Triglyceride-glucose index as a potential predictor of major adverse cardiovascular and cerebrovascular events in patients with coronary heart disease complicated with depression.

Background: Triglyceride-glucose (TyG) index is a surrogate marker of insulin resistance and metabolic abnormalities, which is closely related to the prognosis of a variety of diseases. Patients with both CHD and depression have a higher risk of major adverse cardiovascular and cerebrovascular events (MACCE) and worse outcome. TyG index may be able to predict the adverse prognosis of this special population. Methods: The retrospective cohort study involved 596 patients with both CHD and depression between June 2013 and December 2023. The primary outcome endpoint was the occurrence of MACCE, including all-cause death, stroke, MI and emergent coronary revascularization. The receiver operating characteristic (ROC) curve, Cox regression analysis, Kaplan-Meier survival analysis, and restricted cubic spline (RCS) analysis were used to assess the correlation between TyG index and MACCE risk of in patients with CHD complicated with depression. Results: With a median follow-up of 31 (15-62) months, MACCE occurred in 281(47.15%) patients. The area under the ROC curve of TyG index predicting the risk of MACCE was 0.765(0.726-0.804) (P<0.01). Patients in the high TyG index group(69.73%) had a significantly higher risk of developing MACCE than those in the low TyG index group(23.63%) (P<0.01). The multifactorial RCS model showed a nonlinear correlation (nonlinear P<0.01, overall P<0.01), with a critical value of 8.80 for the TyG index to predict the occurrence of MACCE. The TyG index was able to further improve the predictive accuracy of MACCE. Conclusions: TyG index is a potential predictor of the risk of MACCE in patients with CHD complicated with depression.

Electrochemical biosensor based on the integration of maple leaf-like gold nanocrystal and truncated aptamer for detection of α-amanitin with high sensitivity, selectivity and rapidity.

This study reports the fabrication of three-dimensional gold nanocrystals as sensing material in the presence of l-glutathion and high-performance aptamer with 20 bases of α-amanitin via truncation and optimization of along aptamer. The resulting maple leaf-like gold nanocrystal (ML-Au) exhibits an improved catalytic activity due to more exposed high-index facets. The use of truncated aptamer increases the sensitivity by 15 times and reduces the reaction time by two times compared with those of original aptamer. An α-amanitin electrochemical biosensor constructed by integrating ML-Au nanocrystals with truncated aptamer exhibits high sensitivity, selectivity and rapidity. An increase of the α-amanitin concentration in the range of 1 × 10-14-1 × 10-9 M causes a linear decrease in the amperometric current with a limit of detection of 2.9 × 10-15 M (S/N = 3). The proposed analytical method is satisfactorily used for electrochemical sensing of α-amanitin in urine and wild mushroom samples.

Thin-film transistor for temporal self-adaptive reservoir computing with closed-loop architecture.

Reservoir computing is a powerful neural network-based computing paradigm for spatiotemporal signal processing. Recently, physical reservoirs have been explored based on various electronic devices with outstanding efficiency. However, the inflexible temporal dynamics of these reservoirs have posed fundamental restrictions in processing spatiotemporal signals with various timescales. Here, we fabricated thin-film transistors with controllable temporal dynamics, which can be easily tuned with electrical operation signals and showed excellent cycle-to-cycle uniformity. Based on this, we constructed a temporal adaptive reservoir capable of extracting temporal information of multiple timescales, thereby achieving improved accuracy in the human-activity-recognition task. Moreover, by leveraging the former computing output to modify the hyperparameters, we constructed a closed-loop architecture that equips the reservoir computing system with temporal self-adaptability according to the current input. The adaptability is demonstrated by accurate real-time recognition of objects moving at diverse speed levels. This work provides an approach for reservoir computing systems to achieve real-time processing of spatiotemporal signals with compound temporal characteristics.

Flash-based content addressable memory with L2 distance for memory-augmented neural network.

Memory-augmented neural network (MANN) has received increasing attention as a promising approach to achieve lifelong on-device learning, of which implementation of the explicit memory is vital. Content addressable memory (CAM) has been designed to accelerate the explicit memory by harnessing the in-memory-computing capability. In this work, a CAM cell with quadratic code is proposed, and a 1Mb Flash-based multi-bit CAM chip capable of computing Euclidean (L2) distance is fabricated. Compared with ternary CAM, the latency and energy are significantly reduced by 5.3- and 46.6-fold, respectively, for the MANN on Omniglot dataset. Besides, the recognition accuracy has slight degradation (<1%) even after baking for 105 s at 200°C, demonstrating the robustness to environmental disturbance. Performance evaluation indicates a reduction of 471-fold in latency and 1267-fold in energy compared with GPU for search operation. The proposed robust and energy-efficient CAM provides a promising solution to implement lifelong on-device machine intelligence.

