There is a difficulty reconciling the anti-humanistic approach of systems theory (as developed by Niklas Luhmann) with methodologies centering the individual. Society is viewed as a constellation of systems - politics, law, economy, media - and not the agglomeration of billions of individuals. This fore fronting of systems, and the unknowability of the cognitive processes of individuals, makes empirical operationalization of systems theory difficult. Can an interview method ever be accepted as a robust empirical operationalization of a theory which views society as a collection of intertwined and self-reinforcing systems, recursively communicating according to their own internalized contingencies? This paper attempts a theoretical justification for a methodology where individuals (researcher and interviewees) observe a system from which, theoretically, the human agent is excised and the focus is only on the construction of systemic possibilities. Reconciling theory and method is an important step in recognizing the possibilities of systems theory as a way of examining society.
Large language models (LLMs) are deep learning-based artificial intelligence models that have achieved remarkable success in natural language processing. Typically composed of neural networks with billions of parameters, they are trained on massive unlabeled datasets using self-supervised or semi-supervised learning. Beyond language, LLMs hold immense potential for addressing complex bioinformatics challenges. This review provides a comprehensive overview of transformer-based model applications in genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. We discuss critical components, including tokenization strategies for diverse biological data, transformer architectures, attention mechanisms, and pretraining approaches. We also survey currently available foundation models and their downstream applications across bioinformatics domains. Finally, we highlight major challenges that remain insufficiently addressed in prior reviews and outline future perspectives and design principles for next-generation biological language models, offering practical guidance for both users and developers.
Extreme temperature variability (ETV) is a key dimension of climate risk for billions of residents. However, how urbanization shapes global ETV divergence remains unclear. Here, we assemble a 1950-2020 panel of 10,522 cities and demonstrate that ETV trajectories diverge by development status as measured by the human development index (HDI). ETV intensifies in high-development cities, whereas it slowly weakens in low-development cities, resulting in a current gap of roughly 1.66 °C. Decomposing ETV into event frequency and intensity reveals that cumulative ETV is driven mainly by intensity. Using an interpretable machine-learning framework, we find that aerosols are the urban factor most strongly associated with ETV after controlling for climate and geography. Blue-green space is consistently associated with lower ETV, whereas urban morphology has a smaller and context-dependent effect. These findings link global ETV inequality to urban governance and support targeted management that focusses on limiting volatility under climate risk.
The word sex can refer to at least seven distinct, evolutionarily related biological phenomena (0-6 below). Bacteria and archaea use mechanisms for horizontal gene transfer (0) broadly and promiscuously, even without cell contact. Their endosymbiotic merger led to eukaryotes and a new form of gene exchange, using syngamy and meiosis (1) to mix and recombine similar genomes. This innovation altered the course of evolution. The requirement for chromosome homology in meiosis separated evolving lineages. Mating types evolved within them, differentiating roles of the cells pairing in syngamy, and gamete size dimorphisms (2) evolved many times. Organisms evolved diverse gamete-production strategies (3) and a plethora of traits associated with those strategies (4). They also evolved many ways to facilitate gamete encounters (5). Some of these were expressed in other contexts and gained new functions (6). These phenomena include cellular genetic processes (0, 1), alternative states of cells and organisms (2-4), and things organisms do (5, 6) that have diversified over billions of years. Sex is neither biologically simple nor conceptually singular, but the word is often used without qualifiers, assuming shared understanding that may not exist. We present a framework for multiple sex concepts that serve as anchor points to discuss the relationships among these phenomena and the diversity and complexity of each, including the biologically fuzzy edges generated by developmental variation and evolutionary change. We highlight several communication challenges that may limit biological understanding and/or facilitate the deployment of biology to justify social harms. For instance, sex is used for three alternative-state concepts (2-4 above) whose distinctions are sometimes collapsed, fostering overgeneralization based on the supposed simplicity of anisogamy, but even gamete sex is evolutionarily complex and subject to shifting definitional criteria. Attempts to narrowly bound "biological sex" minimize this complexity and what we can learn from it, while facilitating the misuse of biology in anti-diversity social projects. Another challenge is that using accessible language that works for organisms like ourselves may misrepresent or obscure the biology of other life forms, but specialized language can create information silos; these limit the broad comparisons that are necessary both for perspective on our own biology and a more expansive understanding of life. The multiple sex concepts framework is a way to acknowledge the scope and discuss the complexity of sex in biology, offering a scaffold to facilitate broader thinking, better communication, and discovery.
