Enhanced activation of peroxymonosulfate with cobalt-doped manganese-iron oxides for contaminant degradation: Regulation of oxygen vacancy defects.

Peroxymonosulfate (PMS) based on heterogeneous catalytic reaction was a promising advanced oxidation process (AOP) to remove refractory contaminants. However, the contaminant degradation efficiency was challenged by the limited number of catalytic active site and low capacity for durable electron transfer. In this study, cobalt-doped manganese-iron oxides (CoxMn1-xFe2O4) rich in oxygen vacancy (Ov) were synthesized using a microwaved hydrothermal method and applied to activate PMS for bisphenol A (BPA) degradation, which achieved the complete removal of BPA within 30 min. In all samples, Co0.5Mn0.5Fe2O4 exhibited good catalytic activity for PMS, which was approximately 21.10 times higher than that of MnFe2O4. The results of density functional theory calculations and in-situ characterization demonstrated that the enhanced performance was ascribed to the generation of Ov and the enrichment of active site, which significantly accelerated the cycling of redox pairs and improved the PMS adsorption, which was more favorable to the formation of active specie in the electron transport process. The oxidation process involved both free radical and non-radical mechanisms, with main reactive species of O2-, and 1O2 being responsible for BPA degradation. In addition, the effects of different aqueous matrices, the results of reusability experiments, and ecotoxicity assessment experiments demonstrated the viability of the Co0.5Mn0.5Fe2O4/PMS system for real sewage purification. This research revealed a structural regulation method to enhance the catalytic activity of the material and offered new perspectives on the engineering of rich Ov.

Development and validation of a prognostic model for cachexia in postoperative gastric cancer patients with low nutritional risk: a dual-center retrospective cohort study.

BACKGROUND: Gastric cancer can lead to excessive catabolism in patients. After undergoing gastric surgery, patients may experience additional unintended weight loss, resulting in severe malnutrition and potentially cachexia. METHODS: We selected and incorporated patients from two centers. Cohort 1 (n = 1393) served as the development cohort, while cohort 2 (n = 501) was designated as an external validation cohort. Within cohort 1, 70% of the patients were utilized for model training, with the remaining 30% reserved for internal validation. The training set initially underwent univariate logistic regression, followed by multivariate logistic regression. The factors ultimately incorporated were used to construct the model and create nomograms. The discriminative ability was assessed using ROC curves in all three datasets, calibration curves were used to evaluate consistency, and decision curves analysis was performed to assess the clinical application value. RESULTS: The model incorporated 12 factors, specifically: age (OR = 1.07), preoperative BMI (OR = 0.89), surgery type (Total Gastrectomy (TG), OR = 1.83), chemotherapy (yes, OR = 1.52), stage (III, OR = 1.40), anastomotic obstruction (yes, OR = 6.85), Postsurgical Gastroparesis Syndrome (PGS) (yes, OR = 2.27), albumin (OR = 0.95), hemoglobin (OR = 0.98), triglycerides (OR = 0.36), CRP (OR = 1.07), and Neutrophil to Lymphocyte Ratio (NLR) (OR = 1.05). The model demonstrated robust performance in ROC with AUC values of 0.805 in the training set, 0.767 in the validation set, and 0.795 in Cohort 2. Calibration curves in all three datasets exhibited a high degree of concordance between actual and predicted probabilities. Decision curve analysis (DCA) indicated that the model holds substantial clinical utility across all three datasets. CONCLUSIONS: We have developed a predictive model for cachexia in patients undergoing gastric cancer surgery. This model enables healthcare professionals to accurately estimate the risk of cachexia in postoperative patients with nutritional deficits, allowing for timely nutritional interventions to enhance patient quality of life and prognosis.

Effective decontamination of DR-81 dye from aqueous solutions using eco-friendly graphene oxide nanoparticles.

