Clinical and genetic features in autosomal recessive bestrophinopathy in Chinese cohort.

PURPOSE: To provide a genotype and phenotype characterization of the BEST1 mutation in Chinese patients with autosomal recessive bestrophinopathy (ARB) through multimodal imaging and next-generation sequencing (NGS). METHODS: Seventeen patients from 17 unrelated families of Chinese origin with ARB were included in a retrospective cohort study. Phenotypic characteristics, including anterior segment features, were assessed by multimodal imaging. Multigene panel testing, involving 586 ophthalmic disease-associated genes, and Sanger sequencing were performed to identify disease-causing variants. RESULTS: Among 17 ARB patients, the mean follow-up was 15.65 months and average onset age was 30.53 years (range: 9-68). Best corrected visual acuity ranged from light perception to 0.8. EOG recordings showed a typically decreased Arden ratio in 12 patients, and a normal or slightly decreased Arden ratio in two patients. Anterior features included shallow anterior chambers (16/17), ciliary pronation (16/17), iris bombe (13/17), iridoschisis (2/17), iris plateau (1/17), narrow angles (16/17) and reduced axial lengths (16/17). Sixteen patients had multiple bilateral small, round, yellow vitelliform deposits distributed throughout the posterior pole, surrounding the optic disc. Initial diagnoses included angle-closure glaucoma (four patients), Best disease (three patients), and central serous chorioretinopathy secondary to choroidal neovascularization (CNV) (one patient), with the remainder diagnosed with ARB. Fourteen patients underwent preventive laser peripheral iridotomy, four of whom also received combined trabeculectomy and iridotomy in both eyes for uncontrolled intraocular pressure. One patient received intravitreal conbercept for CNV. Overall, 15 distinct disease-causing variants of BEST1 were identified, with 14 (82.35%) patients having missense mutations. Common mutations included p. Arg255-256 and p. Ala195Val (both 23.68%), with the most frequent sites in exons 7 and 5. CONCLUSIONS: This study provides a comprehensive characterization of anterior segment and genetic features in ARB, with a wide array of morphological abnormalities. Findings are relevant for refining clinical practices and genetic counseling and advancing pathogenesis research.

Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.

BACKGROUND: Acute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period. OBJECTIVE: Our study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF. METHODS: In our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of 'with readmission/mortality' and 'without readmission/mortality'. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation. RESULTS: In our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators-spanning peripheral immune cells and traditional clinical metrics-that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks. CONCLUSION: Our analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.

Identification of novel PD-1/PD-L1 small molecule inhibitors: virtual screening, synthesis and in vitro characterisation.

The PD-1/PD-L1 pathway is considered as one of the most promising immune checkpoints in tumour immunotherapy. However, researchers are faced with the inherent limitations of antibodies, driving them to pursue PD-L1 small molecule inhibitors. Virtual screening followed by experimental validation is a proven approach to discover active compounds. In this study, we employed multistage virtual screening methods to screen multiple compound databases to predict new PD-1/PD-L1 ligands. 35 compounds were proposed by combined analysis of fitness scores, interaction pattern and MM-GBSA binding affinities. Enzymatic assay confirmed that 10 out of 35 ligands were potential PD-L1 inhibitors, with inhibitory rate higher than 50% at the concentration of 30 µM. Among them, ZDS20 was identified as the most effective inhibitor with low micromolar activity (IC50 = 3.27 µM). Altogether, ZDS20 carrying novel scaffold was identified and could serve as a lead for the development of new classes of PD-L1 inhibitors.

ABA-mediated regulation of rice grain quality and seed dormancy via the NF-YB1-SLRL2-bHLH144 Module.

