From Priming to Therapy: Leveraging New Insights in T Cell Activation

CD8 T cells constitute one of the pillars of the adaptive immune response. They play a key role in eliminating pathogen-infected and cancerous cells. To effectively carry out their function, naïve CD8 T cells must undergo priming/activation in which several cell types, receptors, cytokines, and chemokines are involved. Various therapeutic approaches, such as vaccinations and anti-cancer therapies, i.e., immune checkpoint blockade (ICB) and Chimeric Antigen Receptor (CAR) T cell transfer, attempt to harness CD8 T cell biology to protect from or treat life-threatening diseases. Despite the significant success of CD8-T-cell-related therapies, the overall response rate of cancer patients remains relatively low, perhaps due to an incomplete understanding of the crucial events leading to optimal CD8 T cell activation. Recent findings highlight the importance of CD4 T cell help in CD8 T cell priming as well as the existence of an additional priming phase for the selection and expansion of high affinity T cells. Together, these findings offer a refined conceptual framework to guide future research and therapeutic development. Here, we present a revised perspective on clinically relevant CD8 T cell–based therapies in light of recent discoveries.

Open Water in Winter: An Influential but Underestimated Ecosystem in Northern Boreal Mountain Regions

Patches of open water (polynyas) persist throughout six-month winters on many ice-covered lakes in boreal mountain ecoregions of northwestern North America. We explored their distribution, hydrological correlates, and the diversity of species using them from freeze-up to break-up. In headwater drainages, lakes with outflow polynyas were significantly larger than those without, but many small lakes also had polynyas. There was a consistent threshold in upstream catchment size below which outflow polynyas were absent and above which they persistently occurred in downstream lakes. Outflow polynyas depend on winter-long through-flow of water, likely maintained by the hydraulic head of higher elevation ground water in perched water tables in this region of very limited permafrost. Based on camera trapping, two species, the American dipper and river otter, used polynyas heavily throughout winter foraging. Polynyas likely provided crucial forage for at least 9 species of migratory waterfowl (Anatidae) to complete their spring migration or to prepare for reproduction on local lakes. Cameras recorded additional 5 bird and 11 mammal species, as foragers, scavengers, or incidentally. We report previously undocumented significance of these spatially-limited and seasonal polynya ecosystems in expanding the diversity of winter ecological opportunity for numerous species on small to medium-sized lakes.

Large Language Model for Secure Operation of Power Systems

The integration of large-scale renewable energy, multi-criteria operational constraints, and complex grid topologies has intensified the challenges faced by the security monitoring process within power system dispatch. Dispatch guidelines, typically expressed in natural language, are difficult for conventional algorithms to interpret and apply in real time, while general-purpose Large Language Models (LLMs) lack domain-specific knowledge, risking inaccurate or unsafe recommendations. This study proposes an LLM-based monitoring framework that integrates domain-specific prompt engineering with fuzzy evaluation to address these limitations. The framework interprets dispatch guidelines, analyzes real-time power flow data, and converts semantic assessments into quantitative safety scores, enabling closed-loop decision-making. Validation on the IEEE 14-bus system demonstrates that the optimized LLM outperforms a general LLM in accuracy, logical consistency, and stability under complex multi-standard scenarios, while reducing reliance on manual intervention. The results highlight the framework’s potential to enhance monitoring efficiency and ensure intelligent, secure power system operation.

A Green Way for the Synthesis of Ester Oil by an Ionic Liquid as Both a Catalyst and Lubricant Additive

A series of ionic liquids 1-alkyl-3-methylim idazole bis(2-ethylhexyl) phosphate, were prepared, and the catalytic performance of ionic liquids was evaluated through the esterification reaction of pentaerythrotol and hexanoic acid at a stoichiometric ratio as a model reaction. The results showed that the [BMIM][DEHP] and [HMIM][DEHP] exhibited good catalytic activity. The [HMIM][DEHP] was chosen as a lubricant additive to further investigate the tribological properties after the reaction, and the results for both COF and WSD and wear volume indicate that the introduction of [HMIM][DEHP] has improved the friction reducing and anti-wear properties of pentaerythrotol tetra-hexanoate.

Collagen Biosynthesis to Engineered Biomaterials: Molecular Design, Synthetic Strategy, and Biomedical Application

Collagen, a principal component of the extracellular matrix, provides mechanical strength and stability to tissues and organs through its structural organization. Its biocompatibility has established it as a crucial material in biomedical applications such as drug delivery systems, cell culture matrices, and tissue engineering scaffolds. However, the use of animal-derived collagen carries risks of pathogen transmission, which has driven research towards developing synthetic collagen alternatives. Advances in AI-assisted protein engineering are accelerating the design of synthetic collagens and their applications in biomaterials. This review examines collagen’s structural characteristics, biosynthesis strategies, biological activities as well as AI-assisting engineering.