Effective management of industrial and agricultural wastes requires a multifaceted approach that considers environmental, economic, and social factors. Our ability to recover resources and create a circular bioeconomy from agricultural waste can be enhanced by implementing sustainable methods such as reducing, reusing, and recycling it. Active graphene oxide (GO) was prepared through the gasification of agricultural waste and further mixed with FeAlOx catalyst for three hours at 800 °C as an efficient adsorbent. The synthesized material was comprehensively characterized using Fourier-transform infrared spectroscopy, X-ray diffraction, high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, Brunauer-Emmett-Teller surface area analysis, and thermal gravimetrical analysis. In order to remove direct red 81 (DR-81) dye from wastewater, the synthesized nanomaterial was implemented as an effective adsorbent. Several processing variables, including pH, contact time, and dosage, were studied to examine the optimum conditions that directly influence the DR-81 decontamination of onto the fabricated GO. The optimal dosage from the synthesized GO for DR-81 decontamination was 0.5 g/L at pH = 7 after 30 min. At pH 7.0 and 25 °C, the produced GO had the highest sorption capacity of 132.14 mg/g towards the DR-81. In addition, equilibrium and kinetic studies were capably fitted via the Freundlich and pseudo-second-order models, respectively. As a result of its particular properties, which include a high surface area, adsorption capacity, structural robustness, variation tolerance, and thermal stability. These promising findings supported the usage of synthesized GO as a superior adsorbent material for DR-81 decontamination from wastewater.

Impaired Iron-Sulfur Cluster Synthesis Induces Mitochondrial PARthanatos in Diabetic Cardiomyopathy.

Diabetic cardiomyopathy (DCM), a severe complication of diabetes, is characterized by mitochondrial dysfunction, oxidative stress, and DNA damage. Despite its severity, the intrinsic factors governing cardiomyocyte damage in DCM remain unclear. It is hypothesized that impaired iron-sulfur (Fe-S) cluster synthesis plays a crucial role in the pathogenesis of DCM. Reduced S-sulfhydration of cysteine desulfurase (NFS1) is a novel mechanism that contributes to mitochondrial dysfunction and PARthanatos in DCM. Mechanistically, hydrogen sulfide (H2S) supplementation restores NFS1 S-sulfhydration at cysteine 383 residue, thereby enhancing Fe-S cluster synthesis, improving mitochondrial function, increasing cardiomyocyte viability, and alleviating cardiac damage. This study provides novel insights into the interplay between Fe-S clusters, mitochondrial dysfunction, and PARthanatos, highlighting a promising therapeutic target for DCM and paving the way for potential clinical interventions to improve patient outcomes.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

Enhanced Visual SLAM for Collision-Free Driving with Lightweight Autonomous Cars.

The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car's poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes.

Alcohols as Alkyl Electrophiles for Deoxygenative Heck Reaction Enabled by Excited State Pd Catalysis.

Here, we present a general method for the photoinduced Pd-catalyzed deoxygenative Heck reaction of vinyl arenes with ortho-iodophenyl-thionocarbonate derived from alcohols. Mechanistic studies reveal that the deoxygenation involves a 5-endo-trig cyclization and fragmentation process, with radical addition identified as the rate-determining step in this transformation. This one-pot procedure demonstrates excellent selectivity for less hindered hydroxyl groups in diols, facilitating late-stage functionalization of complex molecules and scalability to gram-scale synthesis. The protocol highlights significant synthetic potential and can be extended to the cascade 1,1-difunctionalization of isocyanides and the intermolecular radical cascade cyclization of N-arylacrylamides.

Recent Advances in Lipid Nanoparticles and Their Safety Concerns for mRNA Delivery.

