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Since the launch of lithium-ion batteries, elements (such as silicon, tin, or aluminum) that can be alloyed with lithium have been expected as anode materials, owing to larger capacity. However, their successful application has not been accomplished because of drastic structural degradation caused by cyclic large volume change during battery reactions. To prolong lifetime of alloy anodes, we must circumvent the huge volume strain accompanied by insertion/extraction of lithium. Here we report that by using aluminum-foil anodes, the volume expansion during lithiation can be confined to the normal direction to the foil and, consequently, the electrode cyclability can be markedly enhanced. Such a unidirectional volume-strain circumvention requires an appropriate hardness of the matrix and a certain tolerance to off-stoichiometry of the resulting intermetallic compound, which drive interdiffusion of matrix component and lithium along the normal-plane direction. This metallurgical concept would invoke a paradigm shift to future alloy-anode battery technologies. Alloy anode materials in lithium batteries usually suffer from fatal structural degradation due to the large volume change during cycling. Here the authors report a design in which Al foil serves as both anode and current collector to circumvent the strain.

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.

Soil organic matter (SOM) and pH are essential soil fertility indictors of paddy soil in the middle-lower Yangtze Plain. Rapid, non-destructive and accurate determination of SOM and pH is vital to preventing soil degradation caused by inappropriate land management practices. Visible-near infrared (vis-NIR) spectroscopy with multivariate calibration can be used to effectively estimate soil properties. In this study, 523 soil samples were collected from paddy fields in the Yangtze Plain, China. Four machine learning approaches—partial least squares regression (PLSR), least squares-support vector machines (LS-SVM), extreme learning machines (ELM) and the Cubist regression model (Cubist)—were used to compare the prediction accuracy based on vis-NIR full bands and bands reduced using the genetic algorithm (GA). The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to inter-quartile distance (RPIQ) were used to assess the prediction accuracy. The ELM with GA reduced bands was the best model for SOM (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87) and pH (R2 = 0.76, RMSE = 0.43, RPIQ = 2.15). The performance of the LS-SVM for pH prediction did not differ significantly between the model with GA (R2 = 0.75, RMSE = 0.44, RPIQ = 2.08) and without GA (R2 = 0.74, RMSE = 0.45, RPIQ = 2.07). Although a slight increase was observed when ELM were used for prediction of SOM and pH using reduced bands (SOM: R2 = 0.81, RMSE = 5.17, RPIQ = 2.87; pH: R2 = 0.76, RMSE = 0.43, RPIQ = 2.15) compared with full bands (R2 = 0.81, RMSE = 5.18, RPIQ = 2.83; pH: R2 = 0.76, RMSE = 0.45, RPIQ = 2.07), the number of wavelengths was greatly reduced (SOM: 201 to 44; pH: 201 to 32). Thus, the ELM coupled with reduced bands by GA is recommended for prediction of properties of paddy soil (SOM and pH) in the middle-lower Yangtze Plain.

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 .

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.

Nanos2 plays a key role in self-renewing spermatogenic stem cells (SSCs) and maintains the stem cell state during spermatogenesis. Alleles of the Nanos2 gene knockout showed germline ablated but otherwise structurally normal. To identify the probable ceRNA regulator involved in the process of spermatogenesis by Nanos2 , whole transcriptome sequencing was performed in the testes between Nanos2 knock out mice and wild type mice. Finally, a total of 8644 Differentially expressed (DE) mRNAs,180 DE miRNAs, 9538 DE lncRNA and 481 DE circRNAs were identified. Three of each RNAs were selected randomly and identified by real-time PCR to verify the accuracy of sequencing. GO and KEGG functional enrichment analyses revealed similar result of DE mRNAs and target of DE miRNAs/lncRNAs/ circRNAs, mainly involved in the generation, composition, and activity of sperm cells. Furthermore, the regulatory ceRNA network of miRNA(up)-circRNA-lncRNA-mRNA and miRNA(down)-circRNA-lncRNA-mRNA were constructed based on the common targeted miRNA.The results enable us to better understand the interaction of coding RNA and non coding RNA in regulating the generation of spermatogenic stem cells through Nanos2 pathway, and also provided novel insights into molecular mechanism of spermatogenesis.

Wind shapes high‐altitude winter snow mass balance and influences water resources by controlling snow accumulation, erosion, and sublimation loss, yet accurately quantifying these processes remains challenging in high‐altitude regions like the Tibetan Plateau due to complex wind‐snow interactions and extreme measurement conditions. To address these challenges, we present an integrated observation system to monitor wind‐blown snow processes and develop a Gaussian kernel‐based probabilistic classification method that incorporates measurement uncertainties to identify wind‐driven snow events. This method enables more robust analysis of rapid snow mass changes compared to traditional classification. The study site is in the northeastern Tibetan Plateau at 4,147 m elevation with strong winds and frequent winter snowfall. Our results show that wind‐driven snow deposition and erosion events account for 68.5% of observed snow mass changes, while purely precipitation‐driven accumulation events only contribute 3.1% of total changes, with the remaining 28.4% being mixed events involving both precipitation and wind‐driven processes. Our results provide observational evidence that blowing snow sublimation is amplified when wind speeds exceed approximately 8 m s−1 ${\\mathrm{s}}^{-1}$, highlighting the pivotal role of suspended particles in enhancing evapotranspiration losses. This continuous wind‐driven reshaping of the snowpack leads to rapid changes in snow depth, density, and even thermal properties, challenging traditional modeling approaches that assume more gradual layer evolution. This study provides a robust method for identifying wind‐driven snow events and quantifying their influences on snow mass balance. Our findings emphasize the importance of incorporating wind‐driven processes in high‐altitude snow models and monitoring systems to better understand snow dynamics.

Frozen ground has an important role in regional hydrological cycles and ecosystems, particularly on the Qinghai–Tibetan Plateau (QTP), which is characterized by high elevations and a dry climate. This study modified a distributed, physically based hydrological model and applied it to simulate long-term (1971–2013) changes in frozen ground its the effects on hydrology in the upper Heihe basin, northeastern QTP. The model was validated against data obtained from multiple ground-based observations. Based on model simulations, we analyzed spatio-temporal changes in frozen soils and their effects on hydrology. Our results show that the area with permafrost shrank by 8.8 % (approximately 500 km2), predominantly in areas with elevations between 3500 and 3900 m. The maximum depth of seasonally frozen ground decreased at a rate of approximately 0.032 m decade−1, and the active layer thickness over the permafrost increased by approximately 0.043 m decade−1. Runoff increased significantly during the cold season (November–March) due to an increase in liquid soil moisture caused by rising soil temperatures. Areas in which permafrost changed into seasonally frozen ground at high elevations showed especially large increases in runoff. Annual runoff increased due to increased precipitation, the base flow increased due to changes in frozen soils, and the actual evapotranspiration increased significantly due to increased precipitation and soil warming. The groundwater storage showed an increasing trend, indicating that a reduction in permafrost extent enhanced the groundwater recharge.

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.

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m−1), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R2 = 0.88, root mean square error (RMSE) = 4.89 dS m−1, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future.