Anti-melanoma differentiation-associated gene 5 (MDA5) positive juvenile dermatomyositis (JDM) (anti-MDA5+ JDM) is a distinct subtype of JDM characterized by marked clinical heterogeneity. Although cutaneous manifestations and interstitial lung disease (ILD) are well recognized, atypical musculoskeletal presentations may lead to diagnostic delay. We report a 16-year-old male with anti-MDA5+ JDM who presented with progressive joint contractures as the predominant manifestation over a 2-year period. The patient also exhibited restricted mouth opening, with a classical dermatomyositis (DM) cutaneous rash absent or only mild, and proximal muscle weakness, mild to moderate. Laboratory evaluation revealed high-titer anti-MDA5 antibodies and a markedly elevated serum immunoglobulin E (IgE) level. Magnetic resonance imaging (MRI) demonstrated periarticular and soft-tissue involvement, and muscle biopsy confirmed pathological features consistent with DM. Treatment with systemic glucocorticoids in combination with methotrexate resulted in substantial improvement in joint mobility, with good tolerability during follow-up. Progressive contractures can occasionally become the predominant presenting manifestation in anti-MDA5+ JDM and contribute to diagnostic delay. This case underscores the importance of early evaluation for idiopathic inflammatory myopathies (IIM), including myositis-specific antibody (MSA) testing and muscle biopsy, in adolescents with unexplained progressive joint contractures.
Accurate and rapid mapping of burned areas is critical for understanding the impacts of forest fires on ecosystems, the carbon cycle, and post-fire recovery processes. Traditional pixel-based approaches often provide limited accuracy due to spectral confusion, topographic variability, and sensitivity to empirically defined threshold values. Although deep learning frameworks such as U-Net, DeepLab, and SegFormer have achieved substantial improvements in spatial accuracy in recent years, their operational scalability remains constrained by the need for labeled training data and region-specific retraining. This study proposes a zero-shot burned area mapping approach based on the Segment Anything Model (SAM) using Sentinel-2 data. SAM is a foundation model trained on more than one billion segmentation masks and is capable of performing prompt-based segmentation without task-specific additional training. In the proposed methodology, composite images derived from Sentinel-2-based NBR, NBR2, and NDVI indices were generated and used as the primary input representation for SAM under a zero-shot configuration. Subsequently, the effects of different pre-processing, post-processing, and hyperparameter configurations on model performance were systematically investigated. Quantitative analyses revealed that multi-scale configurations, particularly crop_n_layers = 2, substantially improved geometric integrity and boundary accuracy. The highest Intersection over Union (IoU) values were 0.89 for the Bursa site and 0.87 for the Çanakkale site, while the corresponding F1 scores were 0.94 and 0.92, respectively. These results indicate that SAM can achieve performance levels comparable to supervised models trained with labeled data, despite requiring no task-specific training. Furthermore, integrating NBR-, NBR2-, and NDVI-based composite inputs with SAM outputs improved the discrimination between burned and unburned surfaces, particularly by reducing boundary fragmentation and spectral confusion in visually heterogeneous areas. The proposed method reduces dependence on manual labeling, which is one of the major limitations of deep learning-based remote sensing workflows, by operating without fully labeled training datasets. Therefore, it offers a fast and scalable solution with reduced processing costs and strong adaptability to different ecosystems and sensor conditions. In this regard, this study represents one of the first systematic applications of SAM for burned forest area detection and provides a label-free, flexible, and generalizable alternative to conventional supervised deep learning approaches. Overall, the findings demonstrate that SAM can serve as a powerful, scalable, and low-cost framework for zero-shot environmental monitoring and automatic burned area detection, particularly in data-scarce or time-critical post-fire assessment scenarios.