Abscisic acid (ABA) plays a crucial role in promoting plant stress resistance and seed dormancy. However, how ABA regulates rice quality remains unclear. This study identifies a key transcription factor SLR1-like2 (SLRL2), which mediates the ABA-regulated amylose content (AC) of rice. Mechanistically, SLRL2 interacts with NF-YB1 to co-regulate Wx, a determinant of AC and rice quality. In contrast to SLR1, SLRL2 is ABA inducible but insensitive to GA. In addition, SLRL2 exhibits DNA-binding activity and directly regulates the expression of Wx, bHLH144 and MFT2. SLRL2 competes with NF-YC12 for interaction with NF-YB1. NF-YB1 also directly represses SLRL2 transcription. Genetic validation supports that SLRL2 functions downstream of NF-YB1 and bHLH144 in regulating rice AC. Thus, an NF-YB1-SLRL2-bHLH144 regulatory module is successfully revealed. Furthermore, SLRL2 regulates rice dormancy by modulating the expression of MFT2. In conclusion, this study revealed an ABA-responsive regulatory cascade that functions in both rice quality and seed dormancy.

Impact of inter-lesion distance and first-pass isolation on outcomes of pulmonary venous isolation for paroxysmal atrial fibrillation.

BACKGROUND: Contiguity of ablation lesions is a critical determinant of success for paroxysmal atrial fibrillation (PAF) ablation. Evidence supports maintaining an inter-lesional distance (ILD) ≤ 6 mm during pulmonary venous isolation (PVI). Meanwhile, first-pass isolation (FPI) on PVI outcome in follow-up was not deeply studied. The impact of ILD and FPI on PAF ablation outcomes was investigated. METHODS: Consecutive PAF patients who underwent first-time antral PVI were recruited. Coordinates of ablation points were extracted from the electro-anatomical mapping system and analyzed using custom-developed software to determine the ILD. A gap is defined as ILD greater than 6 mm. FPI was defined as the achievement of PVI by encircling the ipsilateral veins while simultaneously recording their electrical activity using a multipolar catheter. The primary endpoint was freedom from documented atrial arrhythmias including AF, atrial tachycardia (AT), or atrial flutter (AFL) lasting longer than 30 s during follow-up. RESULTS: A total of 105 patients underwent first-time antral PVI. During 13.3 ± 0.6 months of follow-up, atrial arrhythmias recurrence was noted in 22.9% of the patients. Atrial arrhythmia recurrence was significantly higher in patients with more gaps (> 2) (37.0% versus 11.9%, P < 0.01), and the number of gaps was an independent predictor of AF/AT/AFL recurrence. (Hazard ratio [HR] 1.20, 95% CI 1.03-1.40, P = 0.02). The group with FPI for at least one ipsilateral pair of PVs exhibited a decreased number of gaps (2.0 versus 7.0, P < 0.01) and demonstrated a significant correlation with a reduction of recurrence (HR 0.26, 95% CI 0.09-0.71, P = 0.01). Among 16 patients who underwent repeat ablation, the number of gaps during the index PVI was associated with PV reconnection (PVR) (P < 0.01). CONCLUSIONS: Gaps created during PVI are a modifiable determinant of AF/AT/AFL recurrence, and avoidance of gaps is crucial to improve clinical outcomes of PAF ablation. In addition, FPI exhibited a strong predictive capability for clinical success in patients with PAF.

Eu(III) functionalized ZnMOF based efficient dual-emission sensor integrated with self-calibrating logic gate for intelligent detection of epinephrine.

The rapid detection of epinephrine (EPI) in serum holds immense importance in the early disease diagnosis and regular monitoring. On the basis of the coordination post-synthetic modification (PSM) strategy, a Eu3+ functionalized ZnMOF (Eu3+@ZnMOF) was fabricated by anchoring the Eu3+ ions within the microchannels of ZnMOF as secondary luminescent centers. Benefiting from two independent luminescent centers, the prepared Eu3+@ZnMOF shows great potential as a multi-signal self-calibrating luminescent sensor in visually and efficiently detecting serum EPI levels, with high reliability, fast response time, excellentrecycleability, and low detection limits of 17.8 ng/mL. Additionally, an intelligent sensing system was designed in accurately and reliably detecting serum EPI levels, based on the designed self-calibrating logic gates. Furthermore, the possible sensing mechanisms were elucidated through theoretical calculations as well as spectral overlaps. This work provides an effective and promising strategy for developing MOFs-based self-calibrating intelligent sensing platforms to detect bioactive molecules in bodily fluids.

Potential Underestimation of Left Ventricular Mechanical Dyssynchrony in Dyssynchrony and Outcomes Assessment.

Objective: Left ventricular (LV) mechanical dyssynchrony (LVMD) is fundamental to the progression of heart failure and ventricular remodeling. The status of LVMD in different patterns of bundle branch blocks (BBB) is unclear. In this study, we analyzed the relationship between LVMD and left ventricular systolic dysfunction using real-time three-dimensional echocardiography (RT-3DE). Methods: RT-3DE and conventional two-dimensional echocardiography were performed on 68 patients with left bundle branch block (LBBB group), 106 patients with right bundle branch block (RBBB group), and 103 patients without BBB (Normal group). The RT-3DE data sets provided time-volume analysis for global and segmental LV volumes. The LV systolic dyssynchrony index (LVSDI) was calculated using the standard deviation (SD) and maximal difference (Dif) of time to minimum segmental volume (tmsv) for LV segments adjusted by the R-R interval. LVMD was considered if the LVSDI (Tmsv-16-SD) was greater than or equal to 5%. Results: LVSDI is negatively and significantly correlated with left ventricular ejection fraction (LVEF), but not with BBB or QRS duration. The proportion of LVMD in the LBBB, RBBB, and Normal group was 30.88%, 28.30%, and 25.24%, respectively, and there was no significant difference. Conclusion: In dilated cardiomyopathy, LVMD is more closely related to LVEF reduction than QRS morphology and duration.

RGA1 regulates grain size, rice quality and seed germination in the small and round grain mutant srg5.

BACKGROUND: Generating elite rice varieties with high yield and superior quality is the main goal of rice breeding programs. Key agronomic traits, including grain size and seed germination characteristics, affect the final yield and quality of rice. The RGA1 gene, which encodes the α-subunit of rice G-protein, plays an important role in regulating rice architecture, seed size and abiotic stress responses. However, whether RGA1 is involved in the regulation of rice quality and seed germination traits is still unclear. RESULTS: In this study, a rice mutant small and round grain 5 (srg5), was identified in an EMS-induced rice mutant library. Systematic analysis of its major agronomic traits revealed that the srg5 mutant exhibited a semi-dwarf plant height with small and round grain and reduced panicle length. Analysis of the physicochemical properties of rice showed that the difference in rice eating and cooking quality (ECQ) between the srg5 mutant and its wild-type control was small, but the appearance quality was significantly improved. Interestingly, a significant suppression of rice seed germination and shoot growth was observed in the srg5 mutant, which was mainly related to the regulation of ABA metabolism. RGA1 was identified as the candidate gene for the srg5 mutant by BSA analysis. A SNP at the splice site of the first intron disrupted the normal splicing of the RGA1 transcript precursor, resulting in a premature stop codon. Additional linkage analysis confirmed that the target gene causing the srg5 mutant phenotype was RGA1. Finally, the introduction of the RGA1 mutant allele into two indica rice varieties also resulted in small and round rice grains with less chalkiness. CONCLUSIONS: These results indicate that RGA1 is not only involved in the control of rice architecture and grain size, but also in the regulation of rice quality and seed germination. This study sheds new light on the biological functions of RGA1, thereby providing valuable information for future systematic analysis of the G-protein pathway and its potential application in rice breeding programs.

Extraction, purification, structural features, modifications, bioactivities, structure-activity relationships, and applications of polysaccharides from garlic: A review.

Garlic is a common vegetable and spice in people's daily diets, in which garlic polysaccharide (GP) is one of the most important active components with a variety of benefits, such as antioxidant, immune-enhancing, anti-inflammatory, liver-protective and bowel-regulating properties. >20 types of GPs, mainly crude polysaccharides, have been identified. However, the exact chemical composition of GPs or the mechanism underlying their pharmacological activity is still not fully understood. The extraction and purification methods of GPs are compared in this review while providing detailed information on their structural features, identification methods, major biological activities, mechanisms of actions, structural modifications, structure-activity relationships as well as potential applications. Finally, the limitations of GP research and future issues that need to be addressed are discussed in this review. GPs are widely recognized as substances with great potential in the pharmaceutical and food industries. Therefore, this review aims to provide a comprehensive summary of the latest research progresses in the field of GPs, together with scientific insights and a theoretical support for the development of GPs in research and industrialization.

