A Biodegradable, Stretchable, Healable, and Self-Powered Optoelectronic Synapse Based on Ionic Gelatins for Neuromorphic Vision System.

Optoelectronic synapses have gained increasing attentions as a fundamental building block in the development of neuromorphic visual systems. However, it remains a challenge to integrate multiple functions into a single optoelectronic synapse that can be widely applied in wearable artificial intelligence and implantable neuromorphic vision systems. In this study, a stretchable optoelectronic synapse based on biodegradable ionic gelatin heterojunction is successfully developed. This device exhibits self-powered synaptic plasticity behavior with broad spectral response and excellent elastic properties, yet it degrades rapidly upon disposal. After complete cleavage, the device can be fully repaired within 1 min, which is mainly attributed to the non-covalent interactions between different molecular chains. Moreover, the recovery and reprocessing of the ionic gelatins result in optoelectronic properties that are virtually indistinguishable from their original state, showcasing the resilience and durability of ionic gelatins. The combination of biodegradability, stretchability, self-healing, zero-power consumption, ease of large-scale preparation, and low cost makes the work a major step forward in the development of biodegradable and stretchable optoelectronic synapses.

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information.

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification.

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Markerauto2: A fast and robust fully automatic fiducial marker-based tilt series alignment software for electron tomography.

Cryoelectron tomography (cryo-ET) has become an indispensable technology for visualizing in situ biological ultrastructures, where the tilt series alignment is the key step to obtain a high-resolution three-dimensional reconstruction. Specifically, with the advent of high-throughput cryo-ET data collection, there is an increasing demand for high-accuracy and fully automatic tilt series alignment, to enable efficient data processing. Here, we propose Markerauto2, a fast and robust fully automatic software that enables accurate fiducial marker-based tilt series alignment. Markerauto2 implements the following novel pipelines: (1) an accelerated high-precision fiducial marker detection with wavelet multiscale template, (2) an ultra-fast and robust fiducial marker tracking supported by hashed geometric features, (3) a high-angle fiducial marker supplementation strategy to produce more complete tracks, and (4) a precise and robust calibration of projection parameters with group-weighted parameter optimization. Comprehensive experiments conducted on both simulated and real-world datasets demonstrate the robustness, efficiency, and effectiveness of the proposed software.

[Effects of Biochar and Straw Return on Soil Microbial Community Characteristics and Functional Differences in Saline Water Drip Irrigation Cotton Fields].

In arid areas, fresh water resources are insufficient, and agricultural water mainly depends on shallow saline groundwater. However, long-term saline irrigation will cause soil salt accumulation and soil environment deterioration, which is not conducive to crop growth. In this study, based on the long-term irrigation of fresh water (0.35 dS·m-1, FW) and saline water (8.04 dS·m-1, SW), biochar (3.7 t·hm-2, BC) and straw (6 t·hm-2, ST) were added to the soil by an equal-carbon design. The aim was to clarify the effects of biochar and straw returning on the physical and chemical properties and microbial community structure of salinized soil. The results showed that saline irrigation significantly increased soil water content, electrical conductivity, available phosphorus, and total carbon content but significantly decreased pH value and available potassium content. The contents of available phosphorus, available potassium, and total carbon in soil were significantly increased by biochar and straw returning, but the conductivity value of soil irrigated with saline water was significantly decreased. The dominant bacteria in each treatment were Proteobacteria, Actinomycetes, Acidobacteria, Chloromycetes, and Blastomonas. Saline water irrigation significantly increased the relative abundance of Blastomonas and Proteobacteria but significantly decreased the relative abundance of Acidobacteria and Actinobacteria. Under the condition of fresh water irrigation, the relative abundance of Chlorocurvula was significantly reduced by the return of biochar. Straw returning significantly increased the relative abundance of Proteobacteria but significantly decreased the relative abundance of Acidobacteria, Actinomyces, Chloromyces, and Blastomonas. Under saline irrigation, the relative abundance of Chlorocurvula and Blastomonas were significantly reduced by biochar return to field. Straw returning significantly increased the relative abundance of Proteobacteria but significantly decreased the relative abundance of Acidobacteria, Actinomyces, Chloromyces, and Blastomonas. LEfSe analysis showed that saline irrigation decreased the potential markers and functional numbers of soil microorganisms.Under saline irrigation, biochar returning increased the number of potential markers and functions of soil microorganisms. Straw returning to field increases the number of potential markers of soil microorganisms. RDA results showed that soil microbial community and functional structure were significantly correlated with EC1:5, SWC, and pH. Saline water irrigation will deteriorate the soil environment, which is not conducive to agricultural production, among which EC1:5, SWC, and pH are important factors driving changes in soil microbial community and functional structure. Using biochar and straw to return to the field can reduce the harm of salt to soil and crops, laying a foundation for improving agricultural productivity.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach.

BACKGROUND: Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach. RESULTS: According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3. CONCLUSIONS: In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.

The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4-9 s to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining.

Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.

Washed microbiota transplantation for Crohn's disease: A metagenomic, metatranscriptomic, and metabolomic-based study.

BACKGROUND: Fecal microbiota transplantation (FMT) is a promising therapeutic approach for treating Crohn's disease (CD). The new method of FMT, based on the automatic washing process, was named as washed microbiota transplantation (WMT). Most existing studies have focused on observing the clinical phenomena. However, the mechanism of action of FMT for the effective management of CD-particularly in-depth multi-omics analysis involving the metagenome, metatranscriptome, and metabolome-has not yet been reported. AIM: To assess the efficacy of WMT for CD and explore alterations in the microbiome and metabolome in response to WMT. METHODS: We conducted a prospective, open-label, single-center clinical study. Eleven CD patients underwent WMT. Their clinical responses (defined as a decrease in their CD Activity Index score of > 100 points) and their microbiome (metagenome, metatranscriptome) and metabolome profiles were evaluated three months after the procedure. RESULTS: Seven of the 11 patients (63.6%) showed an optimal clinical response three months post-WMT. Gut microbiome diversity significantly increased after WMT, consistent with improved clinical symptoms. Comparison of the metagenome and metatranscriptome analyses revealed consistent alterations in certain strains, such as Faecalibacterium prausnitzii, Roseburia intestinalis, and Escherichia coli. In addition, metabolomics analyses demonstrated that CD patients had elevated levels of various amino acids before treatment compared to the donors. However, levels of vital amino acids that may be associated with disease progression (e.g., L-glutamic acid, gamma-glutamyl-leucine, and prolyl-glutamine) were reduced after WMT. CONCLUSION: WMT demonstrated therapeutic efficacy in CD treatment, likely due to the effective reconstruction of the patient's microbiome. Multi-omics techniques can effectively help decipher the potential mechanisms of WMT in treating CD.

Computational insights into the binding modes, keto-enol tautomerization and stereo-electronically controlled decarboxylation of oxaloacetate in the active site of macrophomate synthase.

Oxaloacetic acid (OAA) is a ß-ketocarboxylic acid, which plays an important role as an intermediate in some metabolic pathways, including the tricarboxylic acid cycle, gluconeogenesis and fatty acid biosynthesis. Animal studies have indicated that supplementing oxaloacetic acid shows an increase of lifespan and other substantial health benefits including mitochondrial DNA protection, and protection of retinal, neural and pancreatic tissues. Most of the chemical transformations of OAA in the metabolic pathways have been extensively studied; however, the understanding of decarboxylation of OAA at the atomic level is relatively lacking. Here, we carried out MD simulations and combined quantum mechanical/molecular mechanical (QM/MM) calculations as an example to systematically elucidate the binding modes, keto-enol tautomerization and decarboxylation of OAA in the active site of macrophomate synthase (MPS), which is a Mg(II)-dependent bifunctional enzyme that catalyzes both the decarboxylation of OAA and [4+2] cycloaddition of 2-pyrone with the decarboxylated intermediate of OAA (pyruvate enolate). On the basis of our calculations, it was found that the Mg2+-coordinated oxaloacetate may exist in enol forms and keto forms. The four keto forms can be transformed into each other by simply rotating the C2-C3 single bond, nevertheless, the keto-enol tautomerization strictly requires the assistance of pocket water molecules. In addition, the decarboxylation is stereo-electronically controlled, i.e., it is the relative orientation of the terminal carboxyl anion that determines the rate of decarboxylation. As such, the chemistry of oxaloacetate in the active site of MPS is complex. On one hand, the most stable binding mode (K-I) may undergo enol-keto tautomerization to isomerize to the enol form, which may further react with the second substrate; on the other hand, K-I may isomerize to another binding mode K-II to proceed decarboxylation to generate pyruvate enolate and CO2. Starting from K-I, the enol-keto tautomerization corresponds to a barrier of 16.2 kcal mol-1, whereas the decarboxylation is associated with an overall barrier of 19.7 kcal mol-1. These findings may provide useful information for understanding the chemistry of OAA and the catalysis of related enzymes, and they are basically in agreement with the available experimental kinetic data.

[Occurrence Characteristic and Risk Assessment of Microplastics in Sishui River (Xingyang Section)].

Urban rivers are the main receptors and transporters of microplastic pollution. Understanding the occurrence and environmental risk of microplastics in urban rivers can provide theoretical basis for further control of microplastic pollution. The Sishui River, a tributary of the Yellow River, was selected as the research object. A total of nine water samples were collected from sewage outlets of the Sishui River (Xingyang section). The microplastics in the collected samples were characterized by their sizes, shapes, and colors using a microscope. It was found that microplastics were mostly in the form of transparent fibers and fragments in the water body of sewage outlets, of which the size below 500 µm was relatively high. In addition, PET and PE polymers were identified as the main types using a laser infrared imager. The correlation analysis showed that there was a significant correlation between the PET and PE, indicating that they were similar in origin. The results of the environmental risk assessment showed that the type of microplastics was the main factor affecting the assessment results, whereas the risk values of six sewage samples containing PVC were high. However, the value of pollution load index revealed a low risk level of pollutants in the study area.

Identification of circRNA-disease associations via multi-model fusion and ensemble learning.

Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. In this article, we proposed a novel method for circRNA-disease associations prediction based on ensemble learning, named ELCDA. First, the association heterogeneous network was constructed via collecting multiple information of circRNAs and diseases, and multiple similarity measures are adopted here, then, we use metapath, matrix factorization and GraphSAGE-based models to extract features of nodes from different views, the final comprehensive features of circRNAs and diseases via ensemble learning, finally, a soft voting ensemble strategy is used to integrate the predicted results of all classifier. The performance of ELCDA is evaluated by fivefold cross-validation and compare with other state-of-the-art methods, the experimental results show that ELCDA is outperformance than others. Furthermore, three common diseases are used as case studies, which also demonstrate that ELCDA is an effective method for predicting circRNA-disease associations.

Bacillus licheniformis ameliorates Aflatoxin B1-induced testicular damage by improving the gut-metabolism-testis axis.

