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Crystal engineering of metal−organic frameworks (MOFs) has allowed the construction of complex structures at atomic precision, but has yet to reach the same level of sophistication as organic synthesis. The synthesis of complex MOFs with multiple organic and/or inorganic components is ultimately limited by the lack of control over framework assembly in one-pot reactions. Herein, we demonstrate that multi-component MOFs with unprecedented complexity can be constructed in a predictable and stepwise manner under simple kinetic guidance, which conceptually mimics the retrosynthetic approach utilized to construct complicated organic molecules. Four multi-component MOFs were synthesized by the subsequent incorporation of organic linkers and inorganic clusters into the cavity of a mesoporous MOF, each composed of up to three different metals and two different linkers. Furthermore, we demonstrated the utility of such a retrosynthetic design through the construction of a cooperative bimetallic catalytic system with two collaborative metal sites for three-component Strecker reactions. The crystal engineering of metal–organic frameworks has led to the construction of complex structures, but has yet to reach the same level of sophistication as organic synthesis. Here, Zhou and colleagues use retrosynthetic chemistry to design and produce complex multi-component frameworks.

Due to more extreme weather events and accelerating sea-level rise, coastal sand dunes are subjected to more frequent storm wave inundation and surge impacts, which contribute to widespread coastal erosion problems. In this study, two novel bio-mediated methods, microbial-induced carbonate precipitation (MICP) and enzymatic-induced carbonate precipitation (EICP), were investigated and compared for their effectiveness in mitigating sand dune erosion under wave attack. Small-scale laboratory model tests were performed on MICP-treated, EICP-treated, and untreated sand dunes at dune slope angles and two wave intensities for up to 2 h. The cross-shore profile was captured continuously during the course of the erosion test. The erosion volume above the still water level (SWL) and landward retreat distance at the SWL were calculated based on the captured bed profiles. The results show that both EICP and MICP could substantially reduce sand dune erosion at mild-to-moderate wave and dune slope conditions. However, the effectiveness of MICP treatment deteriorated at steeper dune slopes with longer period of wave attack. Under the most adverse condition (i.e., steepest dune slope, biggest wave, and longest period of wave attack), neither EICP nor MICP could effectively mitigate erosion. Fundamentally, the variable effectiveness of MICP and EICP treatment for sand dune erosion control was attributed to the spatial distribution pattern of formed calcite precipitation, which was determined by the way how EICP and MICP were applied. The calcite precipitation was relatively uniform in EICP-treated sand dunes. In MICP-treated ones, however, substantial calcite precipitation concentrated in the shallow surface layer as confirmed by the surface penetration test and SEM observation.

The China Meteorological Forcing Dataset (CMFD) is the first high spatial-temporal resolution gridded near-surface meteorological dataset developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis datasets and in-situ station data. Its record begins in January 1979 and is ongoing (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in the CMFD, including 2-meter air temperature, surface pressure, and specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate. Validations against observations measured at independent stations show that the CMFD is of superior quality than the GLDAS (Global Land Data Assimilation System); this is because a larger number of stations are used to generate the CMFD than are utilised in the GLDAS. Due to its continuous temporal coverage and consistent quality, the CMFD is one of the most widely-used climate datasets for China. Measurement(s) temperature • pressure • humidity • atmospheric wind speed • radiation • precipitation process Technology Type(s) digital curation Factor Type(s) geographic location • time Sample Characteristic - Environment climate system Sample Characteristic - Location China Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.11558439

A major goal of metal–organic framework (MOF) research is the expansion of pore size and volume. Although many approaches have been attempted to increase the pore size of MOF materials, it is still a challenge to construct MOFs with precisely customized pore apertures for specific applications. Herein, we present a new method, namely linker labilization, to increase the MOF porosity and pore size, giving rise to hierarchical-pore architectures. Microporous MOFs with robust metal nodes and pro-labile linkers were initially synthesized. The mesopores were subsequently created as crystal defects through the splitting of a pro-labile-linker and the removal of the linker fragments by acid treatment. We demonstrate that linker labilization method can create controllable hierarchical porous structures in stable MOFs, which facilitates the diffusion and adsorption process of guest molecules to improve the performances of MOFs in adsorption and catalysis. Expanding pore sizes and volumes in metal-organic frameworks is challenging, but crucial for the encapsulation of larger guest molecules. Here, Zhou and colleagues report a linker labilization strategy to construct MOFs containing hierarchical pore architectures with dimensions ranging from 1.5 to 18 nm.

Due to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance. Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. The MSFF module enhances the capture of global features, thus improving the accuracy of the liver segmentation model. Through the aforementioned research, this paper proposes a liver segmentation network based on detail enhancement and multi-scale feature fusion (DEMF-Net). We conducted extensive experiments on the LiTS17 dataset, and the results demonstrate that the DEMF-Net model achieved significant improvements across various evaluation metrics. Therefore, the proposed DEMF-Net model can achieve precise liver segmentation.