INTRODUCTION: The advent of lipid nanoparticles (LNPs) as a delivery platform for mRNA therapeutics has revolutionized the biomedical field, particularly in treating infectious diseases, cancer, genetic disorders, and metabolic diseases. Recent Advances in Therapeutic LNPs: LNPs, composed of ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, facilitate efficient cellular uptake and cytosolic release of mRNA while mitigating degradation by nucleases. However, as synthetic entities, LNPs face challenges that alter their therapeutic efficacy and safety concerns. Toxicity/Reactogenicity/Immunogenicity: This review provides a comprehensive overview of the latest advancements in LNP research, focusing on preclinical safety assessments encompassing toxicity, reactogenicity, and immunogenicity. Summary and Outlook: Additionally, it outlines potential strategies for addressing these challenges and offers insights into future research directions for enhancing the application of LNPs in mRNA therapeutics.

Application of proton density fat fraction imaging in risk stratification of prostate cancer.

Background: Prostate cancer (PCa) as one of the most prevalent malignancies in men. We introduced a non-invasive quantitative measurement of intraprostatic fat content based on magnetic resonance proton density fat fraction (PDFF) imaging. The study aims to determine the fat fraction (FF) of PCa using proton density magnetic resonance imaging (MRI), gather clinical and routine MRI characteristics, and identify risk factors for high-risk PCa through multifactorial logistic regression. Methods: Clinical and imaging data from 191 pathologically confirmed PCa patients were collected. Patients were stratified based on Gleason score (GS), with 63 in the intermediate- and low-risk group (GS =3+3, 3+4) and 128 in the high-risk group (GS ≥4+3). All patients underwent routine prostate MRI and FF imaging. Clinical and imaging data related to PCa were analyzed, including age, body mass index (BMI), prostate volume (PV) measured by MRI, smoking history, alcohol history, diabetes history, serum prostate-specific antigen (PSA) level, apparent diffusion coefficient (ADC) value, T2 signal intensity (T2SI), Prostate Imaging Reporting and Data System 2.1 (PI-RADS 2.1) score, GS, lesion FF, whole gland FF, periprostatic fat thickness (PPFT), and subcutaneous fat thickness (SFT). Independent risk factors for stratifying PCa risk were identified through multivariate logistic regression analysis, and a predictive model was established. Receiver operating characteristic (ROC) curve analysis was conducted for visual analysis. Results: Significant differences were found in BMI, PV, PSA, tumor ADC value, standard T2SI, PI-RADS score, lesion FF, and PPFT between low- and medium-risk and high-risk groups (P<0.05). No significant differences were observed in age, smoking history, drinking history, diabetes history, and SFT between the two groups (P>0.05). GS correlated significantly with FF (ρ=0.6, P<0.001), PSA (ρ=0.432, P<0.001), ADC value (ρ=-0.379, P<0.001), and PI-RADS (ρ=0.366, P<0.001). Multiple logistic regression analysis revealed that an increase in FF, a PI-RADS score increase of 5 points, and a decrease in ADC value and PV were independent predictors of high-risk PCa (P<0.05). The ROC curve showed that the best cut-off value for the model was 0.67, with an area under the curve (AUC) of 0.907, sensitivity of 78.1%, and specificity of 88.9%. Conclusions: The FF of PCa determined by proton density MRI is significantly associated with GS, serving as an independent predictor of high-risk PCa.

An in situ combustion carbon deposit diagnostic instrument based on Raman microscopy.

Carbon deposit of the aero-engine combustor, resulting from incomplete combustion and fuel pyrolysis, can cause nozzle blockage, fuel consumption increase, power decrease, and even flight unsafety. In this work, an in situ combustion carbon deposit diagnostic instrument is developed to reveal the crystalline structure and the changes under real combustion conditions. The instrument integrates the in situ microscopic Raman technique and the combustion system. The burner is characterized by a sloping tip, making it possible to observe the coke from the side view. The burner is installed to the optical positioning stage by a specially made adapter so that the relative location is fixed and it is possible to observe the carbon deposit from the ignition. The carbon deposit of acetylene/air diffusion jet flame was studied. A 50× objective lens was used to collect the Raman scattering signal of carbon deposits continuously 30 s after ignition. A five-band model was used to fit the Raman spectra. The time-resolved information was calculated, including the normalized total area, area proportion, peak ratio, and crystalline size. The results show that the carbon deposit of acetylene flames with different velocities presents different tendencies of formation and degree of graphitization, which is attributed to the influence of temperature and flow. The performance of this system is evaluated quantitatively. The signal-to-noise ratio of Raman spectra of carbon deposits ranges from 6.4 to 28.9. This work provides an in situ method to analyze the dynamic change of carbon deposit on the burner, and further work is needed to reveal the mechanism.