Global aflatoxin B1 (AFB1) contamination is inevitable, and it can significantly damage testicular development. However, the current mechanism is confusing. Here, by integrating the transcriptome, microbiome, and serum metabolome, we comprehensively explain the impact of AFB1 on testis from the gut-metabolism-testis axis. Transcriptome analysis suggested that AFB1 exposure directly causes abnormalities in testicular inflammation-related signalling, such as tumor necrosis factor (TNF) pathway, and proliferation-related signalling pathways, such as phosphatidylinositide 3-kinases-protein kinase B (PI3K-AKT) pathway, which was verified by immunofluorescence. On the other hand, we found that upregulated inflammatory factors in the intestine after AFB1 exposure were associated with intestinal microbial dysbiosis, especially the enrichment of Bacilli, and enrichment analysis showed that this may be related to NLR family pyrin domain containing 3 (NLRP3)-mediated NOD-like receptor signalling. Also, AFB1 exposure caused blood metabolic disturbances, manifested as decreased hormone levels and increased oxidative stress. Significantly, B. licheniformis has remarkable AFB1 degradation efficiency (> 90%). B. licheniformis treatment is effective in attenuating gut-testis axis damage caused by AFB1 exposure through the above-mentioned signalling pathways. In conclusion, our findings indicate that AFB1 exposure disrupts testicular development through the gut-metabolism-testis axis, and B. licheniformis can effectively degrade AFB1.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features.

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

A viral insulin-like peptide inhibits IGF-1 receptor phosphorylation and regulates IGF1R gene expression.

OBJECTIVE: The insulin/IGF superfamily is conserved across vertebrates and invertebrates. Our team has identified five viruses containing genes encoding viral insulin/IGF-1 like peptides (VILPs) closely resembling human insulin and IGF-1. This study aims to characterize the impact of Mandarin fish ranavirus (MFRV) and Lymphocystis disease virus-Sa (LCDV-Sa) VILPs on the insulin/IGF system for the first time. METHODS: We chemically synthesized single chain (sc, IGF-1 like) and double chain (dc, insulin like) forms of MFRV and LCDV-Sa VILPs. Using cell lines overexpressing either human insulin receptor isoform A (IR-A), isoform B (IR-B) or IGF-1 receptor (IGF1R), and AML12 murine hepatocytes, we characterized receptor binding, insulin/IGF signaling. We further characterized the VILPs' effects of proliferation and IGF1R and IR gene expression, and compared them to native ligands. Additionally, we performed insulin tolerance test in CB57BL/6 J mice to examine in vivo effects of VILPs on blood glucose levels. Finally, we employed cryo-electron microscopy (cryoEM) to analyze the structure of scMFRV-VILP in complex with the IGF1R ectodomain. RESULTS: VILPs can bind to human IR and IGF1R, stimulate receptor autophosphorylation and downstream signaling pathways. Notably, scMFRV-VILP exhibited a particularly strong affinity for IGF1R, with a mere 10-fold decrease compared to human IGF-1. At high concentrations, scMFRV-VILP selectively reduced IGF-1 stimulated IGF1R autophosphorylation and Erk phosphorylation (Ras/MAPK pathway), while leaving Akt phosphorylation (PI3K/Akt pathway) unaffected, indicating a potential biased inhibitory function. Prolonged exposure to MFRV-VILP led to a significant decrease in IGF1R gene expression in IGF1R overexpressing cells and AML12 hepatocytes. Furthermore, insulin tolerance test revealed scMFRV-VILP's sustained glucose-lowering effect compared to insulin and IGF-1. Finally, cryo-EM analysis revealed that scMFRV-VILP engages with IGF1R in a manner closely resembling IGF-1 binding, resulting in a highly analogous structure. CONCLUSIONS: This study introduces MFRV and LCDV-Sa VILPs as novel members of the insulin/IGF superfamily. Particularly, scMFRV-VILP exhibits a biased inhibitory effect on IGF1R signaling at high concentrations, selectively inhibiting IGF-1 stimulated IGF1R autophosphorylation and Erk phosphorylation, without affecting Akt phosphorylation. In addition, MFRV-VILP specifically regulates IGF-1R gene expression and IGF1R protein levels without affecting IR. CryoEM analysis confirms that scMFRV-VILP' binding to IGF1R is mirroring the interaction pattern observed with IGF-1. These findings offer valuable insights into IGF1R action and inhibition, suggesting potential applications in development of IGF1R specific inhibitors and advancing long-lasting insulins.