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The thermal transformation of metal–organic frameworks (MOFs) generates a variety of nanostructured materials, including carbon-based materials, metal oxides, metal chalcogenides, metal phosphides and metal carbides. These derivatives of MOFs have characteristics such as high surface areas, permanent porosities and controllable functionalities that enable their good performance in sensing, gas storage, catalysis and energy-related applications. Although progress has been made to tune the morphologies of MOF-derived structures at the nanometre scale, it remains crucial to further our knowledge of the relationship between morphology and performance. In this Review, we summarize the synthetic strategies and optimized methods that enable control over the size, morphology, composition and structure of the derived nanomaterials. In addition, we compare the performance of materials prepared by the MOF-templated strategy and other synthetic methods. Our aim is to reveal the relationship between the morphology and the physico-chemical properties of MOF-derived nanostructures to optimize their performance for applications such as sensing, catalysis, and energy storage and conversion. Nanomaterials derived from metal–organic frameworks (MOFs) show good performance in sensing, gas storage, catalysis and energy-related applications. In this Review, the influence of the morphology of MOF-derived nanostructures on their performance is elucidated, and the opportunities in this field are discussed.

Mouse embryonic stem cell (mESC) self‐renewal can be maintained by activation of the leukaemia inhibitory factor (LIF)/signal transducer and activator of transcription 3 (Stat3) signalling pathway or dual inhibition (2i) of glycogen synthase kinase 3 (Gsk3) and mitogen‐activated protein kinase kinase (MEK). Several downstream targets of the pathways involved have been identified that when individually overexpressed can partially support self‐renewal. However, none of these targets is shared among the involved pathways. Here, we show that the CP2 family transcription factor Tfcp2l1 is a common target in LIF/Stat3‐ and 2i‐mediated self‐renewal, and forced expression of Tfcp2l1 can recapitulate the self‐renewal‐promoting effect of LIF or either of the 2i components. In addition, Tfcp2l1 can reprogram post‐implantation epiblast stem cells to naïve pluripotent ESCs. Tfcp2l1 upregulates Nanog expression and promotes self‐renewal in a Nanog‐dependent manner. We conclude that Tfcp2l1 is at the intersection of LIF‐ and 2i‐mediated self‐renewal pathways and plays a critical role in maintaining ESC identity. Our study provides an expanded understanding of the current model of ground‐state pluripotency. Upregulation under LIF/2i conditions in ESCs identifies Tfcp2I1 as a convergence point of various pluripotency pathways. As direct regulator of Nanog expression, Tfcp2I1 is ultimately linked to core self‐renewing circuits.

Metastasis is a major cause of death in patients with breast cancer. In the process of cancer development, epithelial-mesenchymal transition (EMT) is crucial to promoting the invasion and migration of tumor cells. In a previous study, the role of resveratrol in migration and metastasis was investigated in MDA-MB-231 (MDA231) human breast cancer cells and a xenograft-bearing mouse model. Additionally, the related mechanism was explored. In the present study, in vitro Transwell assays showed that resveratrol can inhibit the migration of transforming growth factor (TGF)-β1-induced MDA231 cells in a concentration-dependent manner. An enzyme-linked immunosorbent assay (ELISA) showed that resveratrol can reduce the secretion of matrix metalloproteinase (MMP)-2 and MMP-9. Immunofluorescence was performed to confirm the expression of EMT-related markers. Immunofluorescence assays confirmed that resveratrol changed the expression of the EMT-related markers E-cadherin and vimentin. Western blot analysis demonstrated that resveratrol decreased the expression levels of MMP-2, MMP-9, Fibronectin, α-SMA, P-PI3K, P-AKT, Smad2, Smad3, P-Smad2, P-Smad3, vimentin, Snail1, and Slug, as well as increased the expression levels of E-cadherin in MDA231 cells. In vivo, resveratrol inhibited lung metastasis in a mouse model bearing MDA231 human breast cancer xenografts without marked changes in body weight or liver and kidney function. These results indicate that resveratrol inhibits the migration of MDA231 cells by reversing TGF-β1-induced EMT and inhibits the lung metastasis of MDA231 human breast cancer in a xenograft-bearing mouse model.

Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.

The bottleneck of large‐scale implementation of electrocatalytic water‐splitting technology lies in lacking inexpensive, efficient, and durable catalysts to accelerate the sluggish oxygen evolution reaction kinetics. Owing to more metallic features, transition metal telluride (TMT) with good electronic conductivity holds promising potential as an ideal type of electrocatalysts for oxygen evolution reaction (OER), whereas most TMTs reported up to now still show unsatisfactory OER performance that is far below corresponding sulfide and selenide counterparts. Here, the activation and stabilization of cobalt telluride (CoTe) nanoarrays toward OER through dual integration of sulfur (S) doping and surface oxidization is reported. The as‐synthesized CoO@S‐CoTe catalyst exhibits a low overpotential of only 246 mV at 10 mA cm−2 and a long‐term stability of more than 36 h, outperforming commercial RuO2 and other reported telluride‐based OER catalysts. The combined experimental and theoretical results reveal that the enhanced OER performance stems from increased active sites exposure, improved charge transfer ability, and optimized electronic state. This work will provide a valuable guidance to release the catalytic potential of telluride‐based OER catalysts via interface modulating engineering. Surface oxidized S‐doped CoTe nanoarrays are designed and synthesized on nickel foam. The obtained best CoO@S‐CoTe catalyst even exhibits an excellent OER performance, in terms of over potential and stability, that can be comparable to benchmark RuO2. The synergistic doping and surface oxidation strategies are provided to solve the activity and structural stability problems of OER telluride catalysts.

