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Snow is a crucial component of the cryosphere and plays a vital role in the hydrological cycle, energy balance, and ecosystem function of mountainous regions [...]

Based on engineered or bacterial nucleases, the development of genome editing technologies has opened up the possibility of directly targeting and modifying genomic sequences in almost all eukaryotic cells. Genome editing has extended our ability to elucidate the contribution of genetics to disease by promoting the creation of more accurate cellular and animal models of pathological processes and has begun to show extraordinary potential in a variety of fields, ranging from basic research to applied biotechnology and biomedical research. Recent progress in developing programmable nucleases, such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeat (CRISPR)–Cas-associated nucleases, has greatly expedited the progress of gene editing from concept to clinical practice. Here, we review recent advances of the three major genome editing technologies (ZFNs, TALENs, and CRISPR/Cas9) and discuss the applications of their derivative reagents as gene editing tools in various human diseases and potential future therapies, focusing on eukaryotic cells and animal models. Finally, we provide an overview of the clinical trials applying genome editing platforms for disease treatment and some of the challenges in the implementation of this technology.

Nanosized palladium (Pd)-based catalysts are widely used in the direct hydrogen peroxide (H 2 O 2 ) synthesis from H 2 and O 2 , while its selectivity and yield remain inferior because of the O-O bond cleavage from both the reactant O 2 and the produced H 2 O 2 , which is assumed to have originated from various O 2 adsorption configurations on the Pd nanoparticles. Herein, single Pd atom catalyst with high activity and selectivity is reported. Density functional theory calculations certify that the O-O bond breaking is significantly inhibited on the single Pd atom and the O 2 is easier to be activated to form *OOH, which is a key intermediate for H 2 O 2 synthesis; in addition, H 2 O 2 degradation is shut down. Here, we show single Pd atom catalyst displays a remarkable H 2 O 2 yield of 115 mol/g Pd /h and H 2 O 2 selectivity higher than 99%; while the concentration of H 2 O 2 reaches 1.07 wt.% in a batch. Nanosized Pd-based catalysts are widely used in the direct hydrogen peroxide (H 2 O 2 ) synthesis from H 2 and O 2 , while the selectivity and yield of H 2 O 2 remain inferior. Here, a remarkable H 2 O 2 yield of 115 mol/g Pd /h and H 2 O 2 selectivity higher than 99% are reported using a Pd single-atom catalyst for the direct synthesis of H 2 O 2 .

Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work. We aimed to provide clinicians and other health care practitioners with systematic guidance in selecting an LLM that is relevant and appropriate to their needs and facilitate the integration process of LLMs in health care. We conducted a literature search of full-text publications in English on clinical applications of LLMs published between January 1, 2022, and March 31, 2025, on PubMed, ScienceDirect, Scopus, and IEEE Xplore. We excluded papers from journals below a set citation threshold, as well as papers that did not focus on LLMs, were not research based, or did not involve clinical applications. We also conducted a literature search on arXiv within the same investigated period and included papers on the clinical applications of innovative multimodal LLMs. This led to a total of 270 studies. We collected 330 LLMs and recorded their application frequency in clinical tasks and frequency of best performance in their context. On the basis of a 5-stage clinical workflow, we found that stages 2, 3, and 4 are key stages in the clinical workflow, involving numerous clinical subtasks and LLMs. However, the diversity of LLMs that may perform optimally in each context remains limited. GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively. General-purpose LLMs may not perform well in specialized areas as they often require lightweight prompt engineering methods or fine-tuning techniques based on specific datasets to improve model performance. Most LLMs with multimodal abilities are closed-source models and, therefore, lack of transparency, model customization, and fine-tuning for specific clinical tasks and may also pose challenges regarding data protection and privacy, which are common requirements in clinical settings. In this review, we found that LLMs may help clinicians in a variety of clinical tasks. However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks. Therefore, their clinical deployment remains challenging. On the basis of this review, we propose an interactive online guideline for clinicians to select suitable LLMs by clinical task. With a clinical perspective and free of unnecessary technical jargon, this guideline may be used as a reference to successfully apply LLMs in clinical settings.

Chemokines are a large family of small secreted proteins that have fundamental roles in organ development, normal physiology, and immune responses upon binding to their corresponding receptors. The primary functions of chemokines are to coordinate and recruit immune cells to and from tissues and to participate in regulating interactions between immune cells. In addition to the generally recognized antimicrobial immunity, the chemokine/chemokine receptor axis also exerts a tumorigenic function in many different cancer models and is involved in the formation of immunosuppressive and protective tumor microenvironment (TME), making them potential prognostic markers for various hematologic and solid tumors. In fact, apart from its vital role in tumors, almost all inflammatory diseases involve chemokines and their receptors in one way or another. Modulating the expression of chemokines and/or their corresponding receptors on tumor cells or immune cells provides the basis for the exploitation of new drugs for clinical evaluation in the treatment of related diseases. Here, we summarize recent advances of chemokine systems in protumor and antitumor immune responses and discuss the prevailing understanding of how the chemokine system operates in inflammatory diseases. In this review, we also emphatically highlight the complexity of the chemokine system and explore its potential to guide the treatment of cancer and inflammatory diseases. Chemokines are a large family of small secreted proteins that coordinate and recruit immune cells into and out of tissues and to participate in regulating the interactions between immune cells. The chemokine/chemokine receptor axis is involved in the progression of multiple malignancy types and almost all inflammatory diseases. This review summarizes recent advances of chemokine system in antitumor and protumor immune responses and discuss the prevailing understanding of how the chemokine system operates in inflammatory diseases. Modulating the expression of chemokines and/or their corresponding receptors on tumor cells or immune cells provides the basis for the exploitation of new drugs for clinical evaluation in the treatment of related diseases.

