Development and validation of a Multi-Causal investigation and discovery framework for knowledge harmonization (MINDMerge): A case study with acute kidney injury risk factor discovery using electronic medical records.

OBJECTIVE: Accurate diagnoses and personalized treatments in medicine rely on identifying causality. However, existing causal discovery algorithms often yield inconsistent results due to distinct learning mechanisms. To address this challenge, we introduce MINDMerge, a multi-causal investigation and discovery framework designed to synthesize causal graphs from various algorithms. METHODS: MINDMerge integrates five causal models to reconcile inconsistencies arising from different algorithms. Employing credibility weighting and a novel cycle-breaking mechanism in causal networks, we initially developed and tested MINDMerge using three synthetic networks. Subsequently, we validated its effectiveness in discovering risk factors and predicting acute kidney injury (AKI) using two electronic medical records (EMR) datasets, eICU Collaborative Research Database and MIMIC-III Database. Causal reasoning was employed to analyze the relationships between risk factors and AKI. The identified causal risk factors of AKI were used in building a prediction model, and the prediction model was evaluated using the area under the receiver operating characteristics curve (AUC) and recall. RESULTS: Synthetic data experiments demonstrated that our model outperformed significantly in capturing ground-truth network structure compared to other causal models. Application of MINDMerge on real-world data revealed direct connections of pulmonary disease, hypertension, diabetes, x-ray assessment, and BUN with AKI. With the identified variables, AKI risk can be inferred at the individual level based on established BNs and prior information. Compared against existing benchmark models, MINDMerge maintained a higher AUC for AKI prediction in both internal (AUC: 0.832) and external network validations (AUC: 0.861). CONCLUSION: MINDMerge can identify causal risk factors of AKI, serving as a valuable diagnostic tool for clinical decision-making and facilitating effective intervention.

Recent advances in lung cancer organoid (tumoroid) research (Review).

Lung cancer is the most critical type of malignant tumor that threatens human health. Traditional preclinical models have certain defects; for example, they cannot accurately reflect the characteristics of lung cancer and their development is costly and time-consuming. Through self-organization, cancer stem cells (CSCs) generate cancer organoids that have a structure similar to that of lung cancer tissues, overcoming to some extent the aforementioned challenges, thus enabling them to have broader application prospects. Lung cancer organoid (LCO) development methods can be divided into three broad categories based on the source of cells, which include cell lines, patient-derived xenografts and patient tumor tissue/pleural effusion. There are 17 different methods that have been described for the development of LCOs. These methods can be further merged into six categories based on the source of cells, the pre-treatment method used, the composition of the medium and the culture scaffold. These categories are: i) CSCs induced by defined transcription factors; ii) suspension culture; iii) relative optimal culture medium; iv) suboptimal culture medium; v) mechanical digestion and suboptimal culture medium; and vi) hydrogel scaffold. In the current review, the advantages and disadvantages of each of the aforementioned methods are summarized, and references for supporting studies are cited.

Patient, family, and community factors associated with medication adherence among people with hypertension or diabetes: A cross-sectional analysis.

Background: While suboptimal medication adherence remains an obstacle to the management of hypertension and diabetes in China, few studies have investigated associated factors with medication adherence on different dimensions simultaneously. Objective: To systematically examine associated patient, family, and community factors with suboptimal medication adherence among people with hypertension and/or type 2 diabetes in China. Methods: The study stratified a random sample of 622 adults aged 45 years or older with hypertension and/or type 2 diabetes from three southeast cities in China in 2019. Trained interviewers used the Morisky Green Levine Medication Adherence Scale, Self-Efficacy to Manage Chronic Disease (SEMCD) Scale, and the Family Adaptability, Partnership, Growth, Affection, and Resolve (APGAR) Scale to assess medication adherence, self-efficacy, and family function, respectively. Participants also reported their perceived satisfaction with community health services (quantity, quality, affordability, and overall acceptance). The study used the multivariable logistic regression to assess the association of patient, family, and community factors with suboptimal medication adherence. Results: Among the participants, 42.9% reported suboptimal medication adherence. In the multivariable logistic regression model, male participants (odds ratio [OR] = 0.55, p = 0.001) had higher medication adherence compared to females. Having a self-efficacy score that was lower than or equal to the sample mean was significantly associated with lower adherence (OR = 1.44, p = 0.039). Participants unsatisfied with the affordability of community health services and medicine had lower adherence (OR = 2.18, p = 0.028) than those neutral or satisfied. There were no significant associations between family function and medication adherence. Conclusions: Sex, self-efficacy, and perceived affordability of community health services were important factors associated with medication adherence. Healthcare professionals are recommended to consider multiple factors and leverage services and resources in community health centers when promoting medication adherence.

U.S. Ethane Emissions and Trends Estimated from Atmospheric Observations.

Oil and natural gas (O&G) production and processing activities have changed markedly across the U.S. over the past several years. However, the impacts of these changes on air pollution and greenhouse gas emissions are not clear. In this study, we examine U.S. ethane (C2H6) emissions, which are primarily from O&G activities, during years 2015-2020. We use C2H6 observations made by the NOAA Global Monitoring Laboratory and partner organizations from towers and aircraft and estimate emissions from these observations by using an inverse model. We find that U.S. C2H6 emissions (4.43 ± 0.2 Tg·yr-1) are approximately three times those estimated by the EPA's 2017 National Emissions Inventory (NEI) platform (1.54 Tg·yr-1) and exhibit a very different seasonal cycle. We also find that changes in U.S. C2H6 emissions are decoupled from reported changes in production; emissions increased 6.3 ± 7.6% (0.25 ± 0.31 Tg) between 2015 and 2020 while reported C2H6 production increased by a much larger amount (78%). Our results also suggest an apparent correlation between C2H6 emissions and C2H6 spot prices, where prices could be a proxy for pressure on the infrastructure across the supply chain. Overall, these results provide insight into how U.S. C2H6 emissions are changing over time.

The crosstalk between oxidative stress and DNA damage induces neural stem cell senescence by HO-1/PARP1 non-canonical pathway.

Neural stem cells play a crucial role in maintaining brain homeostasis. Neural stem cells senescence can lead to the decline of nerve repair and regeneration, causing brain aging and neurodegenerative diseases. However, the mechanism underlying neural stem cells senescence remains poorly understood. In this study, we report a novel HO-1/PARP1 non-canonical pathway highlighting how oxidative stress triggers the DNA damage response, ultimately leading to premature cellular senescence in neural stem cells. HO-1 acts as a sensor for oxidative stress, while PARP1 functions as a sensor for DNA damage. The simultaneous expression and molecular interaction of these two sensors can initiate a crosstalk of oxidative stress and DNA damage response processes, leading to the vicious cycle. The persistent activation of this pathway contributes to the senescence of neural stem cells, which in turn plays a crucial role in the progression of neurodegenerative diseases. Consequently, targeting this novel signaling pathway holds promise for the development of innovative therapeutic strategies and targets aimed at mitigating neural stem cells senescence-related disorders.

Aryl Hydrocarbon Receptor Activation in Pulmonary Alveolar Epithelial Cells Limits Inflammation and Preserves Lung Epithelial Cell Integrity.

The aryl hydrocarbon receptor (AHR) is a receptor/transcription factor widely expressed in the lung. The physiological roles of AHR expressed in the alveolar epithelium remain unclear. In this study, we tested the hypothesis that alveolar epithelial AHR activity plays an important role in modulating inflammatory responses and maintaining alveolar integrity during lung injury and repair. AHR is expressed in alveolar epithelial cells (AECs) and is active. AHR activation with the endogenous AHR ligand, FICZ (5,11-dihydroindolo[3,2-b] carbazole-6-carboxaldehyde), significantly suppressed inflammatory cytokine expression in response to inflammatory stimuli in primary murine AECs and in the MLE-15 epithelial cell line. In an LPS model of acute lung injury in mice, coadministration of FICZ with LPS suppressed protein leak, reduced neutrophil accumulation in BAL fluid, and suppressed inflammatory cytokine expression in lung tissue and BAL fluid. Relevant to healing following inflammatory injury, AHR activation suppressed TGF-ß-induced expression of genes associated with epithelial-mesenchymal transition. Knockdown of AHR in primary AECs with shRNA or in CRISPR-Cas-9-induced MLE-15 cells resulted in upregulation of α-smooth muscle actin (αSma), Col1a1, and Fn1 and reduced expression of epithelial genes Col4a1 and Sdc1. MLE-15 clones lacking AHR demonstrated accelerated wound closure in a scratch model. AHR activation with FICZ enhanced barrier function (transepithelial electrical resistance) in primary murine AECs and limited decline of transepithelial electrical resistance following inflammatory injury. AHR activation in AECs preserves alveolar integrity by modulating inflammatory cytokine expression while enhancing barrier function and limiting stress-induced expression of mesenchymal genes.