Urban overheating, which affects more than two billion people, is a growing problem for cities in arid and semi-arid regions. The majority of current research relies on two-dimensional satellite indices like Normalized Difference Vegetation Index (NDVI), which do not capture the vertical vegetation structures that shape thermal comfort at street level, despite the fact that urban greening has demonstrated promise as a cooling strategy. Quantitative cooling thresholds specific to hyper-arid climates are also lacking; these are the benchmarks that urban planners genuinely require in order to make well-informed design choices. To close these gaps, this study creates a predictive framework. XGBoost machine learning (R² = 0.84) and SHAP-based threshold analysis were used in conjunction with multi-dimensional vegetation metrics, including NDVI, Green View Index (GVI), and a recently developed Combined Greenery Index (CGI). The main case study is the 16 km² King Salman Garden that is being planned for Riyadh, Saudi Arabia. The analysis provides design benchmarks for cities with Köppen BWh climates by identifying critical non-linear thresholds, CGI ≥ 0.25 and GVI ≥ 25%, beyond which cooling effects increase sharply. The role of wind direction in green space planning is highlighted by model projections that show a mean daytime air temperature reduction of 3.6 °C ± 0.9 °C, extending 1.6 km downwind and 0.8 km upwind. Additionally, the findings identify an optimal range for irrigation efficiency between 65 and 75% of reference evapotranspiration (ET₀), which allows for 83% of maximum cooling with just 57% of full water use. This is a particularly pertinent finding for areas with limited water supplies. Although transferability is influenced by variations in urban form, local climate variability, and governance context, a comparative study with parks in Abu Dhabi, Dubai, and Phoenix reveals consistent threshold patterns across BWh climates. In order to maximize green infrastructure in arid cities, the framework provides urban planners with evidence-based tools.
The study aimed to examine the economic and epidemiological burden of human papillomavirus (HPV) on both men and women in the Czech Republic. It extended beyond the typically studied cervical cancer to encompass a rising incidence of non-cervical HPV-related cancers. The utilization of administrative healthcare claims data enabled the identification of HPV-related diseases using ICD-10 codes. For each identified disease, the proportion corresponding to disease cases directly attributable to HPV was analyzed in terms of the associated healthcare costs. Furthermore, the years of life lost (YLL) and indirect costs associated with premature mortality were calculated using gender-specific life expectancies and average salaries, employing the human capital approach. The findings indicate that there were over 100,000 incident cases of HPV-related diseases between 2018 and 2020, with the majority of these occurring in females (84.2%), and the average age of the patient was 40.6 y. The total medical costs incurred by HPV-related diseases exceeded 1 billion CZK (€41.1 million) over the study period (2018-2020), with an estimated 27,436 y lost due to premature mortality. The indirect costs, attributable exclusively to productivity losses from premature mortality, amounted to over 3.29 billion CZK (€127.7 million). These results highlight the substantial financial and health burdens HPV imposes on the Czech healthcare system, underscoring the necessity for informed policy-making and cost-effective HPV interventions, including enhanced vaccination and preventive programs.
Whether Archean arc-like volcanism reflects subduction remains debated. We present high-resolution geochemical data from a well-preserved 3.13-3.10 Ga arc-like volcanic succession in Australia's Pilbara Craton, a rare Archean analog of modern arc volcanism retaining fluid-mobile element concentrations consistent with primary magmatic values. The sequence records three primitive lava series typical of modern arcs: tholeiitic, calc-alkaline, and the oldest stratigraphically extensive genuine boninites. Geochemical modelling shows this melt diversity requires at least two mantle sources with distinct depletion histories. The mantle H2O required for fluid-assisted melting to produce these lavas substantially exceeds primitive mantle, approaching the H2O-saturated solidus of modern mantle wedges. We infer hydrous melting was triggered by dripduction, the short-lived inclined foundering of hydrated lithosphere without laterally continuous plate boundaries, in an off-plateau setting. Dripduction locally recycled surface water and generated arc-like magmas without self-sustained plate tectonics, possibly promoting mantle-ocean-atmosphere volatile exchange during the Archean.