Defect Engineering in Carbon-Based Metal-Free Catalysts: Active Sites, Reduction Mechanisms, and 3D Architectures for Sustainable 4-Nitrophenol Reduction

Nitrophenols (NPs), classified as priority pollutants due to their significant toxicity, persistence, and bioaccumulation potential, pose severe threats to ecosystems and human health. Catalytic reduction, particularly the conversion of NPs like 4-nitrophenol (4-NP) to less toxic aminophenols using sodium borohydride (NaBH₄), represents a promising remediation strategy. While conventional metal-based catalysts face limitations including high cost, poor durability, and potential metal leaching, carbon-based metal-free catalysts (C-MFCs) have emerged as highly efficient, sustainable, and cost-effective alternatives. However, the precise reaction mechanisms governing NP reduction over C-MFCs remain ambiguous, and significant debate surrounds the nature of the active sites and the structure-activity relationships dictating performance. This review systematically elucidates the catalytic sites and associated reduction mechanisms in C-MFCs. We comprehensively summarize design principles centered on defect engineering strategies, encompassing single-atom (N, S, B, P, O), dual-atom (B,N; N,S; N,P), and tri-atom (B,N,F; N,P,F) doping, alongside non-doping defects such as edge and pore defects. The critical structure-performance relationships linking these engineered active sites to catalytic activity (e.g., turnover frequency, TOF) are analyzed, integrating experimental evidence and theoretical insights. Furthermore, strategies for constructing three-dimensional architectures to enhance active site accessibility and catalyst stability are highlighted. This work provides fundamental insights to guide the rational design of next-generation high-performance C-MFCs for sustainable nitrophenol pollution control.

From Fossil to Future: Trade, Technology and Clean Energy Transitions in High-Impact Developing Economies

This study examines the impact of economic growth, renewable energy equipment imports, and energy use on CO₂ emissions in seven developing countries over the period 2000–2021, employing second-generation panel estimators (Augmented Mean Group AMG, The Common Correlated Effects Mean Group CCEMG) that account for cross-sectional dependence and slope heterogeneity. Results show that economic growth and energy use significantly increase emissions, while renewable energy equipment imports display no direct or robust mitigating effect. This limited impact likely reflects adoption and integration challenges and the absence of complementary policies, underscoring the need for strategies that link imports to technology transfer and domestic manufacturing capacity. Granger causality tests indicate that growth and renewable energy imports drive emissions, highlighting the necessity for integrated green industrial policies, carbon pricing mechanisms, and sustainable finance instruments. These findings suggest that, for developing economies, achieving low-carbon growth requires a coordinated policy mix that aligns environmental objectives with economic development goals.

Why Watershed Ecology?

The Crystallography of Diverse Intermetallic Phases in Binary La-Ni Alloy Obtained by Melting and Its Structural Evolution under High Temperature Sintering

In this study, we have investigated the structural evolution of binary La-Ni alloy under different heat treatments by combing single crystal X-ray diffraction (SXRD) as well as scanning electron microscope (SEM) and transmission electron microscopy (TEM). It has been found that LaNi and La₇Ni₃ can be successfully synthesized through the arc melting method. Then it was found that LaNi₅ appears in the binary La-Ni mixture wrapped by a Tantalum sheet, followed by high-temperature sintering. Next, some pilot experiments have been carried out on the La-Ni mixture by sealing tube technique with some residual oxygen. Serendipitously, oxidation has not been found while La₃Ni₃Si₂ and La₂NiSi besides LaNi phase show up. Meanwhile, the detailed crystal structure information and their topological features of the aforementioned phases as well as their high-resolution TEM images, have been obtained. Furthermore, the orientation relationships of the Si-contaminated mixed phases have been thoroughly investigated by advanced precession images of SXRD patterns.

Physiopathological Insights into Atrial Fibrillation Onset through Heart Rate Variability Correlations

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. Early prediction of AF episodes remains a clinical challenge. This study aimed to generate physiopathological hypotheses for AF onset by analyzing correlations among heart rate variability (HRV) parameters in patients monitored via long-term Holter ECG. We utilized the IRIDIA-AF database, comprising 1319 paroxysmal AF episodes from 872 patients. An XGBoost machine learning model was developed to predict AF onset within 24 h using short- and long-term HRV features, fragmentation indices, and non-linear metrics extracted during sinus rhythm. Model interpretation was performed using SHapley Additive exPlanations (SHAP) values, and dimensionality reduction techniques were applied for data visualization. The model achieved an area under the receiver operating characteristic curve of 0.919 and an area under the precision-recall curve of 0.919, with high accuracy, sensitivity, and specificity. Key predictive features included short-term vagal activity, HRV fragmentation indices, and non-linear parameters, highlighting the role of the autonomic nervous system in AF initiation. Our findings suggest that distinct physiological profiles, detectable via HRV, may underlie AF susceptibility and could inform personalized monitoring and prevention strategies.