Identification of RFC4 as a potential biomarker for pan-cancer involving prognosis, tumour immune microenvironment and drugs.

RFC4 is required for DNA polymerase δ and DNA polymerase ε to initiate DNA template expansion. Downregulated RFC4 inhibits tumour proliferation by causing S-phase arrest and inhibiting mitosis, resulting in the reduction of tumour cells. RFC4 has been implicated that it plays an important role in the initiation and progression of cancers, but a comprehensive analysis of the role of RFC4 in cancer has not been performed. We comprehensively analysed the expression, prognosis, methylation level, splicing level, relationship of RFC4 and immune infiltration, and pan-cancer immunotherapy response used various databases (including TCGA, GTEx, UALCAN, Oncosplicing, TIDE, TISCH, HPA and CAMOIP), and experimented its biological function in HCC. Through pan-cancer analysis, we found that RFC4 is significantly upregulated in most tumours. The tumour patients with high expression of RFC4 have poor prognosis. The methylation level and variable splicing level of RFC4 were abnormal in most tumours compared with the adjacent tissues. Furthermore, RFC4 was closely associated with immune cell infiltration in various cancers. RFC4 was significantly co-expressed with immune checkpoints and other immune-related genes. The expression of RFC4 could indicate the immunotherapy efficacy of some tumours. The RFC4 expression was associated with sensitivity to specific small molecule drugs. Cell experiments have shown that downregulated RFC4 can inhibit cell cycle and tumour cell proliferation. We conducted a systematic pan-cancer analysis of RFC4, and the results showed that RFC4 can serve as a biomarker for cancer diagnosis and prognosis. These findings open new perspectives for precision medicine.

CCGIB: A Cross-Channel Graph Information Bottleneck Principle.

The empirical studies of most existing graph neural networks (GNNs) broadly take the original node feature and adjacency relationship as single-channel input, ignoring the rich information of multiple graph channels. To circumvent this issue, the multichannel graph analysis framework has been developed to fuse graph information across channels. How to model and integrate shared (i.e., consistency) and channel-specific (i.e., complementarity) information is a key issue in multichannel graph analysis. In this article, we propose a cross-channel graph information bottleneck (CCGIB) principle to maximize the agreement for common representations and the disagreement for channel-specific representations. Under this principle, we formulate the consistency and complementarity information bottleneck (IB) objectives. To enable optimization, a viable approach involves deriving variational lower bound and variational upper bound (VarUB) of mutual information terms, subsequently focusing on optimizing these variational bounds to find the approximate solutions. However, obtaining the lower bounds of cross-channel mutual information objectives proves challenging through direct utilization of variational approximation, primarily due to the independence of the distributions. To address this challenge, we leverage the inherent property of joint distributions and subsequently derive variational bounds to effectively optimize these information objectives. Extensive experiments on graph benchmark datasets demonstrate the superior effectiveness of the proposed method.

Shugan Tongluo Qiangjing recipe protects against varicocele of EVC rats through modulating sperm DNA damage, telomere expression and oxidative stress.

Varicocele (VC) refers to expansion and tortuosity of spreading venous plexus in spermatic cord due to poor blood flow. This study aimed to investigate effects of Shugan Tongluo Qiangjing recipe (SGTL) on sperm DNA damage and oxidative stress in experimental VC (EVC) rats. EVC model was established by partial ligation of left renal vein. Spermatic vein diameter, testicular weight, sperm DNA fragmentation index (DFI) were evaluated. Telomere reverse transcriptase (TERT) expression, telomere gene transcription, and testicular tissue morphology were determined·H2O2, catalase, SOD, T-AOC were measured with colorimetry. SGTL significantly decreased spermatic vein diameter (P=0.000) and increased testicular weight (P=0.013) of rats compared those of EVC rats. SGTL maintained testicular tissue morphology in EVC rats. SGTL markedly reduced sperm DFI value in sperm of rats compared to EVC rats (P=0.000). SGTL significantly enhanced TERT expression and telomere gene transcription (P=0.028) in testis of rats compared to EVC rats. SGTL reduced H2O2 levels (P=0.001) and promoted CAT activity (P=0.016), SOD activity (P=0.049), and T-AOC activity (P=0.047) of rats, compared to EVC rats. In conclusion, SGTL could reduce pathogenic process of EVC by reducing sperm DNA damage and regulating telomere length in EVC rats, which may be related to oxidative stress regulation.