We present FLOWR.ROOT, an SE(3)-equivariant flow-matching foundation model that unifies pocket-aware 3D ligand generation with multi-endpoint binding affinity prediction (pIC50, pKi, pKd, pEC50) and pLDDT-based confidence estimation in a single backbone. One trained model supports de novo pocket-conditional generation, interaction- and pharmacophore-conditional sampling, scaffold hopping and elaboration, and fragment growing or replacement, enabled by a mixed isotropic-anisotropic prior placement strategy. Training proceeds in three stages: large-scale pre-training on billions of ligand conformations and millions of mixed-fidelity protein-ligand complexes, refinement on curated co-crystal data, and project-specific adaptation via parameter-efficient LoRA finetuning. Joint structure-affinity modelling enables inference-time importance-sampling guidance for single- and multi-objective design without external scoring functions. Case studies on kinase selectivity (CK2α/CLK3) and scaffold elaboration on TYK2, ERα, and BACE1 illustrate utility from hit identification through lead optimization.
Obesity represents a critical public health challenge. As of 2021, over two billion adults were overweight or living with obesity,1 conditions that increase the risk of multiple chronic diseases and premature mortality. Although effective weight-loss strategies exist, post-intervention weight regain remains the central obstacle to long-term management.
The increasing budget financing of primary health care (PHC) in Kyrgyzstan brings no improvement into workforce indicators that establishes risks to sustainability of family medicine system. The purpose of the study was to assess impact of financing parameters on family medicine efficiency and to justify necessity of reforming system of remuneration of labor. The cross-sectional retrospective analytical study (2013-2023) was based on data from 12 Family Medicine Centers in Southern Kyrgyzstan. The data from MHIF, NatStatCom, and MoH KR were analyzed. The survey included 180 medical workers and 420 patients. The statistical analysis was performed using software SPSS v.25 to calculate the Students t-test, Pearson correlation, linear regression). The post-hoc power analysis confirmed adequate sample size (power ≥ 0.80 at α = 0.05). The PHC funding increased by 94.5% (from 2.56 to 4.98 billion soms), yet physician supply decreased by 22.9% (from 4.8 to 3.7 per 10,000 population). The direct correlation was established between funding and patient satisfaction (r = 0.48; p lt; 0.05). The inverse relationship was established between physician workload and medical care accessibility (r = -0.61; p lt; 0.01). The current capitation model fails to stimulate preventive activities: its share in physicians working time is 14-18% versus 35-40% in EU countries. The transition to combined payment system (fixed salary + 15-20% incentive payments for quality and prevention) is required. The implementation of regional coefficients for PHC system sustainability is needed too. The article was prepared in accordance with STROBE guidelines for observational studies. Рост бюджетного финансирования на первичную медико-санитарную помощь в Кыргызстане не сопровождается улучшением кадровых показателей, что создает риски для устойчивости системы семейной медицины. Цель исследования — оценить влияние параметров финансирования на эффективность семейной медицины и обосновать необходимость реформирования системы оплаты труда. Проведен ретроспективный анализ в рамках одноэтапного исследования (2013—2023) на базе 12 центров семейной медицины южных регионов Кыргызстана. Проанализированы данные Фонда обязательного медицинского страхования, Национального статистического комитета, Министерства здравоохранения Кыргызской Республики. Проведено анкетирование 180 медработников и 420 пациентов. Статистическая обработка выполнена в SPSS v.25 (t-критерий Стъюдента, корреляционный анализ Пирсона, линейная регрессия). Постфактум-анализ мощности подтвердил достаточность выборки (power ≥0,80 при α=0,05). Финансирование первичной медико-санитарной помощи увеличилось на 94,5%, однако обеспеченность врачами снизилась на 22,9%. Выявлена прямая корреляция между финансированием и удовлетворенностью пациентов (r=0,48; plt;0,05) и обратная связь между нагрузкой врача и доступностью помощи (r=−0,61; plt;0,01). Существующая подушевая модель не стимулирует профилактическую работу: ее доля в структуре рабочего времени врача составляет 14—18% против 35—40% в странах ЕС. Требуется переход к комбинированной системе оплаты (фиксированная часть +15—20% стимулирующих выплат за качество и профилактику) и внедрение региональных коэффициентов для устойчивости системы первичной медико-санитарной помощи. Статья подготовлена в соответствии с рекомендациями STROBE для наблюдательных исследований.
