Tools for assembling the cell: Towards the era of cell structural bioinformatics.

Cells consist of large components, such as organelles, that recursively factor into smaller systems, such as condensates and protein complexes, forming a dynamic multi-scale structure of the cell. Recent technological innovations have paved the way for systematic interrogation of subcellular structures, yielding unprecedented insights into their roles and interactions. In this workshop, we discuss progress, challenges, and collaboration to marshal various computational approaches toward assembling an integrated structural map of the human cell.

Aqueous batteries: from laboratory to market.

This perspective discusses the fundamental benefits and drawbacks of aqueous batteries and the challenges of the development of such battery technology from laboratory scale to industrial applications.

DNA methylation of HOX genes and its clinical implications in cancer.

Homeobox (HOX) genes encode highly conserved transcription factors that play vital roles in embryonic development. DNA methylation is a pivotal regulatory epigenetic signaling mark responsible for regulating gene expression. Abnormal DNA methylation is largely associated with the aberrant expression of HOX genes, which is implicated in a broad range of human diseases, including cancer. Numerous studies have clarified the mechanisms of DNA methylation in both physiological and pathological processes. In this review, we focus on how DNA methylation regulates HOX genes and briefly discuss drug development approaches targeting these mechanisms.

Ricci Curvature-Based Graph Sparsification for Continual Graph Representation Learning.

Memory replay, which stores a subset of historical data from previous tasks to replay while learning new tasks, exhibits state-of-the-art performance for various continual learning applications on the Euclidean data. While topological information plays a critical role in characterizing graph data, existing memory replay-based graph learning techniques only store individual nodes for replay and do not consider their associated edge information. To this end, based on the message-passing mechanism in graph neural networks (GNNs), we present the Ricci curvature-based graph sparsification technique to perform continual graph representation learning. Specifically, we first develop the subgraph episodic memory (SEM) to store the topological information in the form of computation subgraphs. Next, we sparsify the subgraphs such that they only contain the most informative structures (nodes and edges). The informativeness is evaluated with the Ricci curvature, a theoretically justified metric to estimate the contribution of neighbors to represent a target node. In this way, we can reduce the memory consumption of a computation subgraph from O(dL) to O(1) and enable GNNs to fully utilize the most informative topological information for memory replay. Besides, to ensure the applicability on large graphs, we also provide the theoretically justified surrogate for the Ricci curvature in the sparsification process, which can greatly facilitate the computation. Finally, our empirical studies show that SEM outperforms state-of-the-art approaches significantly on four different public datasets. Unlike existing methods, which mainly focus on task incremental learning (task-IL) setting, SEM also succeeds in the challenging class incremental learning (class-IL) setting in which the model is required to distinguish all learned classes without task indicators and even achieves comparable performance to joint training, which is the performance upper bound for continual learning.

Tailored chitosan-based entrapped catalyst for dyes removal by highly active, stable, and recyclable nanoparticles toughened hydrogel.

Functional catalytic hydrogels were a promising catalyst carrier with the advantages of low cost, high efficiency and environmental friendliness. However, conventional hydrogels suffered from mechanical defects and brittleness. Acrylamide (AM) and lauryl methacrylate (LMA) were used as raw materials, SiO2-NH2 spheres as toughening agents, and chitosan (CS) as stabilizers to form hydrophobic binding networks. p(AM/LMA)/SiO2-NH2/CS hydrogels exhibited superior stretchability and withstood strains up to 14,000 %. In addition, these hydrogels exhibited exceptional mechanical properties, including a tensile strength of 213 kPa and a toughness of 13.1 MJ/m3. Surprisingly, the introduction of chitosan into hydrogels showed excellent antibacterial activity against S. aureus and E. coli. At the same time, the hydrogel served as a template for the formation of Au nanoparticles. This resulted in high catalytic activity for methylene blue (MB) and Congo red (CR) on p(AM/LMA)/SiO2-NH2/CS-8 %-Au hydrogels with Kapp of 1.038 and 0.76 min-1, respectively. The catalyst was also found to be reusable for 10 cycles while maintaining an efficiency of over 90 %. Therefore, innovative design strategies can be used to develop durable and scalable hydrogel materials for catalysis in the wastewater treatment industry.

LINC00294/miR-620/MKRN2 axis provides biomarkers and negatively regulates malignant progression in colorectal carcinoma.

