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PARP inhibitors have been proven clinically efficacious in platinum-responsive ovarian cancer regardless of BRCA1/2 status and in breast cancers with germline BRCA1/2 mutation. However, resistance to PARP inhibitors may preexist or evolve during treatment in many cancer types and may be overcome by combining PARP inhibitors with other therapies, such as immune checkpoint inhibitors, which confer durable responses and are rapidly becoming the standard of care for multiple tumor types. This study investigated the therapeutic potential of combining niraparib, a highly selective PARP1/2 inhibitor, with anti-PD-1 immune checkpoint inhibitors in preclinical tumor models. Our results indicate that niraparib treatment increases the activity of the type I (alpha) and type II (gamma) interferon pathways and enhances the infiltration of CD8 + cells and CD4 + cells in tumors. When coadministered in immunocompetent models, the combination of niraparib and anti-PD-1 demonstrated synergistic antitumor activities in both BRCA- proficient and BRCA -deficient tumors. Interestingly, mice with tumors cured by niraparib monotherapy completely rejected tumor growth upon rechallenge with the same tumor cell line, suggesting the potential establishment of immune memory in animals treated with niraparib monotherapy. Taken together, our findings uncovered immunomodulatory effects of niraparib that may sensitize tumors to immune checkpoint blockade therapies.

In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

To address the challenges posed by high data dimensionality and class imbalance during intrusion detection, which result in increased computational complexity, resource consumption, and reduced classification accuracy, this paper presents an intrusion-detection algorithm based on an improved Random Forest approach. The algorithm employs the Bald Eagle Search (BES) optimization technique to fine-tune the Kernel Principal Component Analysis (KPCA) algorithm, enabling optimized dimensionality reduction. The processed data are then fed into a cost-sensitive Random Forest classifier for training, with subsequent model validation conducted on the reduced-dimension data. Experimental results demonstrate that compared to traditional Random Forest algorithms, the proposed method reduces the training time by 11.32 s and achieves a 5.59% increase in classification accuracy, an 11.7% improvement in specificity, and a 0.0558 increase in the G-mean value. These findings underscore the promising application potential and performance of this approach in the field of network intrusion detection.

Handwriting brain-computer interfaces (BCIs) have enabled high performance brain-to-text communication for paralyzed individuals. However, the detailed parameters of handwriting movement and their cortical representations remain incompletely understood. Here, we recorded intracortical neural activity from a paralyzed subject and found distinct neural representations for strokes and pen lifts with respect to two-dimensional (2D) velocity on the writing plane, indicating that 2D kinematics alone cannot fully account for the observed neural variance. To address this, we acquired multidimensional handwriting data from healthy subjects, including 3D velocity, grip force, writing pressure, and multi-channel electromyographic (EMG) signals. Incorporating these additional dimensions beyond 2D velocity significantly improved the interpretability of neural signals for both strokes and pen lifts. We further leveraged these additional dimensions to enhance handwriting decoding performance. Together, our findings indicate the motor cortex encodes handwriting as multidimensional movement and highlight the importance of multidimensional features for improving the performance of handwriting BCIs. This study reveals that the brain encodes handwriting in multiple dimensions. The decoding paradigm, informed by movement data from healthy individuals, captures these full dimensions to enhance the performance of brain-to-text communication.

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

The potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain‐computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurate and human‐recognizable handwriting trajectories, significantly outperforming conventional methods. Furthermore, the new framework enabled effective multi‐day data fusion, leading to additional improvements in trajectory quality. By employing a dynamic time warping approach to translate trajectories into text, a recognition rate up to 91.1% is achieved within a 1000‐character database. Additionally, the framework is applied to reconstruct single‐trial trajectories of English letters using a previously published dataset, achieving similarly high recognition rates. Collectively, these findings present a novel BCI decoding scheme capable of accurately reconstructing handwriting trajectories, demonstrating its applicability to both alphabetic and logographic brain‐to‐text translation. This approach has the potential to revolutionize communication for individuals with motor impairments by enabling accurate brain‐to‐text translation across diverse languages. By developing a novel framework that optimizes both shape and temporal loss during decoder training, the authors successfully reconstruct human‐recognizable handwriting trajectories from intracortical neural signals for both Chinese characters and English letters, effectively resolving the temporal misalignment problem in clinical BCIs, thereby establishing a robust brain‐to‐text communication plateform adaptable to diverse writing systems.

