical significance in PD-L1 negative and positive group (7 vs 12 months, p = 0.059). The most common grade 3 or 4 adverse events(AE) were leucopenia (25.0%), neutropenia (17.5%), anemia (10.0%), thrombocytopenia (5.0%), hyponatremia (2.5%), and pneumonia(2.5%). No grade 5 AE was observed. Conclusions The synergy of concurrent PD-1 inhibitor treatment with chemoradiotherapy shows promise as a treatment strategy and an acceptable toxicity for the R/M HNSCC patients.

The present study is committed to exploring the intergranular crack performance of magnesium (Mg) bicrystals with typical twin boundaries (TBs) by molecular dynamics simulations coupled with finite element method. Atomic modeling is conducted to determine the traction–separation (T–S) law in a cohesive zone. Importantly, the T–S curves together with microstructure evolutions are employed to investigate the plastic response during crack propagating. Afterwards, the obtained T–S parameters are embedded into cohesive elements along grain boundaries of polycrystalline structure, which is efficiently characterized by the Voronoi tessellation. As consequence, the simulation of intergranular fracture in Mg bicrystal is successfully realized by finite element analysis with failure criteria. Eventually, the critical stress intensity factors of compact tension specimens with various TBs are predicted availably. The results demonstrate that the crack propagation is strongly sensitive to twin boundary and resultant plastic deformation behavior at the crack tip. Moreover, it is found that microcracks are produced when TBs interact with dislocations. A comparison between simulated results and published experimental data is provided to highlight the reliability of the multiscale method for studying the cracking performance.

Land surface moisture content and energy budget are significantly affected by the soil freeze-thaw process.From August 2017 to July 2018， soil temperature， soil moisture， eddy data from observation and the simulation data of Community Land Model 5.0 （CLM5.0） of Maqu were used to analyze the time evolution characteristics of soil temperature and moisture， radiation and energy flux in soil freeze-thaw process， and to explore the characteristics of surface water and heat exchange during freezing and thawing.The observed data of radiation and energy flux are replaced by simulated data during the freezing process.The results show that： （1） Soil freeze-thaw process includes four stages： the freezing， completely frozen， thawing， and completely thawed stages.Soil temperature， soil moisture， radiation and energy flux have obvious diurnal changes at each stages.In the freezing and thawing stages， the soil moisture change with soil temperature shows an clear diurnal freeze-thaw cycles.（2） The water phase change in the soil during freezing and thawing changes the nature of the underlying surface， and affects the surface radiation budget.The phase transition of water affects the surface latent heat flux， in the completely thawed stage， the surface energy exchange is dominated by latent heat flux.The surface energy exchange is dominated by sensible heat flux in the completely freezed stage.Compared to freezing stages， which surface energy exchange is dominated by latent heat flux， the net radiation gradually increase in thawing stage， leading to sensible heat flux increase.The soil water melts during the day and freezes at night， resulting in frequent release of latent heat flux， affecting the surface energy exchange.The absolute value of soil heat flux in freezing （G0=-9.1 W·m-2） and thawing stage （G0=3.4 W·m-2） is greater than that in completely thawed stage （G0=1.2 W·m-2）.Daily freeze-thaw cycle strengthens surface heat flux.（3） The energy closure rate is the ratio of the sum of sensible heat and latent heat flux to the difference between net radiation flux and soil heat flux.The average energy closure rates of freezing process， completely frozen， thawing process and completely thawed stages were 1.44， 1.59， 0.99 and 0.81.The energy closure rate in thawing and completely thawed stages is close to 1.The daily freeze-thaw cycle affects the soil water phase trasition.Heat released by soil water freezing， resulting in overestimation （underestimation） of soil heat flux and overestimation of energy closure rate.Heat absorbed by soil water thawing， resulting in underestimation of soil heat flux and underestimation of energy closure rate， thus the freeze-thaw process affecting the balance of surface energy budget.

Abstract The service life of large battery packs can be significantly influenced by only one or two abnormal cells with faster aging rates. However, the early‐stage identification of lifetime abnormality is challenging due to the low abnormal rate and imperceptible initial performance deviations. This work proposes a lifetime abnormality detection method for batteries based on few‐shot learning and using only the first‐cycle aging data. Verified with the largest known dataset with 215 commercial lithium‐ion batteries, the method can identify all abnormal batteries, with a false alarm rate of only 3.8%. It is also found that any capacity and resistance‐based approach can easily fail to screen out a large proportion of the abnormal batteries, which should be given enough attention. This work highlights the opportunities to diagnose lifetime abnormalities via “big data” analysis, without requiring additional experimental effort or battery sensors, thereby leading to extended battery life, increased cost‐benefit, and improved environmental friendliness.