BACKGROUND: Colorectal carcinoma (CRC) ranks the third most frequent malignancy worldwide. Makorin RING zinc finger-2 (MKRN2) has been identified as a tumor suppressor in CRC, and the bioinformatics prediction indicated that some non-coding RNAs (ncRNAs) that directly or indirectly regulate MKRN2 might play critical roles in CRC progression. This study aimed to analyze the regulatory effect of LINC00294 on CRC progression, and to explore the underlying mechanisms by assessing miR-620 and MKRN2. The potential prognostic value of the ncRNAs and MKRN2 was also investigated. METHODS: The expression of LINC00294, MKRN2, miR-620 was examined by qRT-PCR. Cell counting kit-8 assay was used to assess the proliferation of CRC cells. Transwell assay was used to evaluate the migration, invasion of CRC cells. Kaplan-Meier method and log-rank test were used to perform comparative analysis of overall survival in CRC patients. RESULTS: Lower expression of LINC00294 was observed in both CRC tissues and cell lines. In CRC cells, LINC00294 overexpression inhibited cell proliferation, migration and invasion, but these effects were directly reversed by the overexpression of miR-620, which was demonstrated as a target of LINC00294. Additionally, MKRN2 was found to be a target gene of miR-620, and might mediate the regulatory function of LINC00294 in CRC progression. In CRC patients, low LINC00294, MKRN2 and high miR-620 expression was associated poor overall survival of CRC. CONCLUSIONS: LINC00294/miR-620/MKRN2 axis had the potential to provide prognostic biomarkers for CRC patients, and negatively regulated the malignant progression of CRC cells, including proliferation, migration and invasion.

A combination of anti-PD-1 therapy and apatinib successfully treated a patient with EGFR mutation-negative advanced lung adenocarcinoma: A case report.

Here, we report the case of a patient with advanced lung adenocarcinoma with negative driver genes, who benefited from treatment with anti-programmed cell death-1 (anti-PD-1) therapy combined with a low dose of apatinib. From February 2020, the patient was treated with camrelizumab combined with pemetrexed disodium. The treatment regimen was adjusted to camrelizumab combined with a low dose of apatinib every 3 weeks because the patient could not tolerate the side effects of the previous chemotherapy, and camrelizumab led to reactive cutaneous capillary endothelial proliferation (RCCEP). After six cycles of camrelizumab plus a low dose of apatinib, the curative effect achieved was complete response (CR), with milder symptoms of RCCEP than before. Until the follow-up time of March 2021, the efficacy evaluation reached CR and the symptoms of RCCEP disappeared. This case report provides a theoretical basis for camrelizumab combined with a low dose of apatinib for the treatment ofcarcinoma patients with advanced lung adenocarcinoma with negative driver genes.

Hierarchical Prototype Networks for Continual Graph Representation Learning.

Despite significant advances in graph representation learning, little attention has been paid to the more practical continual learning scenario in which new categories of nodes (e.g., new research areas in citation networks, or new types of products in co-purchasing networks) and their associated edges are continuously emerging, causing catastrophic forgetting on previous categories. Existing methods either ignore the rich topological information or sacrifice plasticity for stability. To this end, we present Hierarchical Prototype Networks (HPNs) which extract different levels of abstract knowledge in the form of prototypes to represent the continuously expanded graphs. Specifically, we first leverage a set of Atomic Feature Extractors (AFEs) to encode both the elemental attribute information and the topological structure of the target node. Next, we develop HPNs to adaptively select relevant AFEs and represent each node with three levels of prototypes. In this way, whenever a new category of nodes is given, only the relevant AFEs and prototypes at each level will be activated and refined, while others remain uninterrupted to maintain the performance over existing nodes. Theoretically, we first demonstrate that the memory consumption of HPNs is bounded regardless of how many tasks are encountered. Then, we prove that under mild constraints, learning new tasks will not alter the prototypes matched to previous data, thereby eliminating the forgetting problem. The theoretical results are supported by experiments on five datasets, showing that HPNs not only outperform state-of-the-art baseline techniques but also consume relatively less memory. Code and datasets are available at https://github.com/QueuQ/HPNs.

Ammonium Ion Batteries: Material, Electrochemistry and Strategy.

Ammonium-ion batteries (AIBs) have recently attracted increasing attention in the field of aqueous batteries owing to their high safety and fast diffusion kinetics. The NH4 + storage mechanism is quite different from that of spherical metal ions (e.g. Li+ , Na+ , K+ , Mg2+ , and Zn2+ ) because of the formation of hydrogen bonds between NH4 + and host materials. Although many materials have been proposed as electrode materials for AIBs, their performances hardly meet the requirement of future electrochemical energy storage devices. It is thus urgent to design and exploit advanced materials for AIBs. This review highlights the state-of-the-art research on AIBs. The insights into the basic configuration, operating mechanism and recent progress of electrode materials and corresponding electrolytes for AIBs have been comprehensively outlined. The electrode materials are classified and compared according to different NH4 + storage behaviour in the structure. The challenges, design strategies and perspectives are also discussed for the future development of AIBs.