Gut microbiota alterations in adolescent idiopathic scoliosis: a comparison study with healthy control and congenital scoliosis.

PURPOSE: This study aims to compare the composition of GM isolated from individuals with AIS or congenital scoliosis (CS) and age-matched control (Ctr). METHODS: A total of 48 patients with AIS, 24 patients with CS, and 31 healthy individuals were recruited as the discovery cohort, and 9 pairs of siblings where one was affected by AIS were recruited as the validation cohort. The GM profile was determined with 16S rRNA sequencing, and the alpha-diversity and beta-diversity metrics were performed with Mothur. Linear discriminant analysis (LDA) analysis was performed to identify the enriched species. RESULTS: The α diversity (Chao1 index) was significantly lower in AIS patients with low BMI (< 18.5) than those with normal BMI. The PcoA analysis showed a trend of clustering of GM in AIS compared to that in Ctr and CS groups (r2 = 0.0553, p = 0.001). METASTAT analysis showed Cellulomonadaceae was significantly enriched in AIS groups compared to CS and Ctr. LDA analysis showed 9 enriched species in AIS patients. Compared to Ctr, two species including Hungatella genus and Bacteroides fragilis were significantly enriched, while the Firmicutes versus Bacteroidetes (F/B) ratio and the Ruminococcus genus were significantly decreased in AIS but not CS groups. The significantly reduced F/B ratio and Ruminococcus genus in AIS were replicated in the validation cohort. CONCLUSIONS: Our study elucidated an association between low BMI and GM diversity in AIS patients. The reduced F/B ratio and Ruminococcus genus in AIS patients were identified and validated in 9 pairs of AIS patients and their unaffected siblings. Our pilot results may help understand the anthropometric discrepancy in these patients and support a possible role of GM in the pathogenesis of AIS.

Omics research in atherosclerosis.

Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.

Longitudinal assessment of quality of life indicators and prognosis in esophageal cancer patients with curative resection.

Background: The relationship between quality of life and survival outcomes in esophageal cancer patients following curative resection is not well established. This study aimed to longitudinally assess quality of life indicators and their association with overall survival (OS) in these patients. Methods: A total of 232 patients were included in the study, and their quality of life was prospectively assessed at different time points using the European Organisation for Research and Treatment of Cancer (EORTC) 30-item core quality of life questionnaire (QLQ-C30) and the disease-specific esophageal module (QLQ-OES18). The scores of QLQ indicators at each time point were summarized, and changes in postoperative assessment were compared with preoperative assessments. The association of deterioration in certain indicators with OS was evaluated at each time point using Cox univariable analysis. Further confirmation of independent variables was carried out using Cox multivariable analysis. Results: The study cohort comprised 62 females (26.7%), and 113 patients (48.7%) aged over 60 years. The median follow-up time was 80 months (range, 8-118 months). At 24 months after discharge, patients reported improvements in role function, fatigue, cognition function, emotional function, social function, insomnia, appetite loss, nausea and vomiting, constipation, financial status, trouble swallowing saliva, and pain related to esophageal cancer. However, physical function, dyspnea, diarrhea, global health status, choking when swallowing, trouble talking, and reflux remained compromised. Multivariable regression analysis revealed deterioration in role function, emotional function, and coughing difficulty at 6 months, and dyspnea, pain, and cognitive function at 24 months post-discharge were identified as independent prognostic factors for OS. Conclusions: Our findings underscore the importance of monitoring quality of life indicators in esophageal cancer patients as they may significantly influence survival outcomes. The identification of specific quality of life indicators as prognostic factors highlights the need for a patient-centered approach in clinical practice to enhance care and potentially improve survival.

