Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.

GloEC: a hierarchical-aware global model for predicting enzyme function.

The annotation of enzyme function is a fundamental challenge in industrial biotechnology and pathologies. Numerous computational methods have been proposed to predict enzyme function by annotating enzyme labels with Enzyme Commission number. However, the existing methods face difficulties in modelling the hierarchical structure of enzyme label in a global view. Moreover, they haven't gone entirely to leverage the mutual interactions between different levels of enzyme label. In this paper, we formulate the hierarchy of enzyme label as a directed enzyme graph and propose a hierarchy-GCN (Graph Convolutional Network) encoder to globally model enzyme label dependency on the enzyme graph. Based on the enzyme hierarchy encoder, we develop an end-to-end hierarchical-aware global model named GloEC to predict enzyme function. GloEC learns hierarchical-aware enzyme label embeddings via the hierarchy-GCN encoder and conducts deductive fusion of label-aware enzyme features to predict enzyme labels. Meanwhile, our hierarchy-GCN encoder is designed to bidirectionally compute to investigate the enzyme label correlation information in both bottom-up and top-down manners, which has not been explored in enzyme function prediction. Comparative experiments on three benchmark datasets show that GloEC achieves better predictive performance as compared to the existing methods. The case studies also demonstrate that GloEC is capable of effectively predicting the function of isoenzyme. GloEC is available at: https://github.com/hyr0771/GloEC.

PQSDC: a parallel lossless compressor for quality scores data via sequences partition and run-length prediction mapping.

MOTIVATION: The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters. RESULTS: We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely. AVAILABILITY AND IMPLEMENTATION: The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC.

High Spatial Resolution 2D Imaging of Current Density and Pressure for Graphene Devices under High Pressure Using Nitrogen-Vacancy Centers in Diamond.

Current density imaging is helpful for discovering interesting electronic phenomena and understanding carrier dynamics, and by combining pressure distributions, several pressure-induced novel physics may be comprehended. In this work, noninvasive, high-resolution two-dimensional images of the current density and pressure gradient for graphene ribbon and hBN-graphene-hBN devices are explored using nitrogen-vacancy (NV) centers in diamond under high pressure. The two-dimensional vector current density is reconstructed by the vector magnetic field mapped by the near-surface NV center layer in the diamond. The current density images accurately and clearly reproduce the complicated structure and current flow of graphene under high pressure. Additionally, the spatial distribution of the pressure is simultaneously mapped, rationalizing the nonuniformity of the current density under high pressure. The current method opens a significant new avenue to investigate electronic transport and conductance variations in two-dimensional materials and electrical devices under high pressure as well as for nondestructive evaluation of semiconductor circuits.

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.

BACKGROUND: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes. RESULTS: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data. CONCLUSIONS: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Hydrodynamics-Controlled Single-Particle Electrocatalysis.

Electrocatalysis is considered promising in renewable energy conversion and storage, yet numerous efforts rely on catalyst design to advance catalytic activity. Herein, a hydrodynamic single-particle electrocatalysis methodology is developed by integrating collision electrochemistry and microfluidics to improve the activity of an electrocatalysis system. As a proof-of-concept, hydrogen evolution reaction (HER) is electrocatalyzed by individual palladium nanoparticles (Pd NPs), with the development of microchannel-based ultramicroelectrodes. The controlled laminar flow enables the precise delivery of Pd NPs to the electrode-electrolyte interface one by one. Compared to the diffusion condition, hydrodynamic collision improves the number of active sites on a given electrode by 2 orders of magnitude. Furthermore, forced convection enables the enhancement of proton mass transport, thereby increasing the electrocatalytic activity of each single Pd NP. It turns out that the improvement in mass transport increases the reaction rate of HER at individual Pd NPs, thus a phase transition without requiring a high overpotential. This study provides new avenues for enhancing electrocatalytic activity by altering operating conditions, beyond material design limitations.

Singlet Fission in Pechmann Dyes: Planar Chromophore Design and Understanding.

Singlet fission in organic chromophores holds the potential for enhancing photovoltaic efficiencies beyond the single-junction limit. The most basic requirement of a singlet fission material is that it has a large energy gap between its first singlet and triplet excited states. Identifying such compounds is not simple and has been accomplished either through computational screening or by subtle modifications of previously known fission materials. Here, we propose an approach that leverages ground and excited-state aromaticity combined with double-bond conformation to establish simple qualitative design rules for predicting fundamental optical properties without the need for computational modeling. By investigating two Pechmann dye isomers, we demonstrate that although their planarity and degree of charge transfer are similar, singlet fission is active in the isomer with a trans-conformation, while the cis-isomer exhibits greater favorability for polaronic processes, experimentally validated using ultrafast and electron spin resonance spectroscopy. Our results offer a new design perspective that provides a rational framework for tailoring optoelectronic systems to specific applications such as singlet fission or triplet-triplet annihilation.

Phenotyping of FGF12AV52H mutation in mouse implies a complex FGF12 network.

Pathogenic missense mutation of the FGF12 gene is responsible for a variable disease phenotypic spectrum. Disease-specific therapies require precise dissection of the relationship between different mutations and phenotypes. The lack of a proper animal model hinders the investigation of related diseases, such as early-onset epileptic encephalopathy. Here, an FGF12AV52H mouse model was generated using CRISPR/Cas9 technology, which altered the A isoform without affecting the B isoform. The FGF12AV52H mice exhibited seizure susceptibility, while no spontaneous seizures were observed. The increased excitability in dorsal hippocampal CA3 neurons was confirmed by patch-clamp recordings. Furthermore, immunostaining showed that the balance of excitatory/inhibitory neurons in the hippocampus of the FGF12AV52H mice was perturbed. The increases in inhibitory SOM+ neurons and excitatory CaMKII+ neurons were heterogeneous. Moreover, the locomotion, anxiety levels, risk assessment behavior, social behavior, and cognition of the FGF12AV52H mice were investigated by elevated plus maze, open field, three-chamber sociability, and novel object tests, respectively. Cognition deficit, impaired risk assessment, and social behavior with normal social indexes were observed, implying complex consequences of V52H FGF12A in mice. Together, these data suggest that the function of FGF12A in neurons can be immediate or long-term and involves modulation of ion channels and the differentiation and maturation of neurons. The FGF12AV52H mouse model increases the understanding of the function of FGF12A, and it is of great importance for revealing the complex network of the FGF12 gene in physiological and pathological processes.

Mechanism Study of Molecular Trap in All-Organic Polystyrene-Based Dielectric Composite.

It is a huge challenge to explore how charge traps affect the electric breakdown of polymer-based dielectric composites. In this paper, alkane and aromatic molecules with different substituents are investigated according to DFT theoretical method. The combination of strong electron-withdrawing groups and aromatic rings can establish high electron affinity molecules. 4'-Nitro-4-dimethylaminoazobenzene (NAABZ) with a vertical electron affinity of 1.39 eV and a dipole moment of 10.15 D is introduced into polystyrene (PSt) to analyze the influence of charge traps on electric properties. Marcus charge transfer theory is applied to calculate the charge transfer rate between PSt and NAABZ. The nature of charge traps is elaborated from a dynamic perspective. The enhanced breakdown mechanism of polymers-based composites stems from the constraint of carrier mobility caused by the change in transfer rate. But the electrophile nature of high electron affinity filler can decrease the potential barriers at the metal-polymer interface. Simultaneously, the relationship between the electron affinity of fillers and the breakdown strength of polymer-based composites is nonlinear because of the presence of the inversion region. Based on the deep understanding of the molecular trap, this work provides the theoretical calculation for the design and development of high-performance polymer dielectrics.

Dopant-Free Hole Transport Material Based on Non-Covalent Interaction for Efficient Perovskite Solar Cells.

Hole transport materials (HTMs) have a critical impact on the performance of perovskite solar cells (PSCs). Especially, the dopant-free HTMs could avoid the usage of hygroscopic dopants and reduce costs, which are important for device stability. Most of the current organic dopant-free HTMs are polycyclic aromatic hydrocarbons-based planar conjugated structures. Yet, the synthesis of conjugated fused heterocycles is often complicated. In this work, intramolecular non-covalent interaction is introduced to construct two organic HTMs (DCT and DTC), which can be facilely obtained through simple reactions. Compared to DTC with hexyl chain on the central benzene ring, DCT with hexyloxy chains shows better planarity in the core structure, as a result of the intramolecular non-covalent interactions between oxygen on hexyloxy and sulfur atom on the adjacent thiophene, as reflected from its single crystal structure. Moreover, DCT in a pristine state shows a decent hole mobility comparable to the doped Spiro-OMeTAD. Ultimately, conventional devices using dopant-free DCT as HTM show a high efficiency of 22.50%, with excellent long-term stability, and light and thermal stability. The results show that the noncovalent interaction is a useful and simple design strategy for dopant-free HTMs, that can effectively improve the efficiency and stability of PSCs.

Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics.

MOTIVATION: The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. RESULTS: We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChengZ352/SPAN.

DxFormer: a decoupled automatic diagnostic system based on decoder-encoder transformer with dense symptom representations.

MOTIVATION: Symptom-based automatic diagnostic system queries the patient's potential symptoms through continuous interaction with the patient and makes predictions about possible diseases. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods focus on disease diagnosis while ignoring the importance of symptom inquiry. Although these systems have achieved considerable diagnostic accuracy, they are still far below its performance upper bound due to few turns of interaction with patients and insufficient performance of symptom inquiry. To address this problem, we propose a new automatic diagnostic framework called DxFormer, which decouples symptom inquiry and disease diagnosis, so that these two modules can be independently optimized. The transition from symptom inquiry to disease diagnosis is parametrically determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model, respectively. We use the inverted version of Transformer, i.e. the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross-entropy loss. RESULTS: We conduct experiments on three real-world medical dialogue datasets, and the experimental results verify the feasibility of increasing diagnostic accuracy by improving symptom recall. Our model overcomes the shortcomings of previous RL-based methods. By decoupling symptom query from the process of diagnosis, DxFormer greatly improves the symptom recall and achieves the state-of-the-art diagnostic accuracy. AVAILABILITY AND IMPLEMENTATION: Both code and data are available at https://github.com/lemuria-wchen/DxFormer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

MOTIVATION: In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. RESULTS: We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies. AVAILABILITY AND IMPLEMENTATION: Both code and data are available from https://github.com/lemuria-wchen/imcs21. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Experimental study of modified Tavis-Cummings model with directly-coupled superconducting artificial atoms.

The Tavis-Cummings model is intensively investigated in quantum optics and has important applications in generation of multi-atom entanglement. Here, we employ a superconducting circuit quantum electrodynamic system to study a modified Tavis-Cummings model with directly-coupled atoms. In our device, three superconducting artificial atoms are arranged in a chain with direct coupling through fixed capacitors and strongly coupled to a transmission line resonator. By performing transmission spectrum measurements, we observe different anticrossing structures when one or two qubits are resonantly coupled to the resonator. In the case of the two-qubit Tavis-Cummings model without qubit-qubit interaction, we observe two dips at the resonance point of the anticrossing. The splitting of these dips is determined by Δ λ=2g12+g32, where g1 and g3 are the coupling strengths between Qubit 1 and the resonator, and Qubit 3 and the resonator, respectively. The direct coupling J12 between the two qubits results in three dressed states in the two-qubit Tavis-Cummings model at the frequency resonance point, leading to three dips in the transmission spectrum. In this case, the distance between the two farthest and asymmetrical dips, arising from the energy level splitting, is larger than in the previous case. The frequency interval between these two dips is determined by the difference in eigenvalues (Δ λ=ε 1+-ε 1-), obtained through numerical calculations. What we believe as novel and intriguing experimental results may potentially advance quantum optics experiments, providing valuable insights for future research.

High-Order Topological Pumping on a Superconducting Quantum Processor.

High-order topological phases of matter refer to the systems of n-dimensional bulk with the topology of m-th order, exhibiting (n-m)-dimensional boundary modes and can be characterized by topological pumping. Here, we experimentally demonstrate two types of second-order topological pumps, forming four 0-dimensional corner localized states on a 4×4 square lattice array of 16 superconducting qubits. The initial ground state of the system at half-filling, as a product of four identical entangled 4-qubit states, is prepared using an adiabatic scheme. During the pumping procedure, we adiabatically modulate the superlattice Bose-Hubbard Hamiltonian by precisely controlling both the hopping strengths and on-site potentials. At the half pumping period, the system evolves to a corner-localized state in a quadrupole configuration. The robustness of the second-order topological pump is also investigated by introducing different on-site disorder. Our Letter studies the topological properties of high-order topological phases from the dynamical transport picture using superconducting qubits, which would inspire further research on high-order topological phases.

Hsa_circ_0005397 promotes hepatocellular carcinoma progression through EIF4A3.

PURPOSE: The purpose of this study was to explore the expression and potential mechanism of hsa_circ_0005397 in hepatocellular carcinoma progression. METHODS: Quantitative reverse transcription-polymerase chain reaction(qRT-PCR) was used to measure the expression level of hsa_circ_0005397 and EIF4A3 from paired HCC tissues and cell lines. Western Blot (WB) and immunohistochemistry (IHC) were used to verify the protein level of EIF4A3. The specificity of primers was confirmed by agarose gel electrophoresis. Receiver Operating Characteristic (ROC) Curve was drawn to analyze diagnostic value. Actinomycin D and nuclear and cytoplasmic extraction assays were utilized to evaluate the characteristics of hsa_circ_0005397. Cell Counting kit-8 (CCK-8) and colony formation assays were performed to detect cell proliferation. Flow cytometry analysis was used to detect the cell cycle. Transwell assay was performed to determine migration and invasion ability. RNA-binding proteins (RBPs) of hsa_circ_0005397 in HCC were explored using bioinformatics websites. The relationship between hsa_circ_0005397 and Eukaryotic Translation Initiation Factor 4A3 (EIF4A3) was verified by RNA Binding Protein Immunoprecipitation (RIP) assays, correlation and rescue experiments. RESULTS: In this study, hsa_circ_0005397 was found to be significantly upregulated in HCC, and the good diagnostic sensitivity and specificity shown a potential diagnostic capability. Upregulated expression of hsa_circ_0005397 was significantly related to tumor size and stage. Hsa_circ_0005397 was circular structure which more stable than liner mRNA, and mostly distributed in the cytoplasm. Upregulation of hsa_circ_0005397 generally resulted in stronger proliferative ability, clonality, and metastatic potency of HCC cells; its downregulation yielded the opposite results. EIF4A3 is an RNA-binding protein of hsa_circ_0005397, which overexpressed in paired HCC tissues and cell lines. In addition, expression of hsa_circ_0005397 decreased equally when EIF4A3 was depleted. RIP assays and correlation assay estimated that EIF4A3 could interacted with hsa_circ_0005397. Knockdown of EIF4A3 could reverse hsa_circ_0005397 function in HCC progression. CONCLUSIONS: Hsa_circ_0005397 promotes progression of hepatocellular carcinoma through EIF4A3. These research findings may provide novel clinical value for hepatocellular carcinoma.

Fetal overgrowth and weight trajectories during infancy and adiposity in early childhood.

BACKGROUND: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood. Little is known about how infancy growth trajectories affect adiposity in early childhood in LGA. METHODS: In the Shanghai Birth Cohort, we followed up 259 LGA (birth weight >90th percentile) and 1673 appropriate-for-gestational age (AGA, 10th-90th percentiles) children on body composition (by InBody 770) at age 4 years. Adiposity outcomes include body fat mass (BFM), percent body fat (PBF), body mass index (BMI), overweight/obesity, and high adiposity (PBF >85th percentile). RESULTS: Three weight growth trajectories (low, mid, and high) during infancy (0-2 years) were identified in AGA and LGA subjects separately. BFM, PBF and BMI were progressively higher from low- to mid-to high-growth trajectories in both AGA and LGA children. Compared to the mid-growth trajectory, the high-growth trajectory was associated with greater increases in BFM and the odds of overweight/obesity or high adiposity in LGA than in AGA children (tests for interactions, all P < 0.05). CONCLUSIONS: Weight trajectories during infancy affect adiposity in early childhood regardless of LGA or not. The study is the first to demonstrate that high-growth weight trajectory during infancy has a greater impact on adiposity in early childhood in LGA than in AGA subjects. IMPACT: Large-for-gestational age (LGA), a marker of fetal overgrowth, has been linked to obesity in adulthood, but little is known about how weight trajectories during infancy affect adiposity during early childhood in LGA subjects. The study is the first to demonstrate a greater impact of high-growth weight trajectory during infancy (0-2 years) on adiposity in early childhood (at age 4 years) in subjects with fetal overgrowth (LGA) than in those with normal birth size (appropriate-for-gestational age). Weight trajectory monitoring may be a valuable tool in identifying high-risk LGA children for close follow-ups and interventions to decrease the risk of obesity.

Exploring the novel benefits of leucine: Protecting nitrite-induced liver damage in sub-adult grass carp (Ctenopharyngodon idella) through regulating mitochondria quality control.

Leucine is an essential amino acid for fish. The ability of leucine to resist stress in fish has not been reported. Nitrite is a common pollutant in the aquatic environment. Therefore, we investigated the effects of dietary leucine on growth performance and nitrite-induced liver damage, mitochondrial dysfunction, autophagy, and apoptosis for sub-adult grass carp. A total of 450 grass carp (615.91 ± 1.15 g) were selected and randomly placed into 18 net cages. The leucine contents of the six diets were 2.91, 5.90, 8.92, 11.91, 14.93, and 17.92 g/kg, respectively. After a 9-week feeding trial, the nitrite exposure experiment was set up for 96 h. These results indicated that dietary leucine significantly promoted FW, WG, PWG, and SGR of sub-adult grass carp (P < 0.05). Appropriate levels of dietary leucine (11.91-17.92 g/kg) decreased the activities of serum parameters (glucose, cortisol, and methemoglobin contents, glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, and lactate dehydrogenase), the contents of reactive oxygen species (ROS), nitric oxide (NO) and peroxynitrite (ONOO-). In addition, appropriate levels of dietary leucine (11.91-17.92 g/kg) increased the mRNA levels of mitochondrial biogenesis genes (PGC-1α, Nrf1/2, TFAM), fusion-related genes (Opa1, Mfn1/2) (P < 0.05), and decreased the mRNA levels of caspase 3, caspase 8, caspase 9, fission-related gene (Drp1), mitophagy-related genes (Pink1, Parkin) and autophagy-related genes (Beclin1, Ulk1, Atg5, Atg7, Atg12) (P < 0.05). Appropriate levels of dietary leucine (8.92-17.92 g/kg) also increased the protein levels of AMP-activated protein kinase (AMPK), prostacyclin (p62) and decreased the protein levels of protein light chain 3 (LC3), E3 ubiquitin ligase (Parkin), and Cytochrome c (Cytc). Appropriate levels of leucine (8.92-17.92 g/kg) could promote growth performance and alleviate nitrite-induced mitochondrial dysfunction, autophagy, apoptosis for sub-adult grass carp. Based on quadratic regression analysis of PWG and serum GPT activity, dietary leucine requirements of sub-adult grass carp were recommended to be 12.47 g/kg diet and 12.55 g/kg diet, respectively.

Opportunities of Flexible and Portable Electrochemical Devices for Energy Storage: Expanding the Spotlight onto Semi-solid/Solid Electrolytes.

The ever-increasing demand for flexible and portable electronics has stimulated research and development in building advanced electrochemical energy devices which are lightweight, ultrathin, small in size, bendable, foldable, knittable, wearable, and/or stretchable. In such flexible and portable devices, semi-solid/solid electrolytes besides anodes and cathodes are the necessary components determining the energy/power performances. By serving as the ion transport channels, such semi-solid/solid electrolytes may be beneficial to resolving the issues of leakage, electrode corrosion, and metal electrode dendrite growth. In this paper, the fundamentals of semi-solid/solid electrolytes (e.g., chemical composition, ionic conductivity, electrochemical window, mechanical strength, thermal stability, and other attractive features), the electrode-electrolyte interfacial properties, and their relationships with the performance of various energy devices (e.g., supercapacitors, secondary ion batteries, metal-sulfur batteries, and metal-air batteries) are comprehensively reviewed in terms of materials synthesis and/or characterization, functional mechanisms, and device assembling for performance validation. The most recent advancements in improving the performance of electrochemical energy devices are summarized with focuses on analyzing the existing technical challenges (e.g., solid electrolyte interphase formation, metal electrode dendrite growth, polysulfide shuttle issue, electrolyte instability in half-open battery structure) and the strategies for overcoming these challenges through modification of semi-solid/solid electrolyte materials. Several possible directions for future research and development are proposed for going beyond existing technological bottlenecks and achieving desirable flexible and portable electrochemical energy devices to fulfill their practical applications. It is expected that this review may provide the readers with a comprehensive cross-technology understanding of the semi-solid/solid electrolytes for facilitating their current and future researches on the flexible and portable electrochemical energy devices.