Alzheimer's disease (AD) remains a major unmet clinical challenge, with limited therapeutic strategies capable of effectively modulating neuroimmune dysfunction. Leukocyte immunoglobulin-like receptor B4 (LILRB4/ILT3) has recently emerged as an inhibitory microglial immune checkpoint implicated in ApoE-mediated suppression of amyloid-β (Aβ) clearance and inflammatory signaling, supporting its potential as a therapeutic target in AD. Here, we applied DNA-encoded library (DEL) screening of approximately 3.6 billion compounds to identify small molecule binders of LILRB4. Biophysical validation identified APX1 as a direct LILRB4 ligand with submicromolar affinity, which was further confirmed by cellular thermal shift assay (CETSA). Docking-guided mutagenesis studies defined a discrete ligand-binding interface involving key hotspot residues required for stable target engagement. Functionally, APX1 disrupted the LILRB4-ApoE interaction in orthogonal ELISA and biolayer interferometry assays. In human iPSC-derived microglia, APX1 suppressed SHP1/2 phosphorylation, attenuated NF-κB activation and IL-1β secretion, and restored Aβ42 uptake under ApoE-driven inflammatory conditions. APX1 further demonstrated favorable in vitro developability, metabolic stability, and CNS exposure properties. In the 5xFAD mouse model of AD, oral administration of APX1 improved cognitive performance, reduced cortical and hippocampal Aβ42 burden, suppressed neuroinflammatory cytokines, and decreased activated microglial populations. Collectively, these findings establish APX1 as a promising small molecule modulator of the LILRB4-ApoE signaling axis and support pharmacological targeting of neuroimmune checkpoints as a therapeutic strategy for AD.
Metabolic diseases-including obesity, type 2 diabetes mellitus (T2DM), and non-alcoholic fatty liver disease (NAFLD)-affect over 1 billion individuals globally and are characterized by insulin resistance, chronic inflammation, and gut microbiota dysbiosis. Herbal medicines offer multi-component therapeutic potential through microbiota modulation, but mechanistic insights remain fragmented. This review synthesizes recent advances in herbal medicine-mediated gut microbiota regulation in metabolic diseases and delineates underlying molecular mechanisms. A comprehensive literature search was conducted across PubMed and Web of Science. Search strategies employed MeSH terms and free-text keywords encompassing herbal medicines, gut microbiota, and metabolic diseases. Two authors performed study selection and data extraction. Evidence synthesis was structured according to intervention type and metabolic disease category. Herbal polysaccharides and other compounds consistently increased beneficial bacteria and promoted short-chain fatty acids (SCFAs) production, improving intestinal barrier integrity via ZO-1/Occludin upregulation and attenuating TLR4/NF-κB-mediated inflammation. Herbal formulations exerted synergistic effects by remodeling microbial community structure, correcting SCFA/bile acid imbalances, and activating IRS1/PI3K/AKT insulin signaling. Notably, Lactobacillus and Akkermansia emerged as recurrent beneficial targets across multiple herbal interventions. However, evidence is predominantly preclinical, and translational validity to humans requires further validation. Herbal medicines ameliorate metabolic diseases through multi-target gut microbiota modulation, involving SCFA production, bile acid metabolism, and inflammatory pathway attenuation. These mechanistic insights support the development of microbiota-targeted herbal therapeutics, though clinical translation necessitates standardized formulations and rigorous human trials.