Dual synergistic effects assisting Cu-SeS2 electrochemistry for energy storage.

Selenium sulfide (SeS2) features higher electronic conductivity than sulfur and higher theoretical capacity and lower cost than selenium, attracting considerable interest in energy storage field. Although nonaqueous Li/Na/K-SeS2 batteries are attractive for their high energy density, the notorious shuttle effect of polysulfides/polyselenides and the intrinsic limitations of organic electrolyte have hindered the deployment of this technology. To circumvent these issues, here we design an aqueous Cu-SeS2 battery by encapsulating SeS2 in a defect-enriched nitrogen-doped porous carbon monolith. Except the intrinsic synergistic effect between Se and S in SeS2, the porous structure of carbon matrix has sufficient internal voids to buffer the volume change of SeS2 and provides abundant pathways for both electrons and ions. In addition, the synergistic effect of nitrogen doping and topological defect not only enhances the chemical affinity between reactants and carbon matrix but also offers catalytic active sites for electrochemical reactions. Benefiting from these merits, the Cu-SeS2 battery delivers superior initial reversible capacity of 1,905.1 mAh g-1 at 0.2 A g-1 and outstanding long-span cycling performance over 1,000 cycles at 5 A g-1. This work applies variable valence charge carriers to aqueous metal-SeS2 batteries, providing valuable inspiration for the construction of metal-chalcogen batteries.

Gesture Recognition by Ensemble Extreme Learning Machine Based on Surface Electromyography Signals.

In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application scenarios. To enhance this situation, this paper proposed a method combining feature selection and ensemble extreme learning machine (EELM) to improve the recognition performance based on sEMG signals. First, the input sEMG signals are preprocessed and 16 features are then extracted from each channel. Next, features that mostly contribute to the gesture recognition are selected from the extracted features using the recursive feature elimination (RFE) algorithm. Then, several independent ELM base classifiers are established using the selected features. Finally, the recognition results are determined by integrating the results obtained by ELM base classifiers using the majority voting method. The Ninapro DB5 dataset containing 52 different hand movements captured from 10 able-bodied subjects was used to evaluate the performance of the proposed method. The results showed that the proposed method could perform the best (overall average accuracy 77.9%) compared with decision tree (DT), ELM, and random forest (RF) methods.

TSF-transformer: a time series forecasting model for exhaust gas emission using transformer.

Monitoring and prediction of exhaust gas emissions for heavy trucks is a promising way to solve environmental problems. However, the emission data acquisition is time delayed and the pattern of emission is usually irregular, which makes it very difficult to accurately predict the emission state. To deal with these problems, in this paper, we interpret emission prediction as a time series prediction problem and explore a deep learning model, a time-series forecasting Transformer (TSF-Transformer) for exhaust gas emission prediction. The exhaust emission of the heavy truck is not directly predicted, but indirectly predicted by predicting the temperature and pressure changes of the exhaust pipe under the working state of the truck. The basis of our research is based on real-time data feeds from temperature and pressure sensors installed on the exhaust pipe of approximately 12,000 heavy trucks. Therefore, the task of time series forecasting consists of two key stages: monitoring and prediction. The former utilizes the server to receive the data sent by the sensors in real-time, and the latter uses these data as samples for network training and testing. The training of the network throughout the prediction process is done in an unsupervised manner. Also, to visualize the forecast results, we weight the forecast data with the truck trajectories and present them as heatmaps. To the best of our knowledge, this is the first case of using the Transformer as the core component of the prediction model to complete the task of exhaust emissions prediction from heavy trucks. Experiments show that the prediction model outperforms other state-of-the-art methods in prediction accuracy.

[Research progress of epileptic seizure predictions based on electroencephalogram signals].

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.

Prussian Blue Analogues in Aqueous Batteries and Desalination Batteries.

In the applications of large-scale energy storage, aqueous batteries are considered as rivals for organic batteries due to their environmentally friendly and low-cost nature. However, carrier ions always exhibit huge hydrated radius in aqueous electrolyte, which brings difficulty to find suitable host materials that can achieve highly reversible insertion and extraction of cations. Owing to open three-dimensional rigid framework and facile synthesis, Prussian blue analogues (PBAs) receive the most extensive attention among various host candidates in aqueous system. Herein, a comprehensive review on recent progresses of PBAs in aqueous batteries is presented. Based on the application in different aqueous systems, the relationship between electrochemical behaviors (redox potential, capacity, cycling stability and rate performance) and structural characteristics (preparation method, structure type, particle size, morphology, crystallinity, defect, metal atom in high-spin state and chemical composition) is analyzed and summarized thoroughly. It can be concluded that the required type of PBAs is different for various carrier ions. In particular, the desalination batteries worked with the same mechanism as aqueous batteries are also discussed in detail to introduce the application of PBAs in aqueous systems comprehensively. This report can help the readers to understand the relationship between physical/chemical characteristics and electrochemical properties for PBAs and find a way to fabricate high-performance PBAs in aqueous batteries and desalination batteries.

