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English writing is conducive to the online communication and communication of language; the current diagnosis system of English writing is difficult to accurately find and diagnose the wrong words, which leads to a low diagnosis rate of wrong words in English writing system. To solve this problem, this paper designs an intelligent diagnosis system for English writing based on data feature extraction and fusion. First of all, B/S architecture is introduced on the basis of the conventional intelligent diagnosis system structure of English writing, which makes up for the problem that the C/S mode is prone to diagnostic errors. Secondly, the features of English lexical data are extracted and fused to provide better input for the diagnostic model, which effectively solves the problems of complex vocabulary and feature redundancy in English writing. The simulation results show that the proposed intelligent diagnosis system for English writing has higher diagnostic accuracy and faster query speed.

Limited DNA end resection is the key to impaired homologous recombination in BRCA1 -mutant cancer cells. Here, using a loss-of-function CRISPR screen, we identify DYNLL1 as an inhibitor of DNA end resection. The loss of DYNLL1 enables DNA end resection and restores homologous recombination in BRCA1 -mutant cells, thereby inducing resistance to platinum drugs and inhibitors of poly(ADP-ribose) polymerase. Low BRCA1 expression correlates with increased chromosomal aberrations in primary ovarian carcinomas, and the junction sequences of somatic structural variants indicate diminished homologous recombination. Concurrent decreases in DYNLL1 expression in carcinomas with low BRCA1 expression reduced genomic alterations and increased homology at lesions. In cells, DYNLL1 limits nucleolytic degradation of DNA ends by associating with the DNA end-resection machinery (MRN complex, BLM helicase and DNA2 endonuclease). In vitro, DYNLL1 binds directly to MRE11 to limit its end-resection activity. Therefore, we infer that DYNLL1 is an important anti-resection factor that influences genomic stability and responses to DNA-damaging chemotherapy. DYNLL1 antagonizes end resection of DNA double-strand breaks, thereby inhibiting homologous repair, and the loss of DYNLL1 correlates with poor progression-free survival of patients with BRCA1 -mutant ovarian cancer.

In the new model of China’s dual-circulation economy, the opening-up and deepening of financial markets have imposed higher requirements on the risk management capacity of financial institutions, with the issue of loan customers losing contact and defaulting becoming an urgent concern. Based on desensitized samples of lost-linking customers (with multidimensional features such as communication behavior and loan qualifications), this study uses the FP-Growth algorithm to systematically mine association rules between loss-of-contact features and three modes: “Hide and Seek”, “Flee with the Money”, and “False Disappearance”, providing effective risk management strategies for financial institutions. Through association rule mining, this study reveals significant correlations between some feature combinations and lost-linking modes. The results reveal substantial variations in correlation strength among different feature combinations and lost-linking modes, and the association strength increases significantly with the prolongation of overdue time. The results provide banks with quantitative early warning signs based on feature combinations, which can be applied to risk-grading monitoring systems. The research emphasizes the requirement for combined analysis of multidimensional features and dynamic monitoring in precise risk control.

Financial development significantly bolsters a country's economic growth and resilience. Despite increasing focus on the relationship between financial development and economic growth, few studies examine the impact of financial development on domestic investment across countries' income levels. Therefore, this study employs the system Generalized Method of Moments estimator and the Pooled Mean Group estimator to investigate this relationship, utilizing a panel of 152 countries from 1980 to 2021. The empirical findings affirm that financial development positively influences investment performance until a specific threshold over time. However, while increasing financial development benefits investment, further deepening the financial sector may eventually diminish its impact on domestic investment. Specifically, the benefit of investment growth remains valid only up to a threshold of 0.5147, beyond which it becomes a hindrance. In the short run, financial development changes do not substantially impact investment. Additionally, the marginal effect of financial development on investment is more pronounced in low- and middle-income countries. These empirical findings provide valuable reference for enhancing financial development to foster investment growth. Investment is pivotal for sustaining long-term economic growth, fostering development, expanding market access, promoting innovation, and reducing transaction costs. This research aims to examine the impact of financial development on domestic investment across countries with varying income levels. Utilizing the system generalized method of moments (GMM) and the pooled mean group (PMG) estimator, the study analyzes a panel of 152 countries from 1980 to 2021. The findings reveal a positive influence of financial development on investment performance, particularly up to a certain threshold in the long run. Importantly, as countries' income levels rise, the significance of financial development on investment performance becomes more pronounced in low- and middle-income countries. However, with the deepening of the financial sector, its effect on domestic investment may eventually diminish. These results underscore the importance of considering the optimal level of financial development to foster investment growth.