Novel Scaffold Agonists of the α2A Adrenergic Receptor Identified via Ensemble-Based Strategy.

The α2A adrenergic receptor (α2A-AR) serves as a critical molecular target for sedatives and analgesics. However, α2A-AR ligands with an imidazole ring also interact with an imidazoline receptor as well as other proteins and lead to undesirable effects, motivating us to develop more novel scaffold α2A-AR ligands. For this purpose, we employed an ensemble-based ligand discovery strategy, integrating long-term molecular dynamics (MD) simulations and virtual screening, to identify new potential α2A-AR agonists with novel scaffold. Our results showed that compounds SY-15 and SY-17 exhibited significant biological effects in the preliminary evaluation of protein kinase A (PKA) redistribution assays. They also reduced levels of intracellular cyclic adenosine monophosphate (cAMP) in a dose-dependent manner. Upon treatment of the cells with 100 µM concentrations of SY-15 and SY-17, there was a respective decrease in the intracellular cAMP levels by 63.43% and 53.83%. Subsequent computational analysis was conducted to elucidate the binding interactions of SY-15 and SY-17 with the α2A-AR. The binding free energies of SY-15 and SY-17 calculated by MD simulations were -45.93 and -71.97 kcal/mol. MD simulations also revealed that both compounds act as bitopic agonists, occupying the orthosteric site and a novel exosite of the receptor simultaneously. Our findings of integrative computational and experimental approaches could offer the potential to enhance ligand affinity and selectivity through dual-site occupancy and provide a novel direction for the rational design of sedatives and analgesics.

Role of electroanatomical mapping-guided superior vena cava isolation in paroxysmal atrial fibrillation patients without provoked superior vena cava triggers: a randomized controlled study.

AIMS: Data about whether empirical superior vena cava (SVC) isolation (SVCI) improves the success rate of paroxysmal atrial fibrillation (PAF) are conflicting. This study sought to first investigate the characteristics of SVC-triggered atrial fibrillation and secondly investigate the impact of electroanatomical mapping-guided SVCI, in addition to circumferential pulmonary vein isolation (CPVI), on the outcome of PAF ablation in the absence of provoked SVC triggers. METHODS AND RESULTS: A total of 130 patients undergoing PAF ablation underwent electrophysiological studies before ablation. In patients for whom SVC triggers were identified, SVCI was performed in addition to CPVI. Patients without provoked SVC triggers were randomized in a 1:1 ratio to CPVI plus SVCI or CPVI only. The primary endpoint was freedom from any documented atrial tachyarrhythmias lasting over 30 s after a 3-month blanking period without anti-arrhythmic drugs at 12 months after ablation. Superior vena cava triggers were identified in 30 (23.1%) patients with PAF. At 12 months, 93.3% of those with provoked SVC triggers who underwent CPVI plus SVCI were free from atrial tachyarrhythmias. In patients without provoked SVC triggers, SVCI, in addition to CPVI, did not increase freedom from atrial tachyarrhythmias (87.9 vs. 79.6%, log-rank P = 0.28). CONCLUSION: Electroanatomical mapping-guided SVCI, in addition to CPVI, did not increase the success rate of PAF ablation in patients who had no identifiable SVC triggers. REGISTRATION: ChineseClinicalTrials.gov: ChiCTR2000034532.

Study on the Hydrogen Effect and Interface/Border Traps of a Depletion-Mode AlGaN/GaN High-Electron-Mobility Transistor with a SiNx Gate Dielectric at Different Temperatures.