Hydrogen Bond-Assisted Ultra-Stable and Fast Aqueous NH4+ Storage.

Aqueous ammonium ion batteries are regarded as eco-friendly and sustainable energy storage systems. And applicable host for NH4+ in aqueous solution is always in the process of development. On the basis of density functional theory calculations, the excellent performance of NH4+ insertion in Prussian blue analogues (PBAs) is proposed, especially for copper hexacyanoferrate (CuHCF). In this work, we prove the outstanding cycling and rate performance of CuHCF via electrochemical analyses, delivering no capacity fading during ultra-long cycles of 3000 times and high capacity retention of 93.6% at 50 C. One of main contributions to superior performance from highly reversible redox reaction and structural change is verified during the ammoniation/de-ammoniation progresses. More importantly, we propose the NH4+ diffusion mechanism in CuHCF based on continuous formation and fracture of hydrogen bonds from a joint theoretical and experimental study, which is another essential reason for rapid charge transfer and superior NH4+ storage. Lastly, a full cell by coupling CuHCF cathode and polyaniline anode is constructed to explore the practical application of CuHCF. In brief, the outstanding aqueous NH4+ storage in cubic PBAs creates a blueprint for fast and sustainable energy storage.

A TiSe2 -Graphite Dual Ion Battery: Fast Na-Ion Insertion and Excellent Stability.

The sodium dual ion battery (Na-DIB) technology is proposed as highly promising alternative over lithium-ion batteries for the stationary electrochemical energy-storage devices. However, the sluggish reaction kinetics of anode materials seriously impedes their practical implementation. Herein, a Na-DIB based on TiSe2 -graphite is reported. The high diffusion coefficient of Na-ions (3.21×10-11 -1.20×10-9  cm2 s-1 ) and the very low Na-ion diffusion barrier (0.50 eV) lead to very fast electrode kinetics, alike in conventional surface capacitive storage systems. In-situ investigations reveal that the fast Na-ion diffusion involves four insertion stage compositions. A prototype cell shows a reversible capacity of 81.8 mAh g-1 at current density of 100 mA g-1 , excellent stability with 83.52 % capacity retention over 200 cycles and excellent rate performance, suggesting its potential for next-generation large scale high-performance stationary energy storage systems.

Common ion effect enhanced Prussian blue analogues for aqueous ammonium ion storage.

In order to meet the growing demand of energy storage for the power grid, aqueous NH4+ batteries are attracting increasing attention as a promising alternative due to their environmental significance, abundant resources, and fast diffusion ability. In this work, FeFe(CN)6 (FeHCF) is synthesized as a cathode material for aqueous NH4+ batteries and Fe2(SO4)3 is utilized as a kind of functional additive in the electrolyte based on the "common ion effect" to enhance its electrochemical performance. The results indicate that the initial capacity of FeHCF is about 80 mA h g-1 with a coulombic efficiency of 97.8%. The retention rate can attain 96.3% within nearly 1000 cycles. Multivariate analysis methods are carried out to characterize the mechanism of FeHCF in aqueous NH4+ batteries. From the practical standpoint, FeHCF has outstanding cycling stability and rate capability, making it feasible to be applied in the power grid.

Graph Edge Convolutional Neural Networks for Skeleton-Based Action Recognition.

Body joints, directly obtained from a pose estimation model, have proven effective for action recognition. Existing works focus on analyzing the dynamics of human joints. However, except joints, humans also explore motions of limbs for understanding actions. Given this observation, we investigate the dynamics of human limbs for skeleton-based action recognition. Specifically, we represent an edge in a graph of a human skeleton by integrating its spatial neighboring edges (for encoding the cooperation between different limbs) and its temporal neighboring edges (for achieving the consistency of movements in an action). Based on this new edge representation, we devise a graph edge convolutional neural network (CNN). Considering the complementarity between graph node convolution and edge convolution, we further construct two hybrid networks by introducing different shared intermediate layers to integrate graph node and edge CNNs. Our contributions are twofold, graph edge convolution and hybrid networks for integrating the proposed edge convolution and the conventional node convolution. Experimental results on the Kinetics and NTU-RGB+D data sets demonstrate that our graph edge convolution is effective at capturing the characteristics of actions and that our graph edge CNN significantly outperforms the existing state-of-the-art skeleton-based action recognition methods.