Approximately 200-300 billion cells die daily through apoptosis, a prominent form of programmed cell death, to maintain tissue homoeostasis. If apoptotic cells are not efficiently removed by phagocytes, they progress to secondary necrosis when the plasma membrane (PM) becomes permeabilised and release proinflammatory damage-associated molecular patterns (DAMPs) such as HMGB1 and ATP, which drive inflammation and contribute to autoimmune diseases. Thus, controlling inflammation through maintaining PM integrity is critical, however the molecular mechanisms underpinning this is not well defined. Here, we reveal a calcium-dependent process that delays secondary necrosis by promoting PM repair. Mechanistically, calcium influx through T-type voltage-gated calcium channels mediates the recruitment of the lipid scramblase ATG9A and Golgi components to damaged PM regions, thereby preventing early cellular lysis and DAMP release. Inhibition of calcium influx or loss of ATG9A accelerates PM rupture, increases DAMP secretion, and exacerbates inflammatory cell recruitment in vivo. Taken together, this study establishes a novel role for T-type calcium channels and ATG9A in regulating PM repair during apoptosis and highlights their therapeutic potential for controlling unwanted inflammation.
About 1.5-2 billion years ago, an endosymbiosis between aerobic α-proteobacteria and anaerobic archaeal cells generated mitochondria, i.e., organelles capable of producing oxidative energy. The bacterial genome was fundamentally reduced and a circular mitochondrial genome evolved containing mainly the genes coding for the subunits of the electron transport chain. Before the symbiotic event, there existed a virus-host co-evolution which involved the development of sensors for detecting dangerous viral DNA/RNA molecules. Endosymbiosis supplied eukaryotic cells not only with an oxidative powerhouse to allow the evolution of more complex multicellular organisms but it also meant that cells now housed an organelle which was able to generate reactive oxygen species (ROS) and to leak mitochondrial DNA (mtDNA) and double-stranded RNA (dsRNA) into the cytoplasm. There is now abundant evidence that during aging and age-related diseases mitochondria are prone to release both mtDNA and dsRNA. In the cytoplasm, mtDNA/dsRNA molecules activate a number of cytosolic nucleic acid sensors leading to the secretion of type-1 interferons (IFN) and many other cytokines which promote an age-related proinflammatory state. Currently, it is known that mtDNA can activate the cGAS-STING pathway, AIM2 inflammasomes, IFI16 receptors, and ZBP1 sensors and in addition mitochondrial dsRNA stimulates RIG-1/MDA5 signaling. Interestingly, there is abundant evidence that all these receptors are drivers of cellular senescence and inflammaging. For decades, there has been mounting evidence that mitochondria have a crucial role in the aging process. We will examine this question from the perspective of evolution and propose that mitochondrial evolution created an endogenic source for the leakage of dangerous mtDNA/dsRNA which subsequently stimulated cytosolic DNA/RNA sensors, an evolutionarily conserved viral defence mechanism. It seems that these two evolutionary events provided not only the basis for the inevitable process of aging but also ensuring the death of parental organisms.
Under the background of rapid urbanization, imbalances and spatial mismatches between the supply and demand of ecosystem services (ES) have become major constraints on regional sustainable development. Accurately identifying ecological compensation areas and establishing scientifically sound compensation standards are critical for alleviating the conflicts between economic development and ecological conservation. This study takes the middle reaches of the Yangtze River urban agglomeration (MYUA) as the study area and focuses on three key ES in 2023-carbon sequestration (CS), air purification (AP), and water purification (WP). Centered on the questions of what to compensate, who should compensate, and how much to compensate, this study conducts a quantitative assessment of ecosystem service supply and demand, a spatial analysis of ecosystem service flows (ESF), and a systematic estimation of ecological compensation standards. The results indicate that: (1) The overall supply-demand matching of ES in the MYUA is relatively good, with the degree of matching ranked as WP > CS > AP; (2) Significant differences exist in the spatial flow characteristics of the three services. The CS forms 16 intercity flow pathways, with transfer distances ranging from 32.51 to 339.40 km. The AP constitutes a flow network composed of 26 pathways, with transfer distances between 22.32 and 445.58 km. The WP exhibits 21 instances of spatial transfer, among which the flow from Changsha to Jingzhou is the largest (640.73 t), while the flow from Xiangtan to Ezhou is the smallest (0.27 t); (3) The total ecological compensation amounts reach 1.411 billion CNY (≈ 0.20 billion USD) for CS, 678 million CNY (≈ 95.50 million USD) for AP, and a relatively lower 7.6002 million CNY (≈ 1.07 million USD) for WP. This study provides a theoretical basis and decision-making support for formulating scientifically grounded ecological compensation policies and promoting coordinated green development in the MYUA.
