Analysis of factors affecting microbial degradation of cyanobacterial toxins based on theoretical calculations.

Cyanobacterial toxins are the most common algal toxins, which are highly toxic and can persist in the aquatic environment without easy degradation, posing risks to the ecosystem and human health that cannot be ignored. Although microbiological methods for the removal of cyanobacterial toxins from aqueous environments are highly efficient, their degradation efficiency is susceptible to many abiotic environmental factors. In this paper, Microcystin-LR (MC-LR) and its microbial degrading enzymes were selected to study the effects of common environmental factors (temperature (T), NO3-, NH4+, Cu2+, Zn2+) and their levels during microbial degradation of cyanobacterial toxins in aqueous environments by using molecular docking, molecular dynamics simulation, analytical factor design, and the combined toxicokinetics of TOPKAT (toxicity prediction). It was found that the addition of T, NO3- and Cu2+ to the aqueous environment promoted the microbial degradation of MC-LR, while the addition of NH4+ and Zn2+ inhibited the degradation; The level effect study showed that the microbial degradation of MC-LR was promoted by increasing levels of added T and NO3- in the aqueous environment, whereas it was inhibited and then promoted by increasing levels of NH4+, Cu2+ and Zn2+. In addition, the predicted toxicity of common Microcystins (MCs) showed that MC-LR, Microcystin-RR (MC-RR) and Microcystin-YR (MC-YR) were not carcinogenic, developmentally toxic, mutagenic or ocular irritants in humans. MC-LR and MC-RR are mild skin irritants and MC-YR is not a skin irritant. MC-YR has a higher chronic and acute toxicity in humans than MC-LR and MC-RR. Acute/chronic toxicity intensity for aquatic animals: MC-YR > MC-LR > MC-RR and for aquatic plants: MC-LR > MC-YR > MC-RR. This suggests that MC-YR also has a high environmental health risk. This paper provides theoretical support for optimizing the environmental conditions for microbial degradation of cyanobacterial toxins by studying the effects of common environmental factors and their level effects in the aquatic environment.

Raman spectroscopy analysis combined with computed tomography imaging to identify microsatellite instability in gastric cancers.

Accurate determination of microsatellite instability (MSI) status is critical for tailoring treatment approaches for gastric cancer patients. Existing clinical techniques for MSI diagnosis are plagued by problems of suboptimal time efficiency, high cost, and burdensome experimental requirements. Here, we for the first time establish the classification model of gastric cancer MSI status based on Raman spectroscopy. To begin with, we reveal that tumor heterogeneity-induced signal variations pose a prominent impact on MSI classification. To eliminate this issue, we develop Euclidean distance-based Raman Spectroscopy (EDRS) algorithm, which establishes a standard spectrum to represent the "most microsatellite stable" status. The similarity between each spectrum of tissues with the standard spectrum is calculated to provide a direct assessment on the MSI status. Compared to machine learning-algorithms including k-Nearest Neighbors, Random Forest, and Extreme Learning Machine, the EDRS method shows the highest accuracy of 94.6 %. Finally, we integrate the EDRS method with the clinical diagnostic modality, computed tomography, to construct an innovative joint classification model with good classification performance (AUC = 0.914, Accuracy = 94.6 %). Our work demonstrates a robust, rapid, non-invasive, and convenient tool in identifying the MSI status, and opens new avenues for Raman techniques to fit into existing clinical workflow.

Bone Transport for Large Segmental Tibial Defects Using Taylor Spatial Frame versus the Ilizarov Circular Fixator.