Few-Shot Learning With Enhancements to Data Augmentation and Feature Extraction.

The few-shot image classification task is to enable a model to identify novel classes by using only a few labeled samples as references. In general, the more knowledge a model has, the more robust it is when facing novel situations. Although directly introducing large amounts of new training data to acquire more knowledge is an attractive solution, it violates the purpose of few-shot learning with respect to reducing dependence on big data. Another viable option is to enable the model to accumulate knowledge more effectively from existing data, i.e., improve the utilization of existing data. In this article, we propose a new data augmentation method called self-mixup (SM) to assemble different augmented instances of the same image, which facilitates the model to more effectively accumulate knowledge from limited training data. In addition to the utilization of data, few-shot learning faces another challenge related to feature extraction. Specifically, existing metric-based few-shot classification methods rely on comparing the extracted features of the novel classes, but the widely adopted downsampling structures in various networks can lead to feature degradation due to the violation of the sampling theorem, and the degraded features are not conducive to robust classification. To alleviate this problem, we propose a calibration-adaptive downsampling (CADS) that calibrates and utilizes the characteristics of different features, which can facilitate robust feature extraction and benefit classification. By improving data utilization and feature extraction, our method shows superior performance on four widely adopted few-shot classification datasets.

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis.

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance.

Disulfidptosis, A Novel Cell Death Pathway: Molecular Landscape and Therapeutic Implications.

Programmed cell death is pivotal for several physiological processes, including immune defense. Further, it has been implicated in the pathogenesis of developmental disorders and the onset of numerous diseases. Multiple modes of programmed cell death, including apoptosis, pyroptosis, necroptosis, and ferroptosis, have been identified, each with their own unique characteristics and biological implications. In February 2023, Liu Xiaoguang and his team discovered "disulfidptosis," a novel pathway of programmed cell death. Their findings demonstrated that disulfidptosis is triggered in glucose-starved cells exhibiting high expression of a protein called SLC7A11. Furthermore, disulfidptosis is marked by a drastic imbalance in the NADPH/NADP+ ratio and the abnormal accumulation of disulfides like cystine. These changes ultimately lead to the destabilization of the F-actin network, causing cell death. Given that high SLC7A11 expression is a key feature of certain cancers, these findings indicate that disulfidptosis could serve as the basis of innovative anti-cancer therapies. Hence, this review delves into the discovery of disulfidptosis, its underlying molecular mechanisms and metabolic regulation, and its prospective applications in disease treatment.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery.

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Fine-tuning the activation behaviors of ternary modular cabazitaxel prodrugs for efficient and on-target oral anti-cancer therapy.

The disulfide bond plays a crucial role in the design of anti-tumor prodrugs due to its exceptional tumor-specific redox responsiveness. However, premature breaking of disulfide bonds is triggered by small amounts of reducing substances (e.g., ascorbic acid, glutathione, uric acid and tea polyphenols) in the systemic circulation. This may lead to toxicity, particularly in oral prodrugs that require more frequent and high-dose treatments. Fine-tuning the activation kinetics of these prodrugs is a promising prospect for more efficient on-target cancer therapies. In this study, disulfide, steric disulfide, and ester bonds were used to bridge cabazitaxel (CTX) to an intestinal lymph vessel-directed triglyceride (TG) module. Then, synthetic prodrugs were efficiently incorporated into self-nanoemulsifying drug delivery system (corn oil and Maisine CC were used as the oil phase and Cremophor EL as the surfactant). All three prodrugs had excellent gastric stability and intestinal permeability. The oral bioavailability of the disulfide bond-based prodrugs (CTX-(C)S-(C)S-TG and CTX-S-S-TG) was 11.5- and 19.1-fold higher than that of the CTX solution, respectively, demonstrating good oral delivery efficiency. However, the excessive reduction sensitivity of the disulfide bond resulted in lower plasma stability and safety of CTX-S-S-TG than that of CTX-(C)S-(C)S-TG. Moreover, introducing steric hindrance into disulfide bonds could also modulate drug release and cytotoxicity, significantly improving the anti-tumor activity even compared to that of intravenous CTX solution at half dosage while minimizing off-target adverse effects. Our findings provide insights into the design and fine-tuning of different disulfide bond-based linkers, which may help identify oral prodrugs with more potent therapeutic efficacy and safety for cancer therapy.