In the last decade, perovskite solar cells have made a quantum leap in performance with the efficiency increasing from 3.8% to 25%. However, commercial perovskite solar cells have faced a major impediment due to toxicity and stability issues. Therefore, lead-free inorganic perovskites have been investigated in order to find substitute perovskites which can provide a high efficiency similar to lead-based perovskites. In recent studies, as a kind of lead-free inorganic perovskite material, Cs4CuSb2Cl12 has been demonstrated to possess impressive photoelectric properties and excellent environmental stability. Moreover, Cs4CuSb2Cl12 nanocrystals have smaller effective photo-generated carrier masses than bulk Cs4CuSb2Cl12, which provides excellent carrier mobility. To date, there have been no reports about Cs4CuSb2Cl12 nanocrystals used for making solar cells. To explore the potential of Cs4CuSb2Cl12 nanocrystal solar cells, we propose a lead-free perovskite solar cell with the configuration of FTO/ETL/Cs4CuSb2Cl12 nanocrystals/HTL/Au using a solar cell capacitance simulator. Moreover, we numerically investigate the factors that affect the performance of the Cs4CuSb2Cl12 nanocrystal solar cell with the aim of enhancing its performance. By selecting the appropriate hole transport material, electron transport material, thickness of the absorber layer, doping densities, defect density in the absorber, interface defect densities, and working temperature point, we predict that the Cs4CuSb2Cl12 nanocrystal solar cell with the FTO/TiO2/Cs4CuSb2Cl12 nanocrystals/Cu2O/Au structure can attain a power conversion efficiency of 23.07% at 300 K. Our analysis indicates that Cs4CuSb2Cl12 nanocrystals have great potential as an absorbing layer towards highly efficient lead-free all-inorganic perovskite solar cells.

Background There is no formal diagnostic criterion for sepsis-induced cardiomyopathy (SICM), but left ventricular ejection fraction (LVEF) < 50% was the most commonly used standard. Tissue motion annular displacement (TMAD) is a novel speckle tracking indicator to quickly assess LV longitudinal systolic function. This study aimed to evaluate the feasibility and discriminatory value of TMAD for predicting SICM, as well as prognostic value of TMAD for mortality. Methods We conducted a single-center retrospective observational study in patients with sepsis or septic shock who underwent echocardiography examination within the first 24 h after admission. Basic clinical information and conventional echocardiographic data, including mitral annular plane systolic excursion (MAPSE), were collected. Based on speckle tracking echocardiography (STE), global longitudinal strain (GLS) and TMAD were, respectively, performed offline. The parameters acquisition rate, inter- and intra-observer reliability, time consumed for measurement were assessed for the feasibility analysis. Areas under the receiver operating characteristic curves (AUROC) values were calculated to assess the discriminatory value of TMAD/GLS/MAPSE for predicting SICM, defined as LVEF < 50%. Kaplan–Meier survival curve analysis was performed according to the cutoff values in predicting SICM. Cox proportional hazards model was performed to determine the risk factors for 28d and in-hospital mortality. Results A total of 143 patients were enrolled in this study. Compared with LVEF, GLS or MAPSE, TMAD exhibited the highest parameter acquisition rate, intra- and inter-observer reliability. The mean time for offline analyses with TMAD was significantly shorter than that with LVEF or GLS ( p  < 0.05). According to the AUROC analysis, TMADMid presented an excellent discriminatory value for predicting SICM (AUROC > 0.9). Patients with lower TMADMid (< 9.75 mm) had significantly higher 28d and in-hospital mortality (both p  < 0.05). The multivariate Cox proportional hazards model revealed that BMI and SOFA were the independent risk factors for 28d and in-hospital mortality in sepsis cases, but TMAD was not. Conclusion STE-based TMAD is a novel and feasible technology with promising discriminatory value for predicting SICM with LVEF < 50%.

To address the issues of not accurately identifying ice types and thickness in current fiber-optic ice sensors, in this paper, we design a novel fiber-optic ice sensor based on the reflected light intensity modulation method and total reflection principle. The performance of the fiber-optic ice sensor was simulated by ray tracing. The low-temperature icing tests validated the performance of the fiber-optic ice sensor. It is shown that the ice sensor can detect different ice types and the thickness from 0.5 to 5 mm at temperatures of −5 °C, −20 °C, and −40 °C. The maximum measurement error is 0.283 mm. The proposed ice sensor provides promising applications in aircraft and wind turbine icing detection.