OBJECTIVE: Bone transport has become the gold standard for treating large segmental tibial bone defects. The technique for application the Ilizarov circular fixator (ICF) has a long learning curve and is associated with many complications. There are few clinical studies on bone transport via the Taylor spatial frame (TSF). The main purpose of this study was to compare the radiological and clinical and outcomes of bone transport by using the TSF and the ICF. METHODS: There were 62 patients included in this retrospective study from June 2011 to June 2021 and distributed to two groups according to the fixation method: a TSF group consisting of 30 patients and an ICF group consisting of 32 patients. Demographic information, surgical duration, external fixation times, external fixation index, final radiographic results, complications, and clinical outcomes were recorded and examined. The clinical outcomes were assessed using the ASAMI criteria during the most recent clinical visit. Then, statistical analysis such as independent-samples t tests or chi-Square test was performed. RESULTS: The mean surgical duration in the TSF group was 93.8 ± 7.3 min, which was shorter than that in the ICF group (109.8 ± 1.4 min) (p < 0.05). Compared to the ICF group (10.2 ± 2.0 months), the TSF group (9.7 ± 1.8 months) had a shorter average external fixation time (p > 0.05). The external fixation index was 1.4 ± 0.2 m/cm and 1.5 ± 0.1 m/cm in the two groups. Moreover, there was no significant difference between the two groups. At the last follow-up visit, the medial proximal tibial angle (MPTA) and posterior proximal tibial angle (PPTA) in the TSF group were 88.1 ± 12.1° and 80.9 ± 1.3°, respectively. The MPTA and PPTA in the ICF group were 84.4 ± 2.4° and 76.2 ± 1.9°, respectively. There were statistically significant differences between the two groups (all p < 0.05). The complication rate was 50% in the TSF group and 75% in the ICF group. Moreover, the ASAMI score between the two groups was no statistically significant difference (p > 0.05). CONCLUSION: No statistically significant difference was found in clinical outcomes between the use of Taylor spatial frame and Ilizarov circular fixator for treating large segmental tibial bone defects. However, TSF is a shorter and simpler procedure that causes fewer complications and improves limb alignment.

Prediction of the binding interactions between rosmarinic acid and cysteinyl leukotriene receptor type 1 by molecular docking and immobilized receptor chromatography.

Drug-protein interaction analysis is still at the center of research efforts to illustrate binding mechanisms and provide valuable information for selecting drug candidates with ideal properties in the early drug discovery stage. We present the prediction of the binding of rosmarinic acid (RA) to cysteinyl leukotriene receptor type1 (CysLTR1) by molecular docking. According to our findings, CysLTR1 is a potential anti-inflammatory target of RA. Under this assumption, we prepared the immobilized CysLTR1 column via a one-step method and characterized the immobilized CysLTR1 by fluorescent and chromatographic analyses. Furthermore, we used the immobilized CysLTR1 column to evaluate the binding interactions between RA and the immobilized receptor. Molecular docking showed that Tyr 249, Phe 174, Thr 280, Pro 177, and Thr 100 are the main sites for RA to interact with CysLTR1. The main forces that drive the findings are hydrogen bonds and hydrophobic interactions. Characterization results show that CysLTR1 is successfully immobilized with high specificity and stability. Almost no non-specific binding is observed on the immobilized CysLTR1 gels. The association constant and the binding sites are calculated to be 7.268 × 105 L mol-1 and 1.237 × 10-8 mol L-1 by injection amount-dependent method. These results, taken together, confirm the potential target of RA on the anti-inflammatory effect. We believe that it can provide valuable reference information on the in-depth exploration of drug-protein interaction mechanisms, and lead compound screening by this method.

Prediction of tumor regression grade in far-advanced gastric cancer after preoperative immuno-chemotherapy using dual-energy CT-derived extracellular volume fraction.