A new and effective two-step clustering approach for single cell RNA sequencing data.

BACKGROUND: The rapid devolvement of single cell RNA sequencing (scRNA-seq) technology leads to huge amounts of scRNA-seq data, which greatly advance the research of many biomedical fields involving tissue heterogeneity, pathogenesis of disease and drug resistance etc. One major task in scRNA-seq data analysis is to cluster cells in terms of their expression characteristics. Up to now, a number of methods have been proposed to infer cell clusters, yet there is still much space to improve their performance. RESULTS: In this paper, we develop a new two-step clustering approach to effectively cluster scRNA-seq data, which is called TSC - the abbreviation of Two-Step Clustering. Particularly, by dividing all cells into two types: core cells (those possibly lying around the centers of clusters) and non-core cells (those locating in the boundary areas of clusters), we first clusters the core cells by hierarchical clustering (the first step) and then assigns the non-core cells to the corresponding nearest clusters (the second step). Extensive experiments on 12 real scRNA-seq datasets show that TSC outperforms the state of the art methods. CONCLUSION: TSC is an effective clustering method due to its two-steps clustering strategy, and it is a useful tool for scRNA-seq data analysis.

Learning emotional dialects: A British population study of cross-cultural communication.

The aim of the current research was to explore whether we can improve the recognition of cross-cultural freely-expressed emotional faces in British participants. We tested several methods for improving the recognition of freely-expressed emotional faces, such as different methods for presenting other-culture expressions of emotion from individuals from Chile, New Zealand and Singapore in two experimental stages. In the first experimental stage, in phase one, participants were asked to identify the emotion of cross-cultural freely-expressed faces. In the second phase, different cohorts were presented with interactive side-by-side, back-to-back and dynamic morphing of cross-cultural freely-expressed emotional faces, and control conditions. In the final phase, we repeated phase one using novel stimuli. We found that all non-control conditions led to recognition improvements. Morphing was the most effective condition for improving the recognition of cross-cultural emotional faces. In the second experimental stage, we presented morphing to different cohorts including own-to-other and other-to-own freely-expressed cross-cultural emotional faces and neutral-to-emotional and emotional-to-neutral other-culture freely-expressed emotional faces. All conditions led to recognition improvements and the presentation of freely-expressed own-to-other cultural-emotional faces provided the most effective learning. These findings suggest that training can improve the recognition of cross-cultural freely-expressed emotional expressions.

Adjustable short-term memory of SiOx:Ag-based memristor for reservoir computing.

Temporal information processing is critical for a wide spectrum of applications, such as finance, biomedicine, and engineering. Reservoir computing (RC) can efficiently process temporal information with low training costs. Various memristors have been explored to demonstrate RC systems leveraging the short-term memory and nonlinear dynamic behaviours. However, the short-term memory is fixed after the device fabrication, limiting the applications to diverse temporal analysis tasks. In this work, we propose the approaches to modulating the short-term memory of Pt/SiOx:Ag/Pt memristor for the performance improvement of the RC systems. By controlling the read voltage, pulse amplitude and pulse width applied to the devices, the obtainable range of the characteristic time reaches three orders of magnitude from microseconds to around milliseconds. Based on the fabricated memristor, the classification of 4-bit pulse streams is demonstrated. Memristor-based RC systems with adjustable short-term memory are constructed for time-series prediction and pattern recognition tasks with different requirements for the characteristic times. The simulation results show that low normalized root mean square error of 0.003 (0.27) in Hénon map (Mackey-Glass time series) and excellent classification accuracy of 99.6% (91.7%) in spoken-digit recognition (MNIST image recognition) are achieved, which outperforms most memristor-based RC systems recently reported. Furthermore, the RC networks with diverse short-term memories are constructed to address more complicated tasks with low prediction errors. This work proves the high controllability of memristor-based RC systems to handle multiple temporal processing tasks.

HPC-Atlas: Computationally Constructing A Comprehensive Atlas of Human Protein Complexes.

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells. For a complete understanding of human cellular functions, it is crucial to have a comprehensive atlas of human protein complexes. Unfortunately, we still lack such a comprehensive atlas of experimentally validated protein complexes, which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes, as well as the underlying biological mechanisms. To fill this gap, we built Human Protein Complexes Atlas (HPC-Atlas), as far as we know, the most accurate and comprehensive atlas of human protein complexes available to date. We integrated two latest protein interaction networks, and developed a novel computational method to identify nearly 9000 protein complexes, including many previously uncharacterized complexes. Compared with the existing methods, our method achieved outstanding performance on both testing and independent datasets. Furthermore, with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-affected human protein complexes, and 456 multifunctional proteins that contain many potential moonlighting proteins. These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources, but also a valuable resource for exploring new biological findings. The HPC-Atlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

Co-composting of cattle manure and wheat straw covered with a semipermeable membrane: organic matter humification and bacterial community succession.