The expansion of hydraulic fractures in coalbed methane (CBM) reservoirs is key to effective stimulation, making it essential to understand fracture propagation and its influencing factors for efficient resource development. Using petrological characteristics, logging data, microseismic monitoring, and fracturing reports from the Baode Block on the eastern Ordos Basin, this study systematically investigates the geological and engineering factors influencing hydraulic fracture propagation. The real-time monitoring of fracture propagation in 12 fractured wells was conducted using microseismic monitoring techniques. The results indicated that the fracture orientations in the study area ranged from NE30° to NE60°, with fracture lengths varying between 136 and 226 m and fracture heights ranging from 8.5 to 25.3 m. Additionally, the fracturing curves in the study area can be classified into four types: stable, descending, fluctuating, and falling. Among these, the stable and descending types exhibit the most effective fracture propagation and are more likely to generate longer fractures. In undeformed–cataclastic coals and bright and semi-bright coals, long fractures are likely to form. When the Geological Strength Index (GSI) of the coal rock ranges between 60 and 70, fracture lengths generally exceed 200 m. When the coal macrolithotype index (Sm) is below 2, fracture lengths typically exceed 200 m. When the difference between the maximum and minimum horizontal principal stresses exceeds 5 MPa, fractures with length >180 m are formed, while fracture heights generally remain below 15 m. From an engineering perspective, for the study area, hydraulic fracturing measures with a preflush ratio of 20–30%, an average sand ratio of 13–15%, and a construction pressure between 15 MPa and 25 MPa are most favorable for coalbed methane production.

Sensitive H2 sensors at low concentrations and room temperature are desired for the early warning and control of hydrogen leakage. In this paper, a resistive sensor based on Pt-doped In2O3 nanoparticles was fabricated using inkjet printing process. The H2 sensing performance of the sensor was evaluated at low concentrations below 1% at room temperature. It exhibited a relative high response of 42.34% to 0.6% H2. As the relative humidity of 0.5% H2 decreased from 34% to 23%, the response decreased slightly from 34% to 23%. The sensing principle and the humidity effect were discussed. A dynamic current sensing model for dry H2 detection was proposed based on Wolkenstein theory and experimentally verified to be able to predict the sensing behavior of the sensor. The H2 concentration can be calculated within a short measurement time using the model without waiting for the saturation of the response, which significantly reduces the sensing and recovery time of the sensor. The sensor is expected to be a promising candidate for room-temperature H2 detection, and the proposed model could be very helpful in promoting the application of the sensor for real-time H2 leakage monitoring.

Background In individuals receiving continuous ambulatory peritoneal dialysis (CAPD), remnant cholesterol (RC) and high-density lipoprotein cholesterol (HDL-C) levels significantly influence clinical outcomes. Current clinical practice might benefit from assessing these two lipid markers in combination when evaluating cardiovascular disease (CVD) and all-cause mortality. Therefore, this research sought to examine how the RC/HDL-C ratio correlates with both CVD and all-cause mortality rates among individuals receiving CAPD treatment. Methods Between January 1, 2005 and December 31, 2016, a multi-center retrospective analysis of 2006 CAPD patients from five peritoneal dialysis hospitals in China was conducted. Participants were split into two subgroups in accordance with the baseline serum RC/HDL-C ratio restricted cubic spline cutoff value. The correlations between mortality and RC/HDL-C ratio were examined through case-specific hazard modeling. Results The observation period documented 549 all-cause fatalities, with cardiovascular deaths accounting for 269 cases. The Kaplan-Meier analysis revealed statistically significant divergence in both all-cause mortality (log rank test P  < 0.001) and CVD mortality (log rank test P  = 0.003). Elevated RC/HDL-C ratios showed increased hazard ratios (HR) for all-cause mortality (1.335, 95% CI, 1.112–1.603, P  = 0.002) and CVD mortality (1.319, 95% CI, 1.013–1.717, P  = 0.040) compared to lower ratio counterparts. Nevertheless, no statistically meaningful association was found between CVD mortality and either RC (HR: 1.296, 95% CI, 0.992–1.691, P  = 0.057) or HDL-C (HR: 0.887, 95% CI, 0.680–1.157, P  = 0.376). Conclusion The RC/HDL-C ratio independently predicts mortality in CAPD patients, persisting as a significant prognostic marker after multivariable adjustment.

BackgroundGastrointestinal bleeding (GIB) is widespread in patients with impaired renal function. Whether angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (ACEi/ARBs) potentially take a crucial role in avoiding GIB incidence among peritoneal dialysis (PD) patients is unknown.MethodsOverall, 734 PD patients were enrolled after using propensity score matching. Kaplan–Meier analysis and COX regression were used to explore correlation between ACEi/ARBs and GIB. Competitive risk model was aimed to identify whether other events were confounding factors. Forest plot was applied to assess the influence of ACEI/ARBs on GIB incidence in different groups.ResultsDuring 8-year follow-up, 89 (12.13%) cases of GIB were recorded. Kaplan–Meier analysis revealed that the incidence of GIB among patients taking ACEi/ARBs was lower than those subjects who had not (log rank = 6.442, P = 0.011). After adjusted different confounding factors, administration of ACEi/ARBs was associated with lowered GIB incidence (adjusted HR = 0.49, 95% CI 0.32–0.77, P = 0.002). In competitive risk model, considering of other events, the incidence of GIB in two groups was still statistically significant (P = 0.010). Subgroup analysis showed ACEi/ARBs taking impeded GIB in the ≥ 60 age group (HR = 0.52, 95% CI 0.28–0.98, P = 0.040).ConclusionPD patients who were submitted to ACEi/ARBs inclined to have a lower risk for GIB. In this regard, ACEi/ARBs offered a promising choice to GIB.