OBJECTIVES: This study examines the effectiveness of dual-energy CT (DECT) delayed-phase extracellular volume (ECV) fraction in predicting tumor regression grade (TRG) in far-advanced gastric cancer (FAGC) patients receiving preoperative immuno-chemotherapy. MATERIALS AND METHODS: A retrospective analysis was performed on far-advanced gastric adenocarcinoma patients treated with preoperative immuno-chemotherapy at our institution from August 2019 to March 2023. Patients were categorized based on their TRG into pathological complete response (pCR) and non-pCR groups. ECV was determined using the delayed-phase iodine maps. In addition, tumor iodine densities and standardized iodine ratios were meticulously analyzed using the triple-phase enhanced iodine maps. Univariate analysis with five-fold cross-validation and Spearman correlation determined DECT parameters and clinical indicators association with pCR. The predictive accuracy of these parameters for pCR was evaluated using a weighted logistic regression model with five-fold cross-validation. RESULTS: Of the 88 patients enrolled (mean age 60.8 ± 11.1 years, 63 males), 21 (23.9%) achieved pCR. Univariate analysis indicated ECV's significant role in differentiating between pCR and non-pCR groups (average p value = 0.021). In the logistic regression model, ECV independently predicted pCR with an average odds ratio of 0.911 (95% confidence interval, 0.798-0.994). The model, incorporating ECV, tumor area, and IDAV (the relative change rate of iodine density from venous phase to arterial phase), showed an average area under curves (AUCs) of 0.780 (0.770-0.791) and 0.766 (0.731-0.800) for the training and validation sets, respectively, in predicting pCR. CONCLUSION: DECT-derived ECV fraction is a valuable predictor of TRG in FAGC patients undergoing preoperative immuno-chemotherapy. CLINICAL RELEVANCE STATEMENT: This study demonstrates that DECT-derived extracellular volume fraction is a reliable predictor for pathological complete response in far-advanced gastric cancer patients receiving preoperative immuno-chemotherapy, offering a noninvasive tool for identifying potential treatment beneficiaries.

Traceability and policy suggestions for ozone pollution in heavy industrial city in Northeast China.

In the heavy industrial city of Northeast China, there has been a significant decrease in particulate matter pollution while experiencing a sharp increase in ozone (O3) pollution. However, the main influencing factors and source contributions to O3 remain unclear. Taking the case of Siping as an example, this study analyzed the spatiotemporal characteristics, assessed local source contributions to O3, and revealed regional transmission effects using numeric simulation and statistical methods. Temporally, higher O3 concentrations were observed in summer and the afternoon, with hourly peaks up to 254 µg/m3. Spatially, O3 pollution was mainly contributed by background concentrations (34.52%), external transport (34.50%), and local emissions (30.98%) during the case study period (June 11-18, 2021). Among the local emission sources, biological emissions, the industrial sector, and the traffic sector accounted for 35.30%, 32.09%, and 23.58% of the O3 concentration, respectively. For regional atmospheric transmission, high O3 pollution was accompanied by wind from the southwest directions, and the trajectory of air mass transport suggests that eastern Mongolia, the Korean Peninsula, and its neighboring regions contribute to O3 pollution. Furthermore, sensitivity analysis showed that O3 pollution in Siping is a co-controlled region by anthropogenic volatile organic compounds (AVOCs) and NOX, which implies control in an optimal ratio of VOCs and NOX emissions. Thus, our results highlight the importance of joint prevention and control of O3 pollution in the region, optimization of biogenic landscape ecology, and control of VOCs and NOx in both the industrial and transport sectors.

Epidemiology and genotypic diversity of feline bocavirus identified from cats in Harbin, China.

Feline bocavirus (FBoV) is a globally distributed linear, single-stranded DNA virus infect cats, currently classified into three distinct genotypes. Although FBoV can lead to systemic infections, its complete pathogenic potential remains unclear. In this study, 289 blood samples were collected from healthy cats in Harbin, revealing an overall FBoV prevalence of 12.1%. Notably, genotypes 1 and 3 of FBoV were found co-circulating among the cat population in Harbin. Additionally, recombination events were detected, particularly in the newly discovered NG/104 and DL/102 strains. Furthermore, negative selection sites were predominantly observed across the protein coding genes of FBoV. These findings suggest a co-circulation of genetically diverse FBoV strains among cats in Harbin, indicate that purifying selection is the primary driving force shaping the genomic evolution of FBoV, and also underscore the importance of comprehensive surveillance efforts to enhance our understanding of the epidemiology and evolutionary characteristics of FBoV.

A case of congenital bronchial atresia with tracheobronchial stenosis caused by emphysema: Successful management with thoracoscopic surgery.