Obstacles in alpine environments pose significant challenges to aircraft safety during terminal operations. Key challenges include constraints from obstacles within the terminal clearance area and the labor-intensive manual calculations of flight procedures. The focal point of concern lies in the design of approach procedures, particularly due to the heightened risk of collisions with obstacles during the descent segment in such terrain. To address these challenges, initially, this paper proposes processing the terrain data and visualizing and extracting the topographic data of the alpine airport by adopting a bi-cubic b-spline interpolation and cellular automatic machine model. Then, the paper proposes improving the A* path algorithm to make sure it can obey the standards of flight procedure design, utilizing the improved A* path algorithm to design approach procedures. As fuel consumption is directly connected with the economy of aviation companies, this research finally suggests employing the fuel consumption evaluation model to select the most efficient approach flight procedures. This research takes a case study of a Yunnan airport and simulates and designs the optimized approach procedures by A* path algorithm and evaluation based on fuel consumption. Results indicate that the parameters of optimized approach procedures align with the regulation of flight procedure design and meet the requirements of real flight operation. Therefore, the core tenant of this research can provide a feasible idea for flight procedures with alpine airports and has the potential to reduce workload and enhance operational efficiency.

Thunderstorms are recognized as perilous meteorological phenomena characterized by irregular and nonlinear movement, posing significant risks to approaching aircraft and necessitating technical methods to ensure safety to the aviation operations. This research specifically addresses the challenges associated with aircraft during the approach segment and introduces a multialgorithmic model focusing on the optimization of 4D approach trajectory. Firstly, the artificial neural network intelligent model was used to predict the thunderstorm movement track. Secondly, the multialgorithmic model combined by the rapidly exploring random tree with artificial potential field was built to plan the trajectory of the approaching aircraft under thunderstorm weather, and then, the mean filter was adopted to smooth the simulated approaching trajectory. Finally, the reliability of the model with a real case study was demonstrated. After optimized simulation by predicting the thunderstorm weather and trajectory-optimized multialgorithmic model mentioned above, the approach trajectory can be outputted successfully, but with some distortions, postprocessing with the mean filter results in a remarkably smooth approach trajectory, providing enhanced feasibility and efficiency for pilots navigating through thunderstorm weather conditions. It is ultimately proved that refined 4D trajectory operations under hazardous weather conditions hold substantial significance in advancing aviation safety and operational effectiveness.

ABSTRACT Bacteria adapt to nutrient availability by regulating the synthesis of enzymes. Transcriptome- and multi-sugar growth studies suggest that Lactococcus cremoris represses genes involved in the catabolization of lower growth rate-supporting (lower quality) sugars in a hierarchical order. Furthermore, L. cremoris appears to always express genes involved in the catabolization of higher growth rate-supporting sugars (higher quality) relative to the sugar it is growing on. Here, we unraveled the sugar catabolization hierarchy by determining the sugar catabolizing capacity and the proteome of cells exponentially growing on glucose (µmax = 0.72 h−1), lactose (µmax = 0.6 h−1), galactose (µmax = 0.44 h−1), or maltose (µmax = 0.43 h−1). We found that L. cremoris can grow on 14 of the 96 sugars in a Biolog plate, with µmax ranging from 0.32 to 0.72 h−1. Proteome and catabolization rate measurements show that L. cremoris consistently prepares for the catabolization of higher-quality sugars, except trehalose. While cells were not prepared for the catabolization of most lower-quality sugars, some proteins related to fructose and lactose consumption were always present. Moreover, reducing the growth rate of glucose through salt stress had only a minor influence on the sugars that L. cremoris could catabolize. These findings demonstrate that the catabolization hierarchy is not strictly linked to absolute growth rate or sugar quality. Cells instantly catabolizing a higher-quality sugar require enhanced expression of ribosomal and nucleotide metabolism functions for growth rate maximization, whereas transitioning to lower-quality sugars requires enhanced synthesis of proteins related to arginine catabolism and mixed acid fermentation, besides sugar-specific catabolic proteins. IMPORTANCE The availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate—typically growth on glucose—is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of “slower” sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

Cancer is a heterogeneous disease mainly driven by abnormal gene perturbations in regulatory networks. Therefore, it is appealing to identify the common and specific perturbed genes from multiple cancer networks. We developed an integrative network medicine approach to identify novel biomarkers and investigate drug repurposing across cancer types. We used a network-based method to prioritize genes in cancer-specific networks reconstructed using human transcriptome and interactome data. The prioritized genes show extensive perturbation and strong regulatory interaction with other highly perturbed genes, suggesting their vital contribution to tumorigenesis and tumor progression, and are therefore regarded as cancer genes. The cancer genes detected show remarkable performances in discriminating tumors from normal tissues and predicting survival times of cancer patients. Finally, we developed a network proximity approach to systematically screen drugs and identified dozens of candidates with repurposable potential in several cancer types. Taken together, we demonstrated the power of the network medicine approach to identify novel biomarkers and repurposable drugs in multiple cancer types. We have also made the data and code freely accessible to ensure reproducibility and reusability of the developed computational workflow.

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

Lithium-ion batteries are widely used in energy storage and new energy electric vehicles due to their superior performance, but the internal short circuit problem of lithium-ion batteries is a safety hazard during usage for energy storage and vehicle battery packs. If it cannot be detected in time, the deepening of the internal short circuit will be accompanied by an increase in heat, which will cause thermal runaway and lead to safety accidents. Diagnosing whether the battery pack has an internal short circuit and quantitatively estimating the short circuit resistance of the battery cell that has the internal short circuit can effectively prevent the occurrence of thermal runaway. This study proposes a quantitative diagnosis algorithm of Internal short circuit (ISC) based on the remaining charge capacity based on the charging curve of the lithium-ion battery module. The simulation and experimental verification of the algorithm are carried out under the conditions of different voltage acquisition accuracies, sampling periods, temperatures, and aging degrees. The results show that the proposed algorithm can accurately and quantitatively diagnose the ISC under certain conditions: (1) For serious ISC of 10 Ω level, high diagnosis accuracy can be obtained even under the conditions of 10 mV acquisition accuracy, 10 s sampling period, and variable temperature. For early ISC of 100 Ω level, the ISC resistance is smaller than the actual value and the diagnosis time is longer. To improve the accuracy and timeliness of early ISC diagnosis, the voltage acquisition accuracy, and sampling frequency should be higher than 1 mV and 1 Hz, respectively. (2) Battery aging will reduce the accuracy of ISC diagnosis, but it has little effect on the 10 Ω level ISC, and the diagnostic error of the ISC resistance is less than 6% even at an extremely low temperature (−20 ℃). The conclusions are of great significance to improve the accuracy of quantitative diagnosis of ISC for lithium-ion batteries.

BackgroundPostpartum depression (PPD) is associated with several psychological and obstetric factors. Hepatitis B virus (HBV) infection has been linked with a high risk of depression, but little is known about the relationship between maternal HBV infection and PPD. We aimed to investigate the association between HBV infection and PPD.MethodsThis retrospective cohort study included 3,808 mothers who gave birth in a hospital in southern China. Self-reported Edinburgh Postnatal Depression Scale (EPDS) was used to assess PPD. Multivariate logistic regression was used to determine whether maternal HBV infection was associated with PPD risk.ResultsOf the 3,808 participants, 11.9% of mothers had PPD at 6 weeks postpartum. Two hundred and seventy-eight (7.3%) and 3,530 (92.7%) were in the HBV and control groups, respectively. Women with HBV infection were more likely to test positive for PPD (14.7 vs.11.7%). The multivariate logistic regression analysis showed that HBV-infected women did not have a significantly higher incidence of PPD (OR = 1.23; 95% CI, 0.82–1.84) than those without HBV infection in the study cohort. Parity and postpartum hemorrhage were found to be associated with PPD. In addition, our study showed that e antigen positivity was not associated with PPD risk (OR = 0.56, 95% CI 0.19–1.63).ConclusionsTo our knowledge, this is the first investigation of the relationship between maternal HBV infection and PPD. In a cohort of women without prior history or family history of mental illness, having HBV infection was not significantly associated with self-reporting of PPD compared to not having HBV infection.

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.