The role of HLA-DR on plasmacytoid dendritic cells in mediating the effects of Butyrivibrio gut microbiota on Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is viewed as a progressively deteriorating neurodegenerative disorder, the exact etiology of which remains not fully deciphered to this date. The gut microbiota could play a crucial role in PD development by modulating the human immune system. OBJECTIVE: This study aims to explore the relationship between gut microbiota and PD, focusing on how immune characteristics may both directly and indirectly influence their interaction. METHODS: Utilizing cumulative data from genome-wide association studies (GWAS), our research conducted a two-sample Mendelian randomization (MR) analysis to clarify the association between the gut microbiome and PD. Additionally, by employing a two-step MR approach, we assessed the impact of gut microbiota on PD development via immune characteristics and quantified HLA-DR mediation effect on plasmacytoid dendritic cells (pDCs). RESULTS: We discovered significant associations between PD and microbiota, comprising one class, one order, two families, and two genera. Furthermore, we explored the extent to which HLA-DR on pDCs mediates the effect of Butyrivibrio gut microbiota on PD. CONCLUSION: Our study emphasizes the complex interactions between the gut microbiota, immune characteristics, and PD. The relationships and intermediary roles identified in our research provide important insights for developing potential therapies that target the gut microbiome to alleviate symptoms in PD patients.

Formation of reactive intermediates in paddy water from different temperature zones for the promotion of abiotic ammonification.

The paddy field is a hot area of biogeochemical process. The paddy water has a large capacity in photo-generation of reactive intermediates (RIs) due to abundant photosensitive dissolved organic matter (DOM), which is influenced by the spatial heterogeneity of paddy soils but rarely been explored. Our work presents the first investigation of the role of soil properties on photochemistry in paddy water. Soil organic matter (SOM), determined by the temperature, was the dominant factor for the photo-generation of RIs in paddy water of main rice producing areas. The RI concentrations generated with abundant SOM from cool regions are 0.05-8.71 times higher than those for the warm regions in China. The humic-like substance and aromatic-like compounds of DOM plays an essential role in RIs generation, which is abundant in paddy soils rich in SOM from Chinese cool regions. In addition, RIs can efficiently accelerate the photo-ammonification of urea and free amino acids by 15.2 %-164 %, leading to 0.13-0.17 mmol/L/d photo-produced ammonium after fertilization, which is preferentially absorbed by rice. The findings of this study will extend our knowledge of the geochemistry of global paddy field ecosystem. The potential role of RIs in nitrogen cycle should be highlighted in the agroecosystem.

The enhancement of explosive power contributes to the development of anaerobic capacity: A comparison of autoregulatory progressive resistance exercise and velocity-based resistance training.