In this study, the electrical characteristics of depletion-mode AlGaN/GaN high-electron-mobility transistors (HEMTs) with a SiNx gate dielectric were tested under hydrogen exposure conditions. The experimental results are as follows: (1) After hydrogen treatment at room temperature, the threshold voltage VTH of the original device was positively shifted from -16.98 V to -11.53 V, and the positive bias of threshold was 5.45 V. When the VDS was swept from 0 to 1 V with VGS of 0 V, the IDS was reduced by 25% from 9.45 A to 7.08 A. (2) Another group of original devices with identical electrical performance, after the same duration of hydrogen treatment at 100 °C, exhibited a reverse shift in threshold voltage with a negative threshold shift of -0.91 V. The output characteristics were enhanced, and the saturation leakage current was increased. (3) The C-V method and the low-frequency noise method were used to investigate the effect of hydrogen effect on the device interface trap and border trap, respectively. It was found that high-temperature hydrogen conditions can passivate the interface/border traps of SiNx/AlGaN, reducing the density of interface/border traps and mitigating the trap capture effect. However, in the room-temperature hydrogen experiment, the concentration of interface/border traps increased. The research findings in this paper provide valuable references for the design and application of depletion-mode AlGaN/GaN HEMT devices.

Collision-attachment simulation of membrane fouling by oppositely and similarly charged colloids.

The fouling propensity of oppositely charged colloids (OCC) and similarly charged colloids (SCC) on reverse osmosis (RO) and nanofiltration (NF) membranes are systematically investigated using a developed collision-attachment approach. The probability of successful colloidal attachment (i.e., attachment efficiency) is modelled by Boltzmann energy distribution, which captures the critical roles of colloid-colloid/membrane interaction and permeate drag. Our simulations highlight the important effects of ionic strength Is, colloidal size dp and initial flux J0 on combined fouling. In a moderate condition (e.g., Is =10 mM, dp=50 nm and J0= 100 L/m2h), OCC mixtures shows more severe fouling compared to the respective single foulant owing to electrostatic neutralization. In contrast, the flux loss of SCC species falls between those of the two single foulants but more closely resembles that of the single low-charged colloids due to its weak electrostatic repulsion. Increased ionic strength Is leads to less severe fouling for OCC but more severe fouling for SCC, as a result of the suppressed electrostatic attraction/repulsion. At a high Is (e.g., 3-5 M), all the single and mixed systems show the identical pseudo-stable flux Js. Small colloidal size leads to the drag-controlled condition, where severe fouling occurs for both single and mixed foulants. On the contrary, better flux stability appears at greater dp for both individual and mixed species, thanks to the increasingly dominated role of energy barrier and thus lowered attachment efficiency. Furthermore, higher J0 above limiting flux exerts greater permeate drag, leading to elevated attachment efficiency, and thus more flux losses for both OCC and SCC. Our modelling gains deep insights into the role of energy barrier, permeate drag, and attachment efficiency in governing combined fouling, which provides crucial guidelines for fouling reduction in practical engineering.

Discovery of LAH-1 as potent c-Met inhibitor for the treatment of non-small cell lung cancer.

ABSTRCTDysregulated HGF/c-Met pathway has been implicated in multiple human cancers and has become an attractive target for cancer intervention. Herein, we report the discovery of N-(3-fluoro-4-((2-(3-hydroxyazetidine-1-carboxamido)pyridin-4-yl)oxy)phenyl)-1-(4-fluorophenyl)-4-methyl-6-oxo-1,6-dihydropyridazine-3-carboxamide (LAH-1), which demonstrated nanomolar MET kinase activity as well as desirable antiproliferative activity, especially against EBC-1 cells. Mechanism studies confirmed the effects of LAH-1 on modulation of HGF/c-Met pathway, induction of cell apoptosis, inhibition on colony formation as well as cell migration and invasion. In addition, LAH-1 also showed desirable in vitro ADME properties as well as acceptable in vivo PK parameters. The design, synthesis, and characterisation of LAH-1 are described herein.

Improving antibody optimization ability of generative adversarial network through large language model.

Generative adversarial networks (GANs) have successfully generated functional protein sequences. However, traditional GANs often suffer from inherent randomness, resulting in a lower probability of obtaining desirable sequences. Due to the high cost of wet-lab experiments, the main goal of computer-aided antibody optimization is to identify high-quality candidate antibodies from a large range of possibilities, yet improving the ability of GANs to generate these desired antibodies is a challenge. In this study, we propose and evaluate a new GAN called the Language Model Guided Antibody Generative Adversarial Network (AbGAN-LMG). This GAN uses a language model as an input, harnessing such models' powerful representational capabilities to improve the GAN's generation of high-quality antibodies. We conducted a comprehensive evaluation of the antibody libraries and sequences generated by AbGAN-LMG for COVID-19 (SARS-CoV-2) and Middle East Respiratory Syndrome (MERS-CoV). Results indicate that AbGAN-LMG has learned the fundamental characteristics of antibodies and that it improved the diversity of the generated libraries. Additionally, when generating sequences using AZD-8895 as the target antibody for optimization, over 50% of the generated sequences exhibited better developability than AZD-8895 itself. Through molecular docking, we identified 70 antibodies that demonstrated higher affinity for the wild-type receptor-binding domain (RBD) of SARS-CoV-2 compared to AZD-8895. In conclusion, AbGAN-LMG demonstrates that language models used in conjunction with GANs can enable the generation of higher-quality libraries and candidate sequences, thereby improving the efficiency of antibody optimization. AbGAN-LMG is available at http://39.102.71.224:88/.

Physical activity, sleep disorders, and type of work in the prevention of cognitive function decline in patients with hypertension.

BACKGROUND: Hypertensive patients are likelier to have cognitive function decline (CFD). This study aimed to explore physical activity level, sleep disorders, and type of work that influenced intervention effects on cognitive function decline in hypertensive patients and to establish a decision tree model to analyze their predictive significance on the incidence of CFD in hypertensive patients. METHODS: This cross-sectional study recruited patients with essential hypertension from several hospitals in Shandong Province from May 2022 to December 2022. Subject exclusion criteria included individuals diagnosed with congestive heart failure, valvular heart disease, cardiac surgery, hepatic and renal dysfunction, and malignancy. Recruitment is through multiple channels such as hospital medical and surgical outpatient clinics, wards, and health examination centers. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Moreover, we obtained information on the patients' type of work through a questionnaire and their level of physical activity through the International Physical Activity Questionnaire (IPAQ). RESULTS: The logistic regression analysis results indicate that sleep disorder is a significant risk factor for CFD in hypertension patients(OR:1.85, 95%CI:[1.16,2.94]), mental workers(OR:0.12, 95%CI: [0.04,0.37]) and those who perform both manual and mental workers(OR: 0.5, 95%CI: [0.29,0.86]) exhibit protective effects against CFD. Compared to low-intensity, moderate physical activity(OR: 0.53, 95%CI: [0.32,0.87]) and high-intensity physical activity(OR: 0.26, 95%CI: [0.12,0.58]) protects against CFD in hypertension patients. The importance of predictors in the decision tree model was ranked as follows: physical activity level (54%), type of work (27%), and sleep disorders (19%). The area under the ROC curves the decision tree model predicted was 0.72 [95% CI: 0.68 to 0.76]. CONCLUSION: Moderate and high-intensity physical activity may reduce the risk of developing CFD in hypertensive patients. Sleep disorders is a risk factor for CFD in hypertensive patients. Hypertensive patients who engage in mental work and high-intensity physical activity effectively mitigate the onset of CFD in hypertensive patients.

BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network.

BACKGROUND: Automatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining. Currently, stacking various classification networks on pre-trained language models to perform fine-tuning is a common framework to end-to-end solve the biomedical relation extraction (BioRE) problem. However, the sequence-based pre-trained language models underutilize the graphical topology of language to some extent. In addition, sequence-oriented deep neural networks have limitations in processing graphical features. RESULTS: In this paper, we propose a novel method for sentence-level BioRE task, BioEGRE (BioELECTRA and Graph pointer neural net-work for Relation Extraction), aimed at leveraging the linguistic topological features. First, the biomedical literature is preprocessed to retain sentences involving pre-defined entity pairs. Secondly, SciSpaCy is employed to conduct dependency parsing; sentences are modeled as graphs based on the parsing results; BioELECTRA is utilized to generate token-level representations, which are modeled as attributes of nodes in the sentence graphs; a graph pointer neural network layer is employed to select the most relevant multi-hop neighbors to optimize representations; a fully-connected neural network layer is employed to generate the sentence-level representation. Finally, the Softmax function is employed to calculate the probabilities. Our proposed method is evaluated on three BioRE tasks: a multi-class (CHEMPROT) and two binary tasks (GAD and EU-ADR). The results show that our method achieves F1-scores of 79.97% (CHEMPROT), 83.31% (GAD), and 83.51% (EU-ADR), surpassing the performance of existing state-of-the-art models. CONCLUSION: The experimental results on 3 biomedical benchmark datasets demonstrate the effectiveness and generalization of BioEGRE, which indicates that linguistic topology and a graph pointer neural network layer explicitly improve performance for BioRE tasks.

Quantifying the ecological carrying capacity of grasslands in Inner Mongolia.

Quantifying the ecological carrying capacity has emerged as a crucial factor for maintaining ecosystem stability for sustainable development in vulnerable eco-regions. Here, we propose a new framework for ecological carrying capacity quantification suitable for vulnerable eco-regions. We applied this framework to calculate the ecological carrying capacity of Inner Mongolia from 1987-2015 and used a geographical detector to identify the driving factors behind spatial heterogeneity. Our results revealed the following. (1) The above-ground net primary production (ANPP) required to support the ecosystem service of soil conservation (ANPPSC) decreased from northeast to southwest, whereas the distribution pattern of ANPP required to support the ecosystem service of sand fixation (ANPPSF) exhibited a contrary trend. The average annual ANPP required to support the ecosystem service of natural regeneration (ANPPNR) in Inner Mongolia from 1987 to 2015 was 101.27 gCm-2year-1, revealing a similar spatial distribution with ANPP. (2) The total ecological carrying capacity of Inner Mongolian grassland was 78.52 million sheep unit hm-2. The regions with insufficient provisioning service capability accounted for 4.18% of the total area, primarily concentrated in the east and northwest. (3) The average optimal livestock number for grasslands in Inner Mongolia was 1.59 sheep unit hm-2 from 1987-2015, ranging from 0.77 to 1.69 sheep unit hm-2 across different zones. The average ecological carrying capacity of the cold temperate humid, medium-temperate arid, and warm temperate semi-humid regions was less than 1.08 sheep unit m-2, suggesting a need to prohibit grazing in these areas. (4) The primary influencing factors affecting ecological carrying capacity distribution were normalized difference vegetation index (NDVI), precipitation, and soil type. The framework developed herein can help identify sustainable development potential from the ecosystem service perspective and effectively contribute to decision-making in grassland ecosystem management.

BERT2DAb: a pre-trained model for antibody representation based on amino acid sequences and 2D-structure.

Prior research has generated a vast amount of antibody sequences, which has allowed the pre-training of language models on amino acid sequences to improve the efficiency of antibody screening and optimization. However, compared to those for proteins, there are fewer pre-trained language models available for antibody sequences. Additionally, existing pre-trained models solely rely on embedding representations using amino acids or k-mers, which do not explicitly take into account the role of secondary structure features. Here, we present a new pre-trained model called BERT2DAb. This model incorporates secondary structure information based on self-attention to learn representations of antibody sequences. Our model achieves state-of-the-art performance on three downstream tasks, including two antigen-antibody binding classification tasks (precision: 85.15%/94.86%; recall:87.41%/86.15%) and one antigen-antibody complex mutation binding free energy prediction task (Pearson correlation coefficient: 0.77). Moreover, we propose a novel method to analyze the relationship between attention weights and contact states of pairs of subsequences in tertiary structures. This enhances the interpretability of BERT2DAb. Overall, our model demonstrates strong potential for improving antibody screening and design through downstream applications.