Virtual screening (VS) is a powerful approach to exploring a vast chemical space, encompassing libraries of millions to billions of compounds. However, the low hit rates of VS require testing numerous candidates to validate true binders, followed by iterative optimization cycles, which makes experimental validation costly and time-consuming. Here, we report COMBINAUT, an automated parallel synthesis platform that generates diverse chemical scaffolds to accelerate hit validation and refinement. Using a faculty-wide collection of in-house building blocks, the system enables enumeration of over 22.9 million compounds, each designed for parallelized synthesis within 32 h using repurposed solid-phase peptide synthesis equipment. Using this platform, we performed large-scale VS targeting the allosteric pocket of the immuno-oncology target, C-C chemokine receptor 2 (CCR2). Our approach facilitated the rapid synthesis and testing of 100 VS hits spanning diverse molecular architectures. In radioligand binding assays, we successfully validated nine hits with distinct scaffolds, including completely novel CCR2 ligand chemotypes. Iterative hit-to-lead optimization using the automated workflow produced cell-active CCR2 antagonists. This work demonstrates the synergy of automated synthesis and VS, enabling the efficient exploration of chemical space and the rapid discovery of novel ligands.
Poor-quality reinjected water has caused significant formation damage in the Keshen ultra-deep HP/HT gas field. This damage has led to progressively increasing injection pressure and declining injectivity, threatening sustainable gas field water disposal. In this study, we systematically analyzed water quality and operational data from injection wells. Based on this analysis, we developed a theoretical filter-cake model to describe solid particle accumulation and its impact on reservoir impairment. We also proposed an integrated remediation strategy that combines surface water treatment optimization (demulsification, flocculation, and filtration) with periodic downhole acid stimulation. Preliminary field evidence suggests that this strategy is associated with significant recovery in post-treatment injectivity indices. These findings indicate that the integrated approach may contribute to remediating near-wellbore damage and restoring long-term injectivity in complex HP/HT reservoirs.
Software-Defined Networks (SDNs) are already susceptible to cyber-attacks because of the weak security mechanisms and limited resources. This is predicted to be intensified in the Internet of Things era with a forecast of more than 29 billion connected devices in 2030, thereby broadening the threat surface significantly and making it increasingly difficult for cybersecurity solutions to address these threats. Conventional intrusion detection systems(IDSs) on SDNs are challenged by imbalanced multi-class data, high-dimensional and noisy features, and a lack of interpretability. To address these challenges, this study proposes a hybrid SDN-based IDS framework that integrates Generative Adversarial Networks (GANs) to handle imbalanced datasets, one-way ANOVA and Genetic Algorithm (GA) for feature selection, baseline classifier optimization using Grid Search and Explainable AI techniques such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), Morris sensitivity analysis and permutation combination to achieve robust, accurate, and interpretable intrusion detection. The grid search-optimized XGBoost model, denoted as OptiXGB-IDS, demonstrates high performance on the InSDN dataset, with a test accuracy of 99.87%, and macro-averaged precision, recall, and F1-score of 0.9618, 0.9951 and 0.9775 respectively. In addition, the model achieves a Cohen's kappa coefficient of 0.9982 and a Brier score of 0.0024, with an inference time of 0.026312 ms per flow. In order to further test cross-dataset robustness, the proposed framework was also validated on the CIC-IDS2017 dataset, where OptiXGB-IDS achieved the highest test accuracy of 98.70% and outperformed all other competing models. The proposed hybrid GA-GAN-XAI framework significantly improves the IDS performance by providing key traffic flow features and improving spatial and temporal feature learning, making it highly robust and providing a very precise and reliable solution for detecting complex and previously unseen cyber threats in SDN-based IoT environments.