Objectives: Due to the character of the taekwondo, the adenosine triphosphate-phosphocreatine system provides the energy for each kick, the glycolytic system supports the repeated execution of kicks, and the aerobic system promotes recovery between these movements and the bout. Therefore, taekwondo athletes require high explosive power and anaerobic capacity in order to carry out sustained and powerful attacks. So, the purpose of this study is to compare the effects of APRE and VBRT on lower-limb explosive power and anaerobic capacity in college taekwondo players. Methods: A total of 30 taekwondo players completed an 8-week training intervention with autoregulatory progressive resistance exercise (APRE; n = 15) and velocity-based resistance training (VBRT; n = 15). Testing included the one-repetition maximum squat, countermovement jump (CMJ), taekwondo anaerobic intermittent kick test (TAIKT), and 30-s Wingate anaerobic test (WAnT). Results: (1) Intragroup comparisons revealed significant effects for one-repetition maximum squat, peak power of CMJ (CMJPP), relative peak power of CMJ (CMJRPP), and total number of TAIKT (TAIKTTN) in both the APRE and VBRT groups. The VBRT group exhibited small effect sizes for time at peak power of WAnT (WAnTPPT) and moderate effect sizes for peak power of WAnT (WAnTPP), relative peak power of WAnT (WAnTRPP), and fatigue index of TAIKT (TAIKTFI), whereas the APRE group exhibited small effect sizes for TAIKTFI. (2) Intergroup comparisons revealed no significant effects in any of the results. However, VBRT demonstrated a moderate advantage in WAnTPP and WAnTRPP, whereas APRE had a small advantage in CMJPP and CMJRPP. Conclusions: These findings suggest that APRE improved explosive power (CMJPP and CMJRPP) more, whereas VBRT improved anaerobic power output (WAnTPP and WAnTRPP) more. Both methods were found to have similar effects in improving the anaerobic endurance (WAnTPPT and TAIKTTN) and fatigue index (power drop of WAnT and TAIKTFI).

Enhancing Embedded Object Tracking: A Hardware Acceleration Approach for Real-Time Predictability.

While Siamese object tracking has witnessed significant advancements, its hard real-time behaviour on embedded devices remains inadequately addressed. In many application cases, an embedded implementation should not only have a minimal execution latency, but this latency should ideally also have zero variance, i.e., be predictable. This study aims to address this issue by meticulously analysing real-time predictability across different components of a deep-learning-based video object tracking system. Our detailed experiments not only indicate the superiority of Field-Programmable Gate Array (FPGA) implementations in terms of hard real-time behaviour but also unveil important time predictability bottlenecks. We introduce dedicated hardware accelerators for key processes, focusing on depth-wise cross-correlation and padding operations, utilizing high-level synthesis (HLS). Implemented on a KV260 board, our enhanced tracker exhibits not only a speed up, with a factor of 6.6, in mean execution time but also significant improvements in hard real-time predictability by yielding 11 times less latency variation as compared to our baseline. A subsequent analysis of power consumption reveals our approach's contribution to enhanced power efficiency. These advancements underscore the crucial role of hardware acceleration in realizing time-predictable object tracking on embedded systems, setting new standards for future hardware-software co-design endeavours in this domain.

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP.

Water availability (WA) is a key factor influencing the carbon cycle of terrestrial ecosystems under climate warming, but its effects on gross primary production (EWA-GPP ) at multiple time scales are poorly understood. We used ensemble empirical mode decomposition (EEMD) and partial correlation analysis to assess the WA-GPP relationship (RWA-GPP ) at different time scales, and geographically weighted regression (GWR) to analyze their temporal dynamics from 1982 to 2018 with multiple GPP datasets, including near-infrared radiance of vegetation GPP, FLUXCOM GPP, and eddy covariance-light-use efficiency GPP. We found that the 3- and 7-year time scales dominated global WA variability (61.18% and 11.95%), followed by the 17- and 40-year time scales (7.28% and 8.23%). The long-term trend also influenced 10.83% of the regions, mainly in humid areas. We found consistent spatiotemporal patterns of the EWA-GPP and RWA-GPP with different source products: In high-latitude regions, RWA-GPP changed from negative to positive as the time scale increased, while the opposite occurred in mid-low latitudes. Forests had weak RWA-GPP at all time scales, shrublands showed negative RWA-GPP at long time scales, and grassland (GL) showed a positive RWA-GPP at short time scales. Globally, the EWA-GPP , whether positive or negative, enhanced significantly at 3-, 7-, and 17-year time scales. For arid and humid zones, the semi-arid and sub-humid zones experienced a faster increase in the positive EWA-GPP , whereas the humid zones experienced a faster increase in the negative EWA-GPP . At the ecosystem types, the positive EWA-GPP at a 3-year time scale increased faster in GL, deciduous broadleaf forest, and savanna (SA), whereas the negative EWA-GPP at other time scales increased faster in evergreen needleleaf forest, woody savannas, and SA. Our study reveals the complex and dynamic EWA-GPP at multiple time scales, which provides a new perspective for understanding the responses of terrestrial ecosystems to climate change.

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.