Two-dimensional non-Abelian Thouless pump.

Non-Abelian Thouless pumps are periodically driven systems designed by the non-Abelian holonomy principle, in which quantized transport of degenerate eigenstates emerges, exhibiting noncommutative features such that the outcome depends on the pumping sequence. The study of non-Abelian Thouless pump is currently restricted to 1D systems, while extending it to higher-dimensional systems will not only provide effective means to probe non-Abelian physics in high-dimensional topological systems, but also expand the dimension and type of associated non-Abelian geometric phase matrix for potential applications. Here, we propose the design and experimental realization of 2D non-Abelian Thouless pumps on a photonic chip with 2D photonic waveguide arrays, where degenerate photonic modes are topologically pumped simultaneously along two real-space directions. We reveal the associated non-Abelian group and experimentally demonstrate the non-Abelian feature by measuring the pumping sequence dependent output. The proposed 2D non-Abelian Thouless pump shows promising applications for robust optical interconnections and optical computing.

PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy.

Proteins perform different biological functions through binding with various molecules which are mediated by a few key residues and accurate prediction of such protein binding residues (PBRs) is crucial for understanding cellular processes and for designing new drugs. Many computational prediction approaches have been proposed to identify PBRs with sequence-based features. However, these approaches face two main challenges: (1) these methods only concatenate residue feature vectors with a simple sliding window strategy, and (2) it is challenging to find a uniform sliding window size suitable for learning embeddings across different types of PBRs. In this study, we propose one novel framework that could apply multiple types of PBRs Prediciton task through Multi-scale Sequence-based Feature Fusion (PMSFF) strategy. Firstly, PMSFF employs a pre-trained language model named ProtT5, to encode amino acid residues in protein sequences. Then, it generates multi-scale residue embeddings by applying multi-size windows to capture effective neighboring residues and multi-size kernels to learn information across different scales. Additionally, the proposed model treats protein sequences as sentences, employing a bidirectional GRU to learn global context. We also collect benchmark datasets encompassing various PBRs types and evaluate our PMSFF approach to these datasets. Compared with state-of-the-art methods, PMSFF demonstrates superior performance on most PBRs prediction tasks.

Predicting postoperative mechanical complications with the ethnicity-adjusted global alignment and proportion score in degenerative scoliosis: does paraspinal muscle degeneration matter?

BACKGROUND: The global alignment and proportion (GAP) score was developed to predict mechanical complications (MCs) after adult spinal deformity surgery but showed limited sensitivity in the Asian population. Considering variations in sagittal parameters among different ethnic groups, our team developed the ethnicity-adjusted GAP score according to the spinopelvic parameters of 566 asymptomatic Chinese volunteers (C-GAP score). Notably, degenerative scoliosis (DS) patients with MCs following corrective surgery have more severe paraspinal muscle degeneration. For DS patients with various sagittal alignments, the unevenly distributed degeneration of paraspinal muscle may exert different influences on MC occurrence and largely affect the accuracy of the C-GAP score in clinical assessment. Therefore, incorporating paraspinal muscle degeneration indices within the C-GAP score may improve its accuracy in predicting MC occurrence. PURPOSE: We aimed to clarify the influence of paraspinal muscle degeneration on the C-GAP score predicting MC occurrence following DS surgery and modify the C-GAP score with paraspinal muscle degeneration parameters. STUDY DESIGN: A retrospective case-control study. SAMPLE SIZE: A total of 107 adult degenerative scoliosis patients. OUTCOME MEASURES: Demographic information, postoperative sagittal spinopelvic parameters, the GAP score, the C-GAP score, and paraspinal muscle degeneration parameters. METHODS: A total of 107 DS patients undergoing posterior spinal fusion surgery (≥4 vertebrae) with a minimum of 2 years follow-up (or experiencing MCs within 2 years) were retrospectively reviewed. Their C-GAP score was calculated based on our previous study and patients were divided into 3 C-GAP categories, "proportioned" (P), "moderately disproportioned" (MD), and "severely disproportioned" (SD). Relative cross-sectional area (cross-sectional area of muscle-disc ratio×100, rCSA) and fat infiltration rate, FI% at L1/2, L2/3, L3/4, and L4/5 discs were quantitatively evaluated using magnetic resonance imaging (MRI). In each C-GAP category, patients were additionally divided into the MC group and the non-MC group to analyze their paraspinal muscle degeneration. A multivariable logistic regression model consisting of the CSA-weighted average FI% (total FI%) and the C-GAP score, C-GAPM was constructed. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the predictability of the GAP score, the C-GAP score, FI%, and C-GAPM. This project was supported by the National Natural Science Foundation of China (No.82272545) and Special Fund of Science and Technology Plan of Jiangsu Province (No.BE2023658). RESULTS: For all 107 patients, FI% at L1/2, L2/3, L3/4, and L4/5 discs and the total FI% of the MC group (n=32) were significantly higher than those of the non-MC group (n=75). The MC rates of 3 original GAP categories, P, MD, and SD categories were 25.00% (6/24), 27.03%(10/37), and 34.78% (16/46) (χ2=0.944, p=.624). Based on the C-GAP score, the MC rates of the P, MD, and SD categories were 11.90% (5/42), 34.69% (17/49), and 62.50% (10/16), showing significant differences (χ2=15.137, p=.001). In the C-GAP MD category, compared with the non-MC group (n=32), the MC group (n=17) has a higher total FI% (26.16(22.95, 34.00) vs. 22.67(16.39, 27.37)), p=.029). A similar trend was identified in the C-GAP SD category (34.79±11.56 vs. 19.00±5.17, p=.007), but not in the C-GAP P category (25.09(22.82, 32.66) vs. 24.66(17.36, 28.63), p=.361). The AUC of the GAP score, the C-GAP score, the total FI%, and C-GAPM were respectively 0.601, 0.722, 0.716, and 0.772. CONCLUSIONS: Paraspinal muscle degeneration exerts a significant effect on the occurrence of MC in the C-GAP MD, SD instead of P category. The integration of paraspinal muscle FI% with the C-GAP score (C-GAPM) enables a more accurate prediction of MCs following DS surgery. Surgeons should pay adequate attention to paraspinal muscle degeneration during surgical planning and postoperative management for patients in the C-GAP MD and SD categories.

Spatial asymmetry of temporal ecological processes can shift in riverine macroinvertebrates responding to fluctuating climate conditions.

Exploring the temporal dynamics of biological communities can offer valuable insights into the underlying mechanisms driving changes in biodiversity in the context of short and long-term effects of climate fluctuations. However, an understanding of how temporal shifts in climatic fluctuations influence the spatial patterns of the temporary ecological processes remains unexplored. This study examined the relative importance of temporary deterministic and stochastic processes (i.e., the influence of environmental filtering compared to stochastic variation within the same community) on community dynamics across watersheds in 15 rivers of the European Iberian Peninsula using 21 years of data. This study was divided into two time periods (i.e., 1997-2006 and 2007-2017). The climatic differences between the periods included decreasing levels and heightened variability of precipitation. Additionally, there were declining minimum temperatures and rising maximum temperatures, accompanied by reduced fluctuations in both minimum and maximum temperatures. Water quality and its variations also occur along an elevation pattern and changed over the time period studied. Spatial patterns of the relative importance of the ecological processes shifted between the two decades. The significance of stochastic processes increased with elevation in the earlier period, although no clear elevation pattern emerged in the later period. At the same time, the importance of deterministic processes decreased with elevation in the earlier period, and there was no clear pattern of elevation in the later period. An understanding of the patterns in community dynamics existing at various elevations over time can lay the groundwork for predicting and mitigating the impacts of short-term climate changes on biodiversity and guide appropriate conservation actions.