Metal–organic frameworks (MOFs) containing open metal sites are important materials for acetylene (C2H2) adsorption. However, it is inefficient or even impossible to search suitable MOFs by molecular simulation method in nearly infinite MOFs space. Therefore, machine learning (ML) methods are adopted in the material screening and prediction of high-performance MOFs. In this paper, architecture, chemical and structural features are used to analyze the C2H2 adsorption performance of the MOFs. Different ML algorithms are applied to perform classification and regression analysis to the factors affecting material adsorption. By decision tree (DT) algorithm, it is found that only PV, GSA, and Cu-OMS are sufficient to determine the high adsorption of the MOFs. Furthermore, the influence of topology on the performance of MOFs is obtained. Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN), are introduced to analyze the quantitative structure–property relationship (QSPR) between C2H2 adsorption and the features of MOFs. The prediction of the GBDT model is found to have the highest accuracy, with R2 as 0.93 and RMSE as 11.58. In addition, the GBDT model is used for feature analysis, and the contribution of each feature to the performance is obtained, which is of great significance for the design and analysis of MOFs. The successful application of ML to MOFs screening greatly reduce the calculation time and provides important reference for the design and synthesis of new MOFs.

The geological body management of gas storage is essential for the integrity management of Underground Gas Storage (UGS). However, the integrity management of geological body of gas storage is unclear in concept with fuzzy interface and incomplete system at present, and it is also limited to the integrity evaluation of gas reservoir UGS. Therefore, the concept of geological body of salt-cavern gas storage was defined based on the analysis of research results related to the geological body of gas reservoir UGS, and an integrity management system for geological body of salt-cavern gas storage was established. The results indicate that (1) the integrity management system for geological body of gas storage comprises 4 items, which are based on the hazard identification and data analysis and centered on the risk evaluation and integrity evaluation. (2) The risk analysis for geological body of salt-cavern gas storage focuses on the analysis of the 3 indicators, i.e., salt cavern shrinkage, gas leakage and land subsidence. (3) The salt-cavern storage integrity evaluation is composed of 3 major evaluation technologies, i.e., stability evaluation of salt cavern and surface, fault stability evaluation, and integrity evaluation of interbed and caprock. In conclusion, the establishment of the integrity management system for geological body of salt-cavern storage complements the shortage of geological body management in UGS integrity management, and also provides technical support and new application direction for the safe operation management.

Abstract Soil freeze–thaw alternation is a natural characteristic of the Tibetan Plateau (TP), and plays an important role in surface energy balance and eco‐hydrological processes. The soil freeze–thaw process on the TP has changed significantly owing to global warming, affecting the alpine ecosystem structure and function. This study used high‐resolution atmospheric forcing datasets to drive the Community Land Model version 5.0 (CLM5.0) to simulate the near‐surface soil freeze–thaw status between 1979 and 2020. The simulated results were compared with in situ observations, and then the spatiotemporal distribution of the freeze start‐date (FSD), freeze end‐date (FED), freeze duration (FD), and thaw duration (TD) at a depth of 0.1 m were analyzed. The Nash–Sutcliffe efficiency coefficients (NSEs) of FSD, FED, FD, and TD between simulations and in situ observations were 0.77, 0.90, 0.98 and 0.92, and the correlation coefficients of FSD, FED, FD, TD were 0.97, 0.99, 0.99 and 0.98, respectively. The spatial distribution of FSD and TD was characterized by gradually increasing from northwest to southeast while FED and FD exhibited the opposite characteristics. FSD, FED, FD, and TD changed at an area‐mean rate of 1.1, −1.4, −2.5, and 2.5 days decade−1, respectively. This study provides an important reference for analyzing and predicting the changes in near surface soil freeze–thaw status on the TP under the warming climate.

Summary: Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model—a highly nonlinear model with clear physical meanings—with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery’s internal electrochemical characteristics onboard via low-cost embedded sensors.