River ice products from optical satellites are still dominated by binary ice–water or ice–snow discrimination, leaving within-ice spectral heterogeneity largely unresolved. This study benchmarks how far river ice can be subclassified from multispectral reflectance alone on the Tibetan Plateau using Landsat 5/7, Landsat 8/9, and Sentinel-2 surface-reflectance imagery. We compiled 356 winter scenes acquired between 2000 and 2024 across eight Tibetan Plateau basins, delineated river ice using NDSI and RDRI, and extracted 24,674 pixel-level spectra. To define reproducible subclasses, we applied K-means clustering guided by the Silhouette Coefficient, Davies–Bouldin index, Calinski–Harabasz index, and Gap Statistic. Combined with stratified visual interpretation, this approach consistently supported four optical spectral subclasses: thin-snow-covered ice, thick ice cover, thin ice, and frazil ice. Within-sensor classification accuracy remained extremely high (overall accuracy ≥ 0.948; kappa ≥ 0.929), with the Backpropagation Neural Network (BPNN) and tree ensembles performing best. Crucially, evaluating the optimal BPNN architecture revealed exceptional multi-dimensional generalizability: a Leave-One-Basin-Out spatial cross-validation yielded a stable average OA > 99% with an average Kappa > 0.98, while a unified multi-sensor model achieved a robust OA of 90.14% and a Kappa of 0.86. The most stable discriminative cues were visible-band brightness, reflectance turnover near ~0.7 μm, and shortwave-infrared sensitivity to effective thickness and surface wetness. These results provide a sensor-aware benchmark for practical optical river ice spectral subclassification and clarify which multispectral bands most strongly constrain subclass separability.

Previous studies have shown that the maintenance hemodialysis (MHD) severity is pyramidal, with variations in self-management capabilities and supportive care needs generated by different patients during daily care procedures. This study aimed to explore the potential categories of self-management abilities and supportive care needs of patients undergoing MHD to inform the development of personalized intervention programs. We conducted a cross-sectional study using a self-made general data questionnaire, a hemodialysis self-management instrument, and an assessment of the supportive care needs of 302 outpatients undergoing MHD at two medical institutions (a tertiary hospital and a tertiary specialized hospital). Latent profile analysis was employed to classify self-management ability and supportive care needs. According to the latent profile analysis, the self-management and supportive care needs of MHD patients can be divided into three categories: the Self-Determination Model (72.7%),  the Collaborative Support Model (20.0%), and the Comprehensive Care Model (7.3%). Statistically significant differences were observed in payment methods and caregiver health ( P  < 0.05). Among the 11 dimensions, nine showed statistically significant differences across the three profiles (Kruskal–Wallis H test, all P  < 0.001), including all seven supportive care needs dimensions and two self-management dimensions (problem-solving and emotional processing). No significant differences were observed in self-care execution ( P  = 0.421) or partnership ( P  = 0.808). Medical staff can combine the self-management ability and supportive care needs of patients undergoing MHD to formulate personalized and targeted nursing intervention programs for different patients, which may help to improve patients’ self-management ability, respond to their care needs, and thus potentially contribute to the improvement of their quality of life.

As a systemic autoimmune disease, rheumatoid arthritis (RA) usually causes damage not only to joints, but also to other tissues and organs including the heart, kidneys, lungs, digestive system, eyes, skin, and nervous system. Excessive complications are closely related to the prognosis of RA patients and even lead to increased mortality. This article summarizes the serious complications of RA, focusing on its incidence, pathogenesis, clinical features, and treatment methods, aiming to provide a reference for clinicians to better manage the complications of RA.

Top management structures in large U.S. firms have changed significantly since the mid-1980s. The size of the executive team-the group of managers reporting directly to the CEO-doubled during this period. This growth was driven primarily by an increase in functional managers rather than general managers, a phenomenon we term \"functional centralization.\" Using panel data on senior management positions, we show that changes in the structure of the executive team are tightly linked to changes in firm diversification and information technology investments. These relationships depend crucially on the function involved; those closer to the product (\"product\" functions, e.g., marketing and R&D) behave differently from functions further from the product (\"administrative\" functions, e.g., finance, law, and human resources). We argue that this distinction is driven by differences in the information-processing activities associated with each function and apply this insight to refine and extend existing theories of centralization. We also discuss the implications of our results for organizational forms beyond the executive team. This paper was accepted by Bruno Cassiman, business strategy.

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions.