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The tension between reducing CO2 emissions and economic growth has become increasingly prominent in recent years, while China is vigorously promoting ecological civilization to achieve sustainable development. We analyze the decoupling of emissions at the national and provincial levels of the Chinese economy from the perspective of historical patterns and current drivers from 1997 to 2019. Also, we developed three scenarios (i.e., pessimistic, median, and optimistic scenarios) to analyze the impact of decoupling relationship changes. We find that China’s national decoupling relationship has eased since 1997, but it has not yet reached the ideal state, with provinces mainly exhibiting weak decoupling. The EKC hypothesis is tested for the whole country and 30 provinces and find that 15 provinces have two turning points, 13 provinces have one turning point, and the others have no turning point. Based on the scenario analysis, the total emissions in the Pessimistic Scenario (S1) without any improvement of decoupling would increase by 73.97% compared to the level of 2019. However, the total emissions in the Optimistic Scenario (S3), in which all provinces obtained strong decoupling, are almost half of the level of 2019. This is mainly from the reduction of emissions in the western less developed regions (e.g., Shanxi, Inner Mongolia and Xinjiang) and developed coastal regions (e.g., Jiangsu and Shandong). On the basis of the results of factor analysis, we put forward policy recommendations for expand electrification, optimize industrial structure, and promote technological innovation.

Accurate protein contact map prediction (PCMP) is essential for precise protein structure estimation and further biological studies. Recent works achieve significant performance on this task with high quality multiple sequence alignment (MSA). However, the PCMP accuracy drops dramatically while only poor MSA (e.g., absolute MSA count less than 10) is available. Therefore, in this paper, we propose the Contact-Distil to improve the low homologous PCMP accuracy through knowledge distillation on a self-supervised model. Particularly, two pre-trained transformers are exploited to learn the high quality and low quality MSA representation in parallel for the teacher and student model correspondingly. Besides, the co-evolution information is further extracted from pure sequence through a pretrained ESM-1b model, which provides auxiliary knowledge to improve student performance. Extensive experiments show Contact-Distil outperforms previous state-of-the-arts by large margins on CAMEO-L dataset for low homologous PCMP, i.e., around 13.3% and 9.5% improvements against Alphafold2 and MSA Transformer respectively when MSA count less than 10.

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Traditional Chinese medicine (TCM) reportedly has potential therapeutic effects against LC. Objective: This study aimed to investigate the antitumor efficacy of Yishen Qutong granule (YSQTG) in primary LC treatment, to identify its key active pharmaceutical ingredients (APIs), and to explore its possible mechanism of action. Methods: The antitumor role of YSQTG was validated via cell function assays and a xenograft tumor model. Then, high-performance liquid chromatography-mass spectrometry (HPLC–MS) was performed to determine the objective precipitation components of YSQTG, followed by target prediction through reference to databases. Subsequently, the proportion of the predicted targets that underwent actual changes was identified via RNA-sequencing. Enrichment analysis was performed to explore the possible mechanisms of action. Hub genes were screened, and western blotting was used to verify their protein expression levels to identify the core target. Molecular docking between the active compounds and the verified core target was performed, combined with an evaluation of the potential efficacy of candidate compounds using meta-analysis to screen the candidate key APIs. Results: Experiments confirmed that YSQTG could inhibit LC cell proliferation, induce apoptosis in vitro, and inhibit lung tumor growth in vivo. HPLC-MS, RNA-seq, and enrichment analysis showed that oxidative stress-related pathways were the possible mechanism of YSQTG in primary LC treatment. Western blot verification indicated that heme oxygenase 1 (HMOX1, HO-1) could be the core target. Molecular docking and meta-analysis suggested that genistein and quercetin were the candidate key APIs. Conclusion: YSQTG and its active ingredients, genistein and quercetin, may have therapeutic effects against LC through their action on the downregulation of oxidative stress-related HMOX1 protein expression.

The surficial cycling of Mg is directly coupled with the global carbon cycle, a predominant control of Earth’s climate. However, how Earth’s surficial Mg cycle evolved with time had been elusive. Magnesium isotope signatures of seawater (δ26Mgsw) track the surficial Mg cycle, which could provide crucial information on the carbon cycle in Earth’s history. Here, we present a reconstruction of δ26Mgsw evolution over the last 2 billion years using marine halite fluid inclusions and sedimentary dolostones. The two independent archives yield consistent evolutionary trends of δ26Mgsw for the past 430 million years, and the dolostone records extend the δ26Mgsw curve to 2 billion years ago. Modeling results of the net CO2 sequestration efficiency (EMg−CO2) by the surficial Mg cycle based on the δ26Mgsw record reveal a secular decline EMg−CO2 during the past 2 billion years, with the periods of low EMg−CO2 coinciding with ice ages in the Phanerozoic. Our work underlines a previously under-appreciated, but indispensable role of dolostones in regulating Earth’s climate on geologic time scales.

In vitrostudies indicate the neurodevelopmental disorder gene Myelin Transcription Factor 1 Like (MYT1L) suppresses non-neuronal lineage genes during fibroblast-to-neuron direct differentiation. However, MYT1L’s molecular and cellular functions during differentiation in the mammalian brain have not been fully characterized. Here, we found that MYT1L loss leads to up-regulated deep layer (DL) but down-regulated upper layer (UL) neuron gene expression, corresponding to an increased ratio of DL/UL neurons in mouse cortex. To define potential mechanisms, we conducted Cleavage Under Targets & Release Using Nuclease (CUT&RUN) to map MYT1L binding targets in mouse developing cortex and adult prefrontal cortex (PFC), and to map epigenetic changes due to MYT1L mutation. We found MYT1L mainly binds to open chromatin, but with different transcription factor co-occupancies between promoters and enhancers. Likewise, multi-omic dataset integration revealed that, at promoters, MYT1L loss does not change chromatin accessibility but does increase H3K4me3 and H3K27ac, activating both a subset of earlier neuronal development genes as well asBcl11b, a key regulator for DL neuron development. Meanwhile, we discovered that MYT1L normally represses the activity of neurogenic enhancers associated with neuronal migration and neuronal projection development by closing chromatin structures and promoting removal of active histone marks. Further, we show MYT1L interacts with SIN3B and HDAC2in vivo, providing potential mechanisms underlying any repressive effects on histone acetylation and gene expression. Overall, our findings provide a comprehensive map of MYT1L bindingin vivoand mechanistic insights to how MYT1L facilitates neuronal maturation.

Motivation As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. Results On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. Availability and implementation Web server: https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/. Training code and pre-trained model are available at.

Heterostructured construction is regarded as a valuable approach to improve photoelectrochemical (PEC) performances. Herein, porous hollow NiS@NiO spheres were prepared derived from the Ni(TCY) MOFs precursor. Photoactive TiO

Transcription factors (TFs) enact precise regulation of gene expression through site-specific, genome-wide binding. Common methods for TF-occupancy profiling, such as chromatin immunoprecipitation, are limited by requirement of TF-specific antibodies and provide only end-point snapshots of TF binding. Alternatively, TF-tagging techniques, in which a TF is fused to a DNA-modifying enzyme that marks TF-binding events across the genome as they occur, do not require TF-specific antibodies and offer the potential for unique applications, such as recording of TF occupancy over time and cell type specificity through conditional expression of the TF–enzyme fusion. Here, we create a viral toolkit for one such method, calling cards, and demonstrate that these reagents can be delivered to the live mouse brain and used to report TF occupancy. Further, we establish a Cre-dependent calling cards system and, in proof-of-principle experiments, show utility in defining cell type-specific TF profiles and recording and integrating TF-binding events across time. This versatile approach will enable unique studies of TF-mediated gene regulation in live animal models.

Pavement runoff sampling points were set up on the main roads of Chengdu city. Six rainfall-runoff events from July to September in 2017 were sampled by synchronous observation of rainfall, runoff and pollution. The concentration changes of copper, lead, zinc, chromium and cadmium in the runoff process were monitored, and the pollution emission regularity and initial scouring effect were studied. The results show that the emission regularity of pavement runoff pollution is closely related to rainfall characteristics and pollutant occurrence, and the concentration of dissolved heavy metals reaches its peak at the initial stage of runoff. The peak time of particulate heavy metal concentration lagged slightly behind that of rainfall intensity. There is a big difference between the strength of initial scouring degree and dissolved heavy metals the stronger the initial scouring degree of total heavy metals, the weaker the dissolved heavy metals. Reducing pavement runoff in the early stage of rainfall is an effective means to control heavy metal pollution.

Fuzzy c-means clustering (FCM) has proved highly successful in the manipulation and analysis of image information, such as image segmentation. However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process. To tackle these problems, a new objective function of FCM is developed in this work. The main innovation work and results of this paper are outlined as follows. First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving. Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value. In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood. The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss. We perform experiments on both synthetic and real images. Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability to noise and edge-preserving in image segmentation.

A green and efficient visible-light promoted aerobic oxidative C–C bond cleavage of vicinal diols in micellar medium has been developed. This protocol used graphitic carbon nitride with nitrogen vacancies (CN620) as a metal-free recyclable photocatalyst and CTAB as surfactant in water. Control experiments and the ESR results indicated that superoxide radicals and valence band holes played an important role in the reaction. Further isotope experiments suggested both a β-scission/HAT pathway and an oxidation/hydrolysis/dehydration pathway for the reaction, which is different from previous reports. The semiconductor/micellar catalyst system can be recycled at least 10 times without a significant reduction in activity. Furthermore, this reaction could be carried out under solar light irradiation and was applicable to large-scale reactions with similar results.

Drosophila cellularization is a special form of cleavage that converts syncytial embryos into cellular blastoderms by partitioning the peripherally localized nuclei into individual cells. An early event in cellularization is the recruitment of non-muscle myosin II (“myosin”) to the basal tip of cleavage furrows, where myosin forms an interconnected basal array before reorganizing into individual cytokinetic rings. The initial recruitment and organization of basal myosin are regulated by a cellularization-specific gene, dunk, but the underlying mechanism is unclear. Through a genome-wide yeast two-hybrid screen, we identified anillin (Scraps in Drosophila), a conserved scaffolding protein in cytokinesis, as the primary binding partner of Dunk. We show that Dunk regulates the localization of anillin at the cleavage furrows during early cellularization and functionally interacts with anillin in regulating basal myosin. Furthermore, we show that anillin colocalizes with myosin since the onset of cellularization and is required for the initial recruitment and maintenance of myosin at the basal array, prior to the well-documented function of anillin in regulating cytokinetic ring assembly. Based on these results, we propose that Dunk regulates myosin recruitment and organization during early cellularization by interacting with and regulating anillin.

Objective. To explore the feasibility of automatically detecting the degree of meniscus injury by radiomics fusion of dual-mode magnetic resonance imaging (MRI) features of sagittal and coronal planes of the knee joint. Methods. This retrospective study included 164 arthroscopically confirmed meniscus injuries in 152 patients admitted to the Department of Orthopaedics of our hospital from July 2018 to March 2021. A total of 1316-dimensional radiomics signatures were extracted from single-mode sagittal and coronal plane images of menisci, respectively. Then, the sagittal and coronal plane features were fused to form a dual-mode joint feature group with a total of 2632-dimensional radiomics signatures. The minimum redundancy maximum relevance (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate optimal radiomics signatures. The single-mode sagittal plane feature model (Model 1), single-mode coronal plane feature model (Model 2), and the combined sagittal and coronal plane feature model (Model 3) performance were tested by receiver operating characteristic (ROC) curves and Delong test. The calibration curve test was used to verify the reliability of radiomics signatures of the three models. Results. The average intra- and interobserver intraclass correlation coefficients (ICCs) of the most significant 8-dimensional radiomics signatures of Model 1 and Model 2 were 0.935 (range 0.832-0.998) and 0.928 (range 0.845-0.998), respectively. All the three models had good detection performance; Model 3 had the most significant performance (the areas under the curve (AUCs) of training, and validation sets were 0.947 and 0.923, respectively), which was superior to Model 1 (AUCs of training set and validation set were 0.889 and 0.876, respectively) and Model 2 (AUCs of training set and validation set were 0.831 and 0.851, respectively). The detection probability of training and validation sets in the three models was highly consistent with the actual clinical probability. Conclusions. It is feasible to establish a model for automatic detection of meniscus damage by means of radiomics. The detection performance of the dual-mode knee MRI model is better than that of any single-mode model, showing potent feature analysis ability and outstanding detection performance.

In order to understand the insulation operation state of cable joint in real time, an evaluation method of cable joint insulation state based on improved TOPSIS with variable weight is proposed. Taking the partial discharge(PD) data monitored online as an example, the representative PD characteristic parameters is selected. Furthermore, Sen + Mann-Kendall test method is used to quantitatively calculate the trend change of corresponding PD characteristic parameters. Then the deterioration degree is introduced to normalize the PD characteristic parameters. Considering the change of weight under extreme deterioration, a variable weight function is constructed to optimize the constant weight. Finally, an improved TOPSIS method is applied to construct the cable intermediate joint state evaluation model. The case analysis shows that this model is more accurately than the traditional model in assessing the insulation condition of cable intermediate joint.

HKUST-1 MOFs and its derivative HKUST-CuO were coupled with TiO2 nanoparticles to form the heterogeneous composites of HKUST-1/TiO2 and HKUST-CuO/TiO2 based on their well-suitable bandgap energies (Eg). Compared with mono-component HKUST-1 or HKUST-CuO, the prepared composites displayed photoelectrochemical (PEC) response due to the synergistic effect from their heterogeneous structure. Higher photocurrent response was obtained on HKUST-CuO/TiO2-modified ITO electrode (HKUST-CuO/TiO2/ITO), which could be attributed to the hollow structure with a thin shell of HKUST-CuO greatly enhancing visible spectra harvesting. The CuO component in HKUST-CuO not only could accelerate electron transfer on the heterojunction interface but also effectively separate the photo-generated charge carriers (e−1/h+). Based on the excellent PEC performance of prepared photoactive composite material, under visible-light excitation (λ ≥ 420 nm) and with a working potential of 0 V (vs. Ag/AgCl), the S1 (probe DNA)/HKUST-CuO/TiO2/ITO PEC platform was successfully fabricated for colitoxin DNA detection without using ascorbic acid (AA) as an electron donor. Compared with the analysis results on S1/HKUST-1/TiO2/ITO electrode, S1/HKUST-CuO/TiO2/ITO displayed a wider linear response range from 1.0 × 10−6 to 4.0 × 10−1 nM with a lower detection limit of 3.73 × 10−7 nM (S/N = 3), the linear regression equation was ΔI (10−6 A) =0.5549–0.1858 log (CS2/M), which confirmed the HKUST-CuO could improve sensitivity because of its prominent PEC property. The relative standard deviation (RSD) of the PEC sensor for target DNA detection of 2.0 × 10−4 nM was 7.4%. The proposed DNA biosensor also possessed good specificity and stability. Hence, this reported work was a promising strategy for molecular diagnosis in the bio-analysis field.

We propose a multi-color method for the self-correction of the air refractive index based on the dispersive interferometry of an optical frequency comb. This method can be applied to correct the air refractive index for long-distance measurements in moist air. Optical lengths of multiple wavelengths were obtained simultaneously by the dispersive interferometry of an optical frequency comb. Interferometric measurement results and calculations from the empirical equation of air refractive indices were similar, with a standard deviation of 2.0 × 10 − 9 throughout the 2 h continuous measurement period. By applying the multi-color method, correction of the air refractive index with an uncertainty of 3.5 × 10 − 7 was achieved.

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43,695 single cells from peripheral blood mononuclear cells. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.