Research on platform data security governance strategy based on three-party evolutionary game.

With the implementation of the overall national security concept, data security governance rises to a new strategic height. In this paper, for the incomplete status quo of digital service platforms, third-party testing organizations and government regulators in the construction of digital security, an evolutionary game model based on the above three parties is constructed. The model examines the strategic decision-making process, behavioral influences, and evolutionary stability of the three players, and is simulated and analyzed using MATLAB. The results show that the evolutionary system will reach the ideal stable state E ( 1 , 1 , 1 ) , which corresponds to the combination of strategies: providing high-quality products, refusing to rent-seeking, and strict regulation. In order to guide the evolving system to reach the ideal stable state, this study puts forward some policy recommendations in terms of establishing a data security assessment mechanism, collaborative technology governance, and optimizing the governance architecture.

Learning from Classic: DNA-Based Conditional Equilibrium Constant To Regulate Affinity "On-the-Fly" for Bioassays.

Artificial programming of affinity is beneficial to optimize responsiveness in biomolecules for various applications. In one classical theory, one comprehensive parameter, conditional equilibrium constant (K'EDTA), can accurately and quantitatively define the affinity of ethylene diamine tetraacetic acid (EDTA) for metal ions. Learning from the classic, we have proposed a novel DNA-based conditional equilibrium constant (K'DNA) to regulate DNA probes' affinity and response "on-the-fly", long after the probe design and synthesis. Artificial regulation of affinity over several magnitudes has been simply realized via short oligonucleotides with different lengths, concentrations, and combinations. The thermodynamic response can be quantitatively simulated by one DNA-based conditional equilibrium constant (K'DNA), acting as an analogue to the classical EDTA system. The proof of concept of affinity programming also allows improved discrimination of single-nucleotide variants as well as assaying ribonuclease and doxycycline in a homogeneous solution. Therefore, the theory of DNA-based conditional equilibrium constant (K'DNA) will enable to engineer versatile DNA switches with programmable affinity in assays and bionanotechnology.

Drug-target interaction prediction with collaborative contrastive learning and adaptive self-paced sampling strategy.

BACKGROUND: Drug-target interaction (DTI) prediction plays a pivotal role in drug discovery and drug repositioning, enabling the identification of potential drug candidates. However, most previous approaches often do not fully utilize the complementary relationships among multiple biological networks, which limits their ability to learn more consistent representations. Additionally, the selection strategy of negative samples significantly affects the performance of contrastive learning methods. RESULTS: In this study, we propose CCL-ASPS, a novel deep learning model that incorporates Collaborative Contrastive Learning (CCL) and Adaptive Self-Paced Sampling strategy (ASPS) for drug-target interaction prediction. CCL-ASPS leverages multiple networks to learn the fused embeddings of drugs and targets, ensuring their consistent representations from individual networks. Furthermore, ASPS dynamically selects more informative negative sample pairs for contrastive learning. Experiment results on the established dataset demonstrate that CCL-ASPS achieves significant improvements compared to current state-of-the-art methods. Moreover, ablation experiments confirm the contributions of the proposed CCL and ASPS strategies. CONCLUSIONS: By integrating Collaborative Contrastive Learning and Adaptive Self-Paced Sampling, the proposed CCL-ASPS effectively addresses the limitations of previous methods. This study demonstrates that CCL-ASPS achieves notable improvements in DTI predictive performance compared to current state-of-the-art approaches. The case study and cold start experiments further illustrate the capability of CCL-ASPS to effectively predict previously unknown DTI, potentially facilitating the identification of new drug-target interactions.