Hainan province which located in the southernmost part of China is an important special economic zone and experimental zone of free trade.However， the studies of underlying surface impact on weather prediction in Hainan is fewer than other places.To understand the impact of underlying surface on numerical weather prediction， the WRF （Weather Research and Forecasting） model is used to simulate the meteorological field over Hainan in 2013.The comparison between default land surface data （land use， vegetation fraction， terrain， and soil type） and new high-resolution land surface data， i.e.Moderate Resolution Imaging Spectroradiometer （MODIS） land use data in 2013 processed based on IGBP （International geosphere biosphere program） classification standard， vegetation fraction data calculated based on MODIS normalized difference vegetation index （NDVI） in 2013， Shuttle Radar Topography Mission 3 （SRTM3） terrain data and Harmonized World Soil Database （HWSD） soil type data is investigated through numerical sensitivity tests to analyze the impact on meteorological field simulations.The study is divided into four parts.The first part is the introduction； The second part mainly introduces the research area， data sources， model design and evaluation methods； The third part compares the simulation results of using new high-resolution land surface data with the WRF default land surface data and evaluates the simulation results； Then the fourth part is about the result and discussion.The result indicates that using default or new high-resolution land surface data can both simulate the temporal-spatial distribution of regional meteorological fields.Using the new high-resolution land surface data can improve the simulated results of 2 m temperature and 2 m relative humidity， with the Root Mean Square Error （RMSE） reduced by 7.2% （6.5%） and 6.1%（7.7%） in spring （summer）， and the Hit Rate （HR） increased by 3.7%（2.8%） and 3.2%（2.9%） in spring （summer）.The impact of high-resolution land surface on 10 m wind speed simulation seems smaller than that on 2 m temperature and 2 m relative humidity.10 m wind speed error reduces slightly after using high-resolution land surface data， with the RMSE reduced by 3.8%（4.5%） and HR increased by 2.1%（2.9%） in spring （summer）.Compared to other underlying surface data， land use data has more significant impact on near-surface meteorological fields simulation.In general， using the high-resolution land surface data in Hainan can improve the accuracy of WRF model and reduce the simulation error.This study can deepen the understanding of numerical forecast products for forecasters， improve the accuracy of weather forecast， and provide theoretical basis for Seismic Prevention and Disaster Mitigation.

The soil freeze-thaw process has undergone significant changes on the Tibetan Plateau (TP) in the context of global change, resulting in the changes of soil physical and chemical properties, thereby affecting the vegetation phenology and photosynthesis through affecting the utilization capacity of CO _2 and light by vegetation. However, little is known about how soil temperature (ST) and soil moisture (SM) affect the gross primary productivity (GPP) on the TP at different seasons and elevations. In this study, the spatiotemporal variation patterns of GPP, ST, and SM were analyzed based on the Community Land Model version 5.0 (CLM5.0) simulations in order to illustrate the impacts of ST and SM in surface (0–10 cm) and root zone soil (0–100 cm) on GPP between 1979 and 2020. The results showed that the CLM5.0-based GPP and ST were in good agreement with in situ observations. ST, SM and GPP increased at the rates of 0.04 °C a ^−1 , 2.4 × 10 ^−4 mm ^3 mm ^−3 a ^−1 , and 5.36 g C m ^−2 a ^−2 , respectively. SM dominated the variations of GPP in winter (64.3%), while ST almost was the dominant factor in other periods, especially spring (99.9%) and autumn (94.7%). The explanatory power of ST and SM for GPP increased with elevation, especially for ST. The relative contributions of ST and SM to GPP at different time scales in root zone soil were similar to those in surface soil. This study provided a new understanding of how soil freeze-thaw affected GPP changes on the TP in the context of the intensification of warming and humidification.

This study developed an objective identification algorithm for the Southwest Vortex (SWV), the dominant rainstorm system prevalent in Southwest China. Based on the TempestExt package, the newly developed SWV identification algorithm employed a 12 h minimum duration threshold, utilizing the high-resolution ERA5 dataset with 1 h intervals. The results reveal that the algorithm precisely identifies the SWV records documented in the SWV Yearbooks, achieving an optimal balance of high probability of detection and low false alarm rate. In addition, the algorithm can detect the SWVs in advance, offering a significant enhancement to SWV monitoring. Notably, the algorithm-detected SWVs exhibit a strong correlation with rainstorms, with a correlation coefficient of 0.70. From an on-site precipitation perspective, the algorithm-detected SWVs account for 87.6% of the observed rainstorm days. Collectively, this study introduces an updated tool for the SWV identification and underscores the algorithm’s promising potential in improving rainstorm forecasts in Southwest China.