Background Sclerotinia sclerotiorum is a destructive necrotrophic fungus, that causes stem rot in Brassica napus , severely reducing yield worldwide. While host resistance is shaped by complex transcriptional and post-transcriptional regulation, the contribution of alternative splicing (AS) to cultivar-specific resistance in B. napus remains poorly understood. Results We conducted an integrative transcriptomic analysis of three resistant and three susceptible B. napus cultivars pre- and post-inoculation to characterize genotype-specific AS, gene expression changes, and transcript-based co-expression networks during pathogen infection. A total of 1,176 differentially alternatively spliced (DAS) genes were identified from the comparison between the transcriptomes of the infection-induced (II) and cultivar-related (CR) groups. Surprisingly, we found that 91% of DAS genes were genotype specific, highlighting the strong cultivar dependence of AS responses. Intron retention was the predominant AS event, and ~ 80% of the DAS genes were also differentially expressed, suggesting a complex connection between splicing and expression regulation. Weighted gene co-expression network analysis (WGCNA) based on transcripts identified key modules related to the pathogen response, identifying hub regulators involved in membrane trafficking, transcriptional control, and stress-associated metabolism. Prominent DAS genes such as SEC14-like lipid transfer protein ( SFH8 ), FKBP17 , and transcription factors such as HCA2 , VAL2 , and WRI4 could strongly implicated in immune signaling and hormonal pathways. Conclusion Our findings establish AS as a critical and genotype-dependent regulatory layer in B. napus defense against S. sclerotiorum . Linking splicing dynamics with co-expression networks and highlighting key hub regulators can pave the way for improving B. napus resistance in the future.

While the effect of amplification-induced oncogene expression in cancer is known, the impact of copy-number gains on “bystander” genes is less understood. We create a comprehensive map of dosage compensation in cancer by integrating expression and copy number profiles from over 8000 tumors in The Cancer Genome Atlas and cell lines from the Cancer Cell Line Encyclopedia. Additionally, we analyze 17 cancer open reading frame screens to identify genes toxic to cancer cells when overexpressed. Combining these approaches, we propose a class of ‘Amplification-Related Gain Of Sensitivity’ (ARGOS) genes located in commonly amplified regions, yet expressed at lower levels than expected by their copy number, and toxic when overexpressed. We validate RBM14 as an ARGOS gene in lung and breast cancer cells, and suggest a toxicity mechanism involving altered DNA damage response and STING signaling. We additionally observe increased patient survival in a radiation-treated cancer cohort with RBM14 amplification. In cancer, the impact on cellular fitness of copy-number gains affecting collaterally-amplified genes remains poorly understood compared to oncogenes. Here, the authors integrate genomic data from tumours and cell lines and identify a class of ‘Amplification-Related Gain Of Sensitivity’ (ARGOS) genes, with potential therapeutic applications.

Knowledge tracing (KT) is based on modeling students’ behavior sequences to obtain students’ knowledge state and predict students’ future performance. The KT task aims to model students’ knowledge state in real-time according to their historical learning behavior, so as to predict their future learning performance. Online education has become more critical in recent years due to the impact of COVID-19, and KT has also attracted much attention due to its importance in the education field. However, previous KT models generally have the following three problems. Firstly, students’ learning and forgetting behaviors affect their knowledge state, and past KT models have yet to exploit this fully. Secondly, the input of traditional KT models is mainly limited to students’ exercise sequence and answers. In the learning process, students’ answering performance can reflect their knowledge level. Finally, the context of students’ learning sequence also affects their judgment of the knowledge state. In this paper, we combined educational psychology theories to propose enhanced learning and forgetting behavior for contextual knowledge tracing (LFEKT). LFEKT enriches the features of exercises by introducing difficulty information and considers the influence of students’ answering behavior on the knowledge state. In order to model students’ learning and forgetting behavior, LFEKT integrates multiple influencing factors to build a knowledge acquisition module and a knowledge retention module. Furthermore, LFEKT introduces a long short-term memory (LSTM) network to capture the contextual relations of learned sequences. From the experimental results, it can be seen that LFEKT had better prediction performance than existing models on four public datasets, which indicates that LFEKT can better trace students’ knowledge state and has better prediction performance.