The epithelial-mesenchymal transition （EMT） converts epithelial tumor cells into invasive and metastatic cancer cells, leading to mortality in cancer patients. Although TWIST is a master regulator of EMT and metastasis for breast and other cancers, the mechanisms responsible for TWIST-mediated gene transcription remain unknown. In this study, purification and characterization of the TWIST protein complex revealed that TWIST interacts with several components of the Mi2/nucleosome remodeling and deacetylase （Mi2/NuRD） complex, MTA2, RbAp46, Mi2 and HDAC2, and recruits them to the proximal regions of the E-cadherin promoter for transcriptional repression. Depletion of these TWIST complex components from cancer cell lines that depend on TWIST for metastasis efficiently suppresses cell migration and invasion in culture and lung metastasis in mice. These findings not only provide novel mechanistic and functional links between TWIST and the Mi2/NuRD complex but also establish new essential roles for the components of Mi2/NuRD complex in cancer metastasis.

Solar photovoltaic (PV) systems play a crucial role in addressing the growing demand for clean energy and mitigating climate change impacts. However, PV system performance is heavily influenced by the incident solar radiation on panel surfaces, with suboptimal tilt angles leading to significant power losses. Despite the critical importance of tilt angle optimization, many existing PV installations worldwide operate suboptimally due to simplified estimation methods or lack of site-specific optimization. This study presents a novel hybrid approach combining empirical and computational methods to determine optimal annual and monthly PV panel tilt angles using long-term hourly ERA5 reanalysis radiation data. Our results validate the effectiveness of ERA5 data for global tilt angle optimization, demonstrating a strong correlation with established cubic relations. Analysis of spatial and temporal patterns of optimized tilt angles reveals the influence of latitude, local atmospheric conditions, and seasonal variations on optimal PV panel inclination. A comprehensive assessment of the global PV inventory in 2018 shows that 44.6% of installed capacity is located in regions with solar power losses exceeding 1%, resulting in a total loss of 6154 GWh yr−1—equivalent to Luxembourg’s annual electricity consumption. Comparison between optimized tilt angles and those estimated using empirical cubic schemes reveals significant discrepancies in some regions, with annual power losses surpassing 3% when using empirical methods. These findings underscore the importance of accurate, location-specific tilt angle optimization to minimize solar power losses and maximize global PV inventory performance. Our research highlights the potential for substantial energy yield improvements through widespread adoption of optimized tilt angles in PV system design and retrofitting, contributing to enhanced renewable energy production and accelerated progress towards global sustainability goals.

Pine nematode is a highly contagious disease that causes great damage to the world’s pine forest resources. Timely and accurate identification of pine nematode disease can help to control it. At present, there are few research on pine nematode disease identification, and it is difficult to accurately identify and locate nematode disease in a single pine by existing methods. This paper proposes a new network, SCANet (spatial-context-attention network), to identify pine nematode disease based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images. In this method, a spatial information retention module is designed to reduce the loss of spatial information; it preserves the shallow features of pine nematode disease and expands the receptive field to enhance the extraction of deep features through a context information module. SCANet reached an overall accuracy of 79% and a precision and recall of around 0.86, and 0.91, respectively. In addition, 55 disease points among 59 known disease points were identified, which is better than other methods (DeepLab V3+, DenseNet, and HRNet). This paper presents a fast, precise, and practical method for identifying nematode disease and provides reliable technical support for the surveillance and control of pine wood nematode disease.

China’s large-scale tree planting programs are critical for achieving its carbon neutrality by 2060, but determining where and how to plant trees for maximum carbon sequestration has not been rigorously assessed. Here, we developed a comprehensive machine learning framework that integrates diverse environmental variables to quantify tree growth suitability and its relationship with tree numbers. Then, their correlations with biomass carbon stocks were robustly established. Carbon sink potentials were mapped in distinct tree-planting scenarios. Under one of them aligned with China’s ecosystem management policy, 44.7 billion trees could be planted, increasing forest stock by 9.6 ± 0.8 billion m³ and sequestering 5.9 ± 0.5 PgC equivalent to double China’s 2020 industrial CO 2 emissions. We found that tree densification within existing forests is an economically viable and effective strategy and so it should be a priority in future large-scale planting programs. China’s large-scale tree planting could sequester 5.9 ± 0.5 PgC by planting 44.7 billion trees. Tree densification in existing forests may be a more cost-effective strategy than afforestation.

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia 1 . Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50–70% 2 . Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)—high expression of which is a signature specific to the S-III subtype—alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it. A subtype of early-stage hepatocellular carcinoma characterized by disrupted cholesterol homeostasis and associated with a poor prognosis responds to treatment with the SOAT1 inhibitor avasimibe in a patient-derived xenograft mouse model.