Introduction: Congenital bronchial atresia (CBA), as a rare developmental abnormality of the lung, is usually asymptomatic and is accidently discovered in most cases. Currently, no standardized guidelines for the treatment or management of CBA have been established. Case presentation: A 22-year-old male soldier was referred to Shanghai Changhai Hospital, The First Affiliated Hospital of Naval Medical University due to chest tightness and shortness of breath after repeated strenuous activities. Contrast-enhanced computed tomography (CT) revealed an 18mm × 11mm solitary, well-circumscribed, and solid nodule with no enhancement in the right upper lobe (RUL), and emphysematous changes distributed throughout the RUL. A flexible bronchoscopic examination showed extrinsic compression stenosis in the bronchial opening of the right middle lobe (RML). After three-dimensional (3D) reconstruction CT and a multidisciplinary consultation, a diagnosis of CBA in the anterior segment (B3) of RUL was established. Subsequently, thoracoscopic right upper lobectomy was performed and resulted in an improved respiratory capacity 6 months after surgery. To date, the patient has good quality of life without any complication. Conclusion: This study underscores the role of bronchoscopy, 3D reconstruction CT, and a multidisciplinary consultation in the diagnosis of CBA, and highlights that a thoracoscopic intervention should be considered in such case.

Microwave biosensor for the detection of growth inhibition of human liver cancer cells at different concentrations of chemotherapeutic drug.

Cytotoxicity assays are crucial for assessing the efficacy of drugs in killing cancer cells and determining their potential therapeutic value. Measurement of the effect of drug concentration, which is an influence factor on cytotoxicity, is of great importance. This paper proposes a cytotoxicity assay using microwave sensors in an end-point approach based on the detection of the number of live cells for the first time. In contrast to optical methods like fluorescent labeling, this research uses a resonator-type microwave biosensor to evaluate the effects of drug concentrations on cytotoxicity by monitoring electrical parameter changes due to varying cell densities. Initially, the feasibility of treating cells with ultrapure water for cell counting by a microwave biosensor is confirmed. Subsequently, inhibition curves generated by both the CCK-8 method and the new microwave biosensor for various drug concentrations were compared and found to be congruent. This agreement supports the potential of microwave-based methods to quantify cell growth inhibition by drug concentrations.

DiffFSRE: Diffusion-Enhanced Prototypical Network for Few-Shot Relation Extraction.

Supervised learning methods excel in traditional relation extraction tasks. However, the quality and scale of the training data heavily influence their performance. Few-shot relation extraction is gradually becoming a research hotspot whose objective is to learn and extract semantic relationships between entities with only a limited number of annotated samples. In recent years, numerous studies have employed prototypical networks for few-shot relation extraction. However, these methods often suffer from overfitting of the relation classes, making it challenging to generalize effectively to new relationships. Therefore, this paper seeks to utilize a diffusion model for data augmentation to address the overfitting issue of prototypical networks. We propose a diffusion model-enhanced prototypical network framework. Specifically, we design and train a controllable conditional relation generation diffusion model on the relation extraction dataset, which can generate the corresponding instance representation according to the relation description. Building upon the trained diffusion model, we further present a pseudo-sample-enhanced prototypical network, which is able to provide more accurate representations for prototype classes, thereby alleviating overfitting and better generalizing to unseen relation classes. Additionally, we introduce a pseudo-sample-aware attention mechanism to enhance the model's adaptability to pseudo-sample data through a cross-entropy loss, further improving the model's performance. A series of experiments are conducted to prove our method's effectiveness. The results indicate that our proposed approach significantly outperforms existing methods, particularly in low-resource one-shot environments. Further ablation analyses underscore the necessity of each module in the model. As far as we know, this is the first research to employ a diffusion model for enhancing the prototypical network through data augmentation in few-shot relation extraction.

Real-Time Detection of Yeast Growth on Solid Medium through Passive Microresonator Biosensor.

This study presents a biosensor fabricated based on integrated passive device (IPD) technology to measure microbial growth on solid media in real-time. Yeast (Pichia pastoris, strain GS115) is used as a model organism to demonstrate biosensor performance. The biosensor comprises an interdigital capacitor in the center with a helical inductive structure surrounding it. Additionally, 12 air bridges are added to the capacitor to increase the strength of the electric field radiated by the biosensor at the same height. Feasibility is verified by using a capacitive biosensor, and the change in capacitance values during the capacitance detection process with the growth of yeast indicates that the growth of yeast can induce changes in electrical parameters. The proposed IPD-based biosensor is used to measure yeast drop-added on a 3 mm medium for 100 h at an operating frequency of 1.84 GHz. The resonant amplitude of the biosensor varies continuously from 24 to 72 h due to the change in colony height during vertical growth of the yeast, with a maximum change of 0.21 dB. The overall measurement results also fit well with the Gompertz curve. The change in resonant amplitude between 24 and 72 h is then analyzed and reveals a linear relationship with time with a coefficient of determination of 0.9844, indicating that the biosensor is suitable for monitoring yeast growth. Thus, the proposed biosensor is proved to have potential in the field of microbial proliferation detection.

Exosomal NAT10 from esophageal squamous cell carcinoma cells modulates macrophage lipid metabolism and polarization through ac4C modification of FASN.

N-acetyltransferase 10 (NAT10) is acknowledged as a tumor promoter in various cancers due to its role as a regulator of acetylation modification. Tumor-associated macrophages (TAMs) play a pivotal role in the tumor microenvironment (TME). However, the intercellular communication between esophageal squamous cell carcinoma (ESCC) cells and TAMs involving NAT10 remains poorly understood. This study aimed to elucidate the regulatory mechanism of NAT10 in modulating macrophage lipid metabolism and polarization. Experimental evidence was derived from in vitro and in vivo analyses. We explored the association between upregulated NAT10 in ESCC tissues, macrophage polarization, and the therapeutic efficacy of PD-1. Furthermore, we investigated the impact of methyltransferase 3 (METTL3)-induced m6A modification on the increased expression of NAT10 in ESCC cells. Additionally, we examined the role of exosomal NAT10 in stabilizing the expression of fatty acid synthase (FASN) and promoting macrophage M2 polarization through mediating the ac4C modification of FASN. Results indicated that NAT10, packaged by exosomes derived from ESCC cells, promotes macrophage M2 polarization by facilitating lipid metabolism. In vivo animal studies demonstrated that targeting NAT10 could enhance the therapeutic effect of PD-1 on ESCC by mediating macrophage reprogramming. Our findings offer novel insights into improving ESCC treatment through NAT10 targeting.

Resting State Brain Networks under Inverse Agonist versus Complete Knockout of the Cannabinoid Receptor 1.

The cannabinoid receptor 1 (CB1) is famous as the target of Δ9-tetrahydrocannabinol (THC), which is the active ingredient of marijuana. Suppression of CB1 is frequently suggested as a drug target or gene therapy for many conditions (e.g., obesity, Parkinson's disease). However, brain networks affected by CB1 remain elusive, and unanticipated psychological effects in a clinical trial had dire consequences. To better understand the whole brain effects of CB1 suppression we performed in vivo imaging on mice under complete knockout of the gene for CB1 (cnr1-/-) and also under the CB1 inverse agonist rimonabant. We examined white matter structural changes and brain function (network activity and directional uniformity) in cnr1-/- mice. In cnr1-/- mice, white matter (in both sexes) and functional directional uniformity (in male mice) were altered across the brain but network activity was largely unaltered. Conversely, under rimonabant, functional directional uniformity was not altered but network activity was altered in cortical regions, primarily in networks known to be altered by THC (e.g., neocortex, hippocampal formation). However, rimonabant did not alter many brain regions found in both our cnr1-/- results and previous behavioral studies of cnr1-/- mice (e.g., thalamus, infralimbic area). This suggests that chronic loss of cnr1 is substantially different from short-term suppression, subtly rewiring the brain but largely maintaining the network activity. Our results help explain why pathological mutations in CB1 (e.g., chronic pain) do not always provide insight into the side effects of CB1 suppression (e.g., clinical depression), and thus urge more preclinical studies for any drugs that suppress CB1.

Pseudorabies virus manipulates mitochondrial tryptophanyl-tRNA synthetase 2 for viral replication.

The pseudorabies virus (PRV) is identified as a double-helical DNA virus responsible for causing Aujeszky's disease, which results in considerable economic impacts globally. The enzyme tryptophanyl-tRNA synthetase 2 (WARS2), a mitochondrial protein involved in protein synthesis, is recognized for its broad expression and vital role in the translation process. The findings of our study showed an increase in both mRNA and protein levels of WARS2 following PRV infection in both cell cultures and animal models. Suppressing WARS2 expression via RNA interference in PK-15 â€‹cells led to a reduction in PRV infection rates, whereas enhancing WARS2 expression resulted in increased infection rates. Furthermore, the activation of WARS2 in response to PRV was found to be reliant on the cGAS/STING/TBK1/IRF3 signaling pathway and the interferon-alpha receptor-1, highlighting its regulation via the type I interferon signaling pathway. Further analysis revealed that reducing WARS2 levels hindered PRV's ability to promote protein and lipid synthesis. Our research provides novel evidence that WARS2 facilitates PRV infection through its management of protein and lipid levels, presenting new avenues for developing preventative and therapeutic measures against PRV infections.

Highly Sensitive and Linear Resonator-Based Biosensor for White Blood Cell Counting: Feasible Measurement Method and Intrinsic Mechanism Exploration.

Since different quantities of white blood cells (WBCs) in solution possess an adaptive osmotic pressure of cells, the WBCs themselves and in solution have similar concentrations, resulting in them having similar dielectric properties. Therefore, a microwave sensor could have difficulty in sensing the quantity variation when WBCs are in solution. This paper presents a highly sensitive, linear permittivity-inspired microwave biosensor for WBCs, counting through the evaporation method. Such a measurement method is proposed to record measurements after the cell solution is dripped onto the chip and is completely evaporated naturally. The proposed biosensor consists of an air-bridged asymmetric differential inductor and a centrally located circular fork-finger capacitor fabricated on a GaAs substrate using integrated passive fabrication technology. It is optimized to feature a larger sensitive area and improved Q-factor, which increases the effective area of interaction between cells and the electromagnetic field and facilitates the detection of their changes in number. The sensing relies on the dielectric properties of the cells and the change in the dielectric constant for different concentrations, and the change in resonance properties, which mainly represents the frequency shift, corresponds to the macroscopic change in the concentration of the cells. The microwave biosensors are used to measure biological samples with concentrations ranging from 0.25 × 106 to 8 × 106 cells per mL in a temperature (26.00 ± 0.40 °C) and humidity (54.40 ± 3.90 RH%) environment. The measurement results show a high sensitivity of 25.06 Hz/cells·mL-1 with a highly linear response of r2 = 0.99748. In addition, a mathematical modeling of individual cells in suspension is performed to estimate the dielectric constant of individual cells and further explain the working mechanism of the proposed microwave biosensor.

Tracing the impacts of ecological water replenishment on the sources and transformation of groundwater nitrate through isotope and microbial analysis.

Ecological water replenishment (EWR) changes the recharge conditions, flow fields, and physicochemical properties of regional groundwater. However, the resulting impacts on mechanisms regulating the sources and transformation of groundwater nitrate remain unclear. This study investigated how EWR influences the sources and transformation processes of groundwater nitrate using an integrated approach of Water chemistry analysis and stable isotopes (δ15N-NO3- and δ18O-NO3-) along with microbial techniques. The results showed that groundwater NO3-N decreased from 12.98 ± 7.39 mg/L to 7.04 ± 8.52 mg/L after EWR. Water chemistry and isotopic characterization suggested that groundwater nitrate mainly originated from sewage and manure. The Bayesian isotope mixing model (MixSIAR) indicated that EWR increased the average contribution of sewage and manure sources to groundwater nitrate from 46 % to 61 %, whereas that of sources of chemical fertilizer decreased from 43 % to 21 %. Microbial community analysis revealed that EWR resulted in a substantial decrease in the relative abundance of Pseudomonas spp denitrificans, from 13.7 % to 0.6 %. Both water chemistry and microbial analysis indicated that EWR weakened denitrification and enhanced nitrification in groundwater. EWR increases the contribution of nitrate to groundwater by promoting the release of sewage and feces in the unsaturated zone. However, the dilution effect caused by EWR was stronger than the contribution of sewage and fecal sources to groundwater nitrate. As a result, EWR helped to reduce groundwater nitrate concentrations. This study showed the effectiveness of integrated isotope and microbial techniques for delineating the sources and transformations of groundwater nitrate influenced by EWR.

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment.

Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both surface water and groundwater within the basin. However, fluctuations in GWLs during the EWR process exhibit high nonlinearity and complexity in their time series, making it challenging for single data-driven models to predict the trend of groundwater level changes under the backdrop of EWR. This study introduced a new GWL prediction strategy based on a hybrid deep learning model, STL-IWOA-GRU. It integrated the LOESS-based seasonal trend decomposition algorithm (STL), improved whale optimization algorithm (IWOA), and Gated recurrent unit (GRU). The aim was to accurately predict GWLs in the context of EWR. This study gathered GWL, precipitation, and surface runoff data from 21 monitoring wells in the Yongding River Basin (Beijing Section) over a period of 731 days. The research results demonstrate that the improvement strategy implemented for the IWOA enhances the convergence speed and global search capabilities of the algorithm. In the case analysis, evaluation metrics including the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) were employed. STL-IWOA-GRU exhibited commendable performance, with MAE achieving the best result, averaging at 0.266. When compared to other models such as Variance Mode Decomposition-Gated Recurrent Unit (VMD-GRU), Ant Lion Optimizer-Support Vector Machine (ALO-SVM), STL-Particle Swarm Optimization-GRU (STL-PSO-GRU), and STL-Sine Cosine Algorithm-GRU (STL-SCA-GRU), MAE was reduced by 18%, 26%, 11%, and 29%, respectively. This indicates that the model proposed in this study exhibited high prediction accuracy and robust versatility, making it a potent strategic choice for forecasting GWL changes in the context of EWR.

Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction.

Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity concepts. To address this, our paper proposes a two-stage mention-level framework based on an enhanced heterogeneous graph attention network for document-level relation extraction. Our framework employs two different strategies to model intra-sentential and inter-sentential relations between fine-grained entity mentions, yielding local mention representations for intra-sentential relation prediction and global mention representations for inter-sentential relation prediction. For inter-sentential relation prediction and inference, we propose an enhanced heterogeneous graph attention network to better model the long-distance semantic relationships and design an entity-coreference path-based inference strategy to conduct relation inference. Moreover, we introduce a novel cross-entropy-based multilabel focal loss function to address the class imbalance problem and multilabel prediction simultaneously. Comprehensive experiments have been conducted to verify the effectiveness of our framework. Experimental results show that our approach significantly outperforms the existing methods.

CRISPR genome-wide screening identifies PAK1 as a critical driver of ARSI cross-resistance in prostate cancer progression.

Next-generation androgen receptor signaling inhibitors (ARSIs), such as enzalutamide (Enza) and darolutamide (Daro), are initially effective for the treatment of advanced prostate cancer (PCa) and castration-resistant prostate cancer (CRPC). However, patients often relapse and develop cross-resistance, which consequently makes drug resistance an inevitable cause of CRPC-related mortality. By conducting a comprehensive analysis of GEO datasets, CRISPR genome-wide screening results, ATAC-seq data, and RNA-seq data, we systemically identified PAK1 as a significant contributor to ARSI cross-resistance due to the activation of the PAK1/RELA/hnRNPA1/AR-V7 axis. Inhibition of PAK1 followed by suppression of NF-κB pathways and AR-V7 expression effectively overcomes ARSI cross-resistance. Our findings indicate that PAK1 represents a promising therapeutic target gene for the treatment of ARSI cross-resistant PCa patients in the clinic. STATEMENT OF SIGNIFICANCE: PAK1 drives ARSI cross-resistance in prostate cancer progression.