To reduce the cost of production and the pollution of the environment that is due to the overapplication of herbicide in paddy fields, the location information of rice seedlings and weeds must be detected in site-specific weed management (SSWM). With the development of deep learning, a semantic segmentation method with the SegNet that is based on fully convolutional network (FCN) was proposed. In this paper, RGB color images of seedling rice were captured in paddy field, and ground truth (GT) images were obtained by manually labeled the pixels in the RGB images with three separate categories, namely, rice seedlings, background, and weeds. The class weight coefficients were calculated to solve the problem of the unbalance of the number of the classification category. GT images and RGB images were used for data training and data testing. Eighty percent of the samples were randomly selected as the training dataset and 20% of samples were used as the test dataset. The proposed method was compared with a classical semantic segmentation model, namely, FCN, and U-Net models. The average accuracy rate of the SegNet method was 92.7%, whereas the average accuracy rates of the FCN and U-Net methods were 89.5% and 70.8%, respectively. The proposed SegNet method realized higher classification accuracy and could effectively classify the pixels of rice seedlings, background, and weeds in the paddy field images and acquire the positions of their regions.

This study provides crucial insights into phosphate acquisition in SAR11 bacteria, a key group of oligotrophic microorganisms that thrive in nutrient-limited marine ecosystems. By characterizing the unique structural features of Cp PstS, including its distinct hydrogen-bonding network and expanded substrate-binding cavity, this research sheds light on how SAR11 bacteria adapt to limited phosphorus availability. The discovery that Cp PstS may also accommodate organic phosphorus compounds broadens our understanding of microbial nutrient acquisition. These findings have significant implications for marine biogeochemical cycles and offer new perspectives on the evolution of nutrient transport mechanisms in marine microorganisms.

Metal–organic frameworks (MOFs), combining the merits of inorganic and organic components, have received huge attention over the past two decades. Owing to the diversity of structures and excellent physicochemical properties, MOFs have been regarded as promising materials in wide‐ranging fields. However, the application as electrocatalysts is severely hampered by the low electrical conductivity of pristine MOFs. Previous studies have demonstrated that the development of conductive MOFs can be a feasible solution to this issue. Herein, various synthetic strategies to construct conductive MOFs are briefly summarized, including intrinsically conductive MOFs, guest‐based conductive MOFs, and conductive MOF composites. Some successful examples of conductive MOFs used as electrocatalysts in electrochemical energy conversions are also introduced. Finally, the existing problems and present prospects for the ulterior applications of conductive MOFs in electrocatalysis for renewable energy conversion and other reactions are highlighted. This review summarizes the recent advances on the development of conductive metal–organic frameworks (MOFs), including intrinsically conductive MOFs, guest‐based conductive MOFs, and conductive MOF composites. Meanwhile, the electrochemical applications of the emerging conductive MOFs are highlighted.

Photocatalytic carbon dioxide (CO2) reduction can utilize solar light to convert CO2 to high value‐added products, which thus are recognized as an intriguing strategy to solve excessive CO2 emissions. Metal–organic framework (MOF)‐based photocatalysts have shown high potential in the field of CO2 reduction due to their high porosity and tunable structure. In this review, the recent progress achieved in the rational design of MOF‐based photocatalysts, including the pure MOF materials, MOF‐based composites, and the derivatives of MOFs, for the reduction of CO2, are summarized and the developed modification strategies to enhance the photocatalytic performance of MOF‐based photocatalysts are highlighted. The current pending issues and the outlook for the future development are also discussed. This review summarizes the recent progress achieved in the rational design of metal–organic framework (MOF)‐based photocatalysts, including pure MOF materials, MOF‐based composites, and the derivatives of MOFs, for the reduction of CO2. Meanwhile, the developed modification strategies to enhance their photocatalytic performance are highlighted.

CRISPR-based genome editing has been widely implemented in various cell types. In silico single guide RNA (sgRNA) design is a key step for successful gene editing using the CRISPR system, and continuing efforts are aimed at refining in silico sgRNA design with high on-target efficacy and reduced off-target effects. Many sgRNA design tools are available, but careful assessments of their application scenarios and performance benchmarks across different types of genome-editing data are needed. Efficient in silico models can be built that integrate current heterogeneous genome-editing data to derive unbiased sgRNA design rules and identify key features for improving sgRNA design. Comprehensive evaluation of on-target and off-target effects of sgRNA will allow more precise genome editing and gene therapies using the CRISPR system. CRISPR-based genome editing is widely implemented in various cell types and organisms. This rapidly advancing technology mainly comprises the following types of genetic perturbations: gene knockout (KO), gene knockin (KI) for genome editing, and inhibition or activation of gene expression (CRISPRi/a). A major challenge in effectively applying the CRISPR/Cas9 endonuclease system to all of these genetic perturbations is designing highly-efficient single-guide (sg)RNA with minimal off-target cleavage. Because the CRISPR technique has quickly advanced, increasing volumes of genome-editing data have accumulated and challenging computational problems have emerged. In silico sgRNA design has become a key issue for successful gene-editing experiments and will allow CRISPR studies to take advantage of various bioinformatics and computational techniques.