Abstract Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients’ multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

Tumor mutation burden (TMB) is a widely recognized biomarker for predicting the efficacy of immunotherapy. However, its use still remains highly controversial. In this study, we examine the underlying causes of this controversy based on clinical needs. By tracing the source of the TMB errors and analyzing the design philosophy behind variant callers, we identify the conflict between the incompleteness of biostatistics rules and the variety of clinical samples as the critical issue that renders TMB an ambivalent biomarker. A series of experiments were conducted to illustrate the challenges of mutation detection in clinical practice. Additionally, we also discuss potential strategies for overcoming these conflict issues to enable the application of TMB in guiding decision-making in real clinical settings.

Using 1979 -2014 Global Land Data Assimilation System-the Community surface parameter data set， China regional daily observation data grid data set （CN05.1） and ERA-interim reanalyze atmospheric circulation data.To study the relationship between the abnormal soil moisture in the Qinghai-Xizang Plateau in May and the surface heat flux in June and the precipitation change in July.The results show that： （1） From 1979 to 2014， the average soil moisture in the 0~10 cm layer area in May was abnormally high years were： 2000， 2001， 2004， 2005， 2006， 2013； the abnormal low years were： 1994， 1995， 1996， 1998， 1999.Soil moisture increased after 2000.Soil moisture increased after 2000.The soil moisture in the higher Tibetan Plateau， the Three Rivers Source Region， and the southern Tibetan valley is significantly higher than in the lower years.The results passed the 90% confidence test.（2） There is a clear correlation between the abnormality of soil moisture in spring and the distribution of summer precipitation in China.When the soil is wet （dry）， from high latitude to low latitude， the relevant area is "+ - + -" （"- + - +"） banded distribution.（3） The soil is moist in May， and the sensible heat flux in the east and the latent heat flux in the west increase in June.Their combined effect strengthens the convergent circulation of the lower atmosphere and the divergence circulation of the upper layer， so that the East Asia region is controlled by the strong anticyclone circulation above 850 hPa.The 500 hPa and above in the northeast of the plateau is an anticyclone circulation， and the south and west are cyclonic circulations.This circulation field configuration makes the South Asian high pressure move eastward and the Western Pacific subtropical high pressure.（4） In July， the Huanghuai area on the north side of the Pacific High has a vertical upward movement.The warm and moist air flow converges with the dry and cold air flow on the west side of the northeast cold vortex.High-level divergence and low-level convergence are conducive to precipitation in Huanghuai area.The eastern northwestern and northern China regions are controlled by anticyclonic circulation， with strong sinking movement， dry and cold air， and insufficient water vapor transport， which is unfavorable for precipitation.

Different biochars have diverse properties, with ambiguous effects on soil nematodes. This study investigated how aspen sawdust (ABC), bamboo powder (BBC), maize straw (MBC) and peanut-shell biochars (PBC) affected Caenorhabditis elegans via culture assays and RNA-seq analysis. The results showed that biochars derived from different agricultural materials varied significantly in physicochemical properties, and PBC produced more volatile organic compounds (VOCs) to attract C. elegans than ABC, BBC and MBC. Moreover, worms in ABC experienced the worst outcomes, while worms in PBC experienced milder impacts. Nematode body length decreased to 724.6 μm, 784.0 μm and 799.7 μm on average in ABC, BBC and MBC, respectively, compared to the control (1052 μm) and PBC treatments (960 μm). The brood size in ABC, MBC, BBC and PBC decreased 41.1%, 39.4%, 39.2% and 19.1% compared to the control, respectively. Furthermore, the molecular mechanisms of biochar-induced developmental effects on C. elegans were explored. Although several differentially expressed genes (DEGs) were different among the four biochars, worm phenotypic changes were mainly related to col genes (col-129; col-140; col-40; col-184), bli-6, sqt-3, perm-2/4, cdk-8, daf-16 and sod-1/2/5, which are associated with cuticle collagen synthesis, eggshell formation in postembryonic growth and rhythmic processes. Our study suggests that different properties of biochars could be crucial to soil nematodes, as well as the worms' biochemical changes are important for the health in agriculture soil.

The law of the polarization state of Raman fiber amplifier switch in single-core feedback structures is unknown. In this paper, two kinds of optical paths with single-core feedback structures are constructed, namely the Raman amplified Faraday rotating mirror single-core feedback structure (RFA-FRMOP) and the Raman amplified ordinary flat mirror single-core feedback structure (RFA-RMOP). In the process of optical signal amplification involving Raman fiber amplifiers, the polarization dependent gain (PDG) generated by Raman switches is affected by the polarization state, and RFA-FRMOP shows better stability. Surprisingly, polarization analysis of the RFA-FRMOP structure shows that both the ellipticity angle and the long-axis azimuth of the output feedback structure changed less than that of the signal entering the feedback structure. The consistency of the long-axis azimuth variation is the main characteristic of the RFA-FRMOP structure. It is found that the variation of the azimuth of the long axis of the RFA-FRMOP structure is obviously greater than that of the ellipticity. These conclusions provide a theoretical basis for the polarization dependent application of Raman fiber amplifiers in single-core feedback structures.

Objective: The introduction of Bruton’s tyrosine kinase (BTK) inhibitors was a milestone in the treatment of B-cell malignancies in recent years owing to its desired efficacy against chronic lymphocytic leukaemia and small cell lymphocytic lymphoma. However, safety issues have hindered its application in clinical practice. The current study aimed to explore the safety warning signals of BTK inhibitors in a real-world setting using the FDA Adverse Event Reporting System (FAERS) to provide reference for clinical rational drug use.Methods: Owing to the short marketing time of other drugs (zanbrutinib and orelabrutinib), we only analysed ibrutinib and acalabrutinib in this study. All data were obtained from the FAERS database from January 2004 to December 2021. Disproportionality analysis and Bayesian analysis were utilised to detect and assess the adverse event (AE) signals of BTK inhibitors.Results: In total, 43,429 reports of ibrutinib were extracted and 1527 AEs were identified, whereas 1742 reports of acalabrutinib were extracted and 220 AEs were identified by disproportionality analysis and Bayesian analysis. Among reports, males were more prone to develop AEs (58.2% for males vs. 35.6% for females treated with ibrutinib, and 55.9% vs. 31.9%, respectively, for acalabrutinib), and more than 30% of patients that suffered from AEs were over 65 years of age. Subsequently, we investigated the top 20 preferred terms (PTs) associated with the signal strength of ibrutinib and acalabrutinib, and our results identified 25 (13 vs. 12, respectively) novel risk signals. Among the top 20 PTs related to death reports, the terms infectious, pneumonia, pleural effusion, fall, asthenia, diarrhoea, and fatigue were all ranked high for these two BTK inhibitors. Further, cardiac disorders were also an important cause of death with ibrutinib.Conclusion: Patients treated with ibrutinib were more prone to develop AEs than those treated with acalabrutinib. Importantly, infection-related adverse reactions, such as pneumonia and pleural effusion, were the most common risk signals related to high mortality associated with both BTK inhibitors, especially in elderly patients. Moreover, cardiovascular-related adverse reactions, such as atrial fibrillation and cardiac failure, were fatal AEs associated with ibrutinib. Our results provide a rationale for physicians to choose suitable BTK inhibitors for different patients and provide appropriate monitoring to achieve safer therapy and longer survival.

ObjectiveTo systematically evaluate the significance of Fusobacterium nucleatum (Fn) levels the clinicopathological impacts of cancer.MethodsLiterature from Pubmed, Embase, and Web of Science was retrieved to collect all English literatures on the correlation between Fn and cancer, and the quality of literatures collected was assessed based on the Newcastle-Ottawa Quality Assessment Scale. The heterogeneity and sensitivity were detected by Stata 14.0 software, and the correlation between Fn and cancer clinicopathological as the effect variables was assessed according to the odds ratio (OR) and 95% confidence interval (CI). The forest plot was drawn.ResultsA total of 19 articles meeting the inclusion criteria were selected. The incidence of Fn prevalence varied considerably (range: 6.1 to 83.3%) and was greater than 10% in 13 of 19 studies. Compared with those with no/low Fn levels, the high levels of Fn was positively associated with vascular invasion, nerve invasion, depth of invasion, and distant metastasis [vascular invasion: OR = 1.66, 95%CI(1.07, 2.57), I2 = 21.9%, fixed effect model; nerve invasion: OR = 1.36, 95%CI(1.00, 1.84), I2 = 43.1%, fixed effect model; infiltration depth: OR = 1.94, 95%CI(1.20, 3.15), I2 = 67.2%, random effect model; distant metastasis: OR = 1.80, 95%CI(1.23, 2.64), I2 = 3.4%, fixed effect model]. Patients with MLH1 methylation always present a higher Fn levels than those without methylation [OR = 2.53, 95%CI(1.42, 4.53), P = 0.01, I2 = 57.5%, random effect model]. Further, Fn was associatedwith the molecular characteristics of cancers [MSI-H Vs. MSS/MSI-low: OR = 2.92, 95%CI(1.61, 5.32), P = 0.01, I2 = 63.2%, random effect model; High Vs. Low/Negative CIMP: OR = 2.23, 95%CI(1.64, 3.03), P = 0.01, I2 = 64.2%, random effect model; KRAS mutation Vs. wild-type: OR = 1.24, 95%CI(1.04, 1.48), P = 0.02, I2 = 27.0%, fixed effect model; Present Vs. Abscent BRAF mutations: OR = 1.88, 95%CI(1.44, 2.45), P = 0.01, I2 = 24.2%, fixed effect model]. The cancer patients with high levels of Fn often have worse RFS than those with no/low Fn levels[OR = 1.14, 95%CI(0.61, 1.68), P = 0.01, I2 = 80.7%, random effect model].ConclusionThis review and meta-analysis showed that Fn could be used to predict unfavorable prognosis and function as potential prognostic biomarkers in colorectal cancer (CRC). Our data may have implications for targeting Fn to develop strategies for cancer prevention and treatment.

Tumor mutation burden (TMB) is a widely recognized stratification biomarker for predicting the efficacy of immunotherapy; however, the number and universal definition of the categorizing thresholds remain debatable due to the multifaceted nature of efficacy and the imprecision of TMB measurements. We proposed a minimal joint p-value criterion from the perspective of differentiating the comprehensive therapeutic advantages, termed TMBcat, optimized TMB categorization across distinct cancer cohorts and surpassed known benchmarks. The statistical framework applies to multidimensional endpoints and is fault-tolerant to TMB measurement errors. To explore the association between TMB and various immunotherapy outcomes, we performed a retrospective analysis on 78 patients with non-small cell lung cancer and 64 patients with nasopharyngeal carcinomas who underwent anti-PD-(L)1 therapy. The stratification results of TMBcat confirmed that the relationship between TMB and immunotherapy is non-linear, i.e., treatment gains do not inherently increase with higher TMB, and the pattern varies across carcinomas. Thus, multiple TMB classification thresholds could distinguish patient prognosis flexibly. These findings were further validated in an assembled cohort of 943 patients obtained from 11 published studies. In conclusion, our work presents a general criterion and an accessible software package; together, they enable optimal TMB subgrouping. Our study has the potential to yield innovative insights into therapeutic selection and treatment strategies for patients.

Abstract Background The misestimation of surgical risk is a serious threat to the lives of patients when implementing surgical risk calculator. Improving the accuracy of postoperative risk prediction has received much attention and many methods have been proposed to cope with this problem in the past decades. However, those linear approaches are inable to capture the non-linear interactions between risk factors, which have been proved to play an important role in the complex physiology of the human body, and thus may attenuate the performance of surgical risk calculators. Methods In this paper, we presented a new surgical risk calculator based on a non-linear ensemble algorithm named Gradient Boosting Decision Tree (GBDT) model, and explored the corresponding pipeline to support it. In order to improve the practicability of our approach, we designed three different modes to deal with different data situations. Meanwhile, considering that one of the obstacles to clinical acceptance of surgical risk calculators was that the model was too complex to be used in practice, we reduced the number of input risk factors according to the importance of them in GBDT. In addition, we also built some baseline models and similar models to compare with our approach. Results The data we used was three-year clinical data from Surgical Outcome Monitoring and Improvement Program (SOMIP) launched by the Hospital Authority of Hong Kong. In all experiments our approach shows excellent performance, among which the best result of area under curve (AUC), Hosmer–Lemeshow test ( $${{\mathrm{HL}}}_{\hat{c}}$$ HL c ^ ) and brier score (BS) can reach 0.902, 7.398 and 0.047 respectively. After feature reduction, the best result of AUC, $${\mathrm{HL}}_{\hat{c}}$$ HL c ^ and BS of our approach can still be maintained at 0.894, 7.638 and 0.060, respectively. In addition, we also performed multiple groups of comparative experiments. The results show that our approach has a stable advantage in each evaluation indicator. Conclusions The experimental results demonstrate that NL-SRC can not only improve the accuracy of predicting the surgical risk of patients, but also effectively capture important risk factors and their interactions. Meanwhile, it also has excellent performance on the mixed data from multiple surgical fields.