Semipermeable membrane-covered composting is one of the most commonly used composting technologies in northeast China, but its humification process is not yet well understood. This study employed a semipermeable membrane-covered composting system to detect the organic matter humification and bacterial community evolution patterns over the course of agricultural waste composting. Variations in physicochemical properties, humus composition, and bacterial communities were studied. The results suggested that membrane covering improved humic acid (HA) content and degree of polymerization (DP) by 9.28% and 21.57%, respectively. Bacterial analysis indicated that membrane covering reduced bacterial richness and increased bacterial diversity. Membrane covering mainly affected the bacterial community structure during thermophilic period of composting. RDA analysis revealed that membrane covering may affect the bacterial community by altering the physicochemical properties such as moisture content. Correlation analysis showed that membrane covering activated the dominant genera Saccharomonospora and Planktosalinus to participate in the formation of HS and HA in composting, thus promoting HS formation and its structural complexity. Membrane covering significantly reduced microbial metabolism during the cooling phase of composting.

Pre-Ball-Milled Boron Nitride for the Preparation of Boron Nitride/Polyetherimide Nanocomposite Film with Enhanced Breakdown Strength and Mechanical Properties for Thermal Management.

Modern electronics not only require the thermal management ability of polymer packaging materials but also need anti-voltage and mechanical properties. Boron nitride nanosheets (BNNS), an ideal thermally conductive and high withstand voltage (800 kV/mm) filler, can meet application needs, but the complex and low-yield process limits their large-scale fabrication. Herein, in this work, we prepare sucrose-assisted ball-milled BN(SABM-BN)/polyetherimide (PEI) composite films by a casting-hot pressing method. SABM-BN, as a pre-ball-milled filler, contains BNNS and BN thick sheets. We mainly investigated the thermal conductivity (TC), breakdown strength, and mechanical properties of composites. After pre-ball milling, the in-plane TC of the composite film is reduced. It decreases from 2.69 to 2.31 W/mK for BN/PEI composite film at 30 wt% content; however, the through-plane TC of composites is improved, and the breakdown strength and tensile strength of the composite film reach the maximum of 54.6 kV/mm and 102.7 MPa at 5 wt% content, respectively. Moreover, the composite film is used as a flexible circuit substrate, and the working surface temperature is 20 ℃, which is lower than that of pure PEI film. This study provides an effective strategy for polymer composites for electronic packaging.

Highly sensitive and selective electrochemical aptasensor with gold-aspartic acid, glycine acid-functionalized and boron-doped graphene quantum dot nanohybrid for detection of α-amanitin in blood.

Poisonous mushroom may cause fatal harm to human and animal, but its rapid detection still faces a great challenge. The paper reports synthesis of gold-aspartic acid, glycine acid-functionalized and boron-doped graphene quantum dot nanohybrid (AGB-GQD@Au) for the electrochemical detection of α-amanitin. AGB-GQD was prepared by pyrolysis and then reacted with chloroauric acid to produce gold nanoparticles. AGB-GQD@Au offers 12.5 nm-sized particles and Schottky heterojunction, improving the catalytic activity. AGB-GQD@Au connected with hairpin DNA and thionine by Au-S bonds was used as redox probe for electrochemical detection of α-amanitin coupled with one target-induced DNA cycle amplification strategy. α-Amanitin specifically hybridizes with aptamer in duplex DNA to release auxiliary strand DNA. The released DNA triggers one DNA cycle process and brings one redox probe to the electrode surface. By the DNA cycle, one target brings many redox probes to the electrode surface, producing a significant signal amplification. The detection signal was further enhanced by the catalysis of AGB-GQD@Au towards redox of thionine. Differential pulse voltammetric current increases linearly with the increasing α-amanitin in the range from 4 to 4 × 105 fM with the detection limit of 1.2 fΜ (S/N = 3). The analytical method provides advantages of sensitivity, selectivity and repeatability. It has been successfully applied in electrochemical detection of α-amanitin in blood.

Nanoencapsulation of chitooligosaccharides enhances its oral bioavailability and anti-liver fibrotic effects.

Chitooligosaccharides (COS) significantly attenuates liver dysfunction. However, the conundrum of the oral bioavailability of COS limits their pharmacological effects. Therefore, a strategy of nanoencapsulation was employed to enhance oral bioavailability and tissue-targeted distribution of COS. In this study, nanospheres loaded with COS (CANs) were prepared, their bioavailability, biodistribution, transport mechanism and anti-liver fibrosis effects were explored. Nanoencapsulation improved the oral bioavailability of various COS monomers through microfold cell-mediated absorption route in an indiscriminate manner. CANs were more favorably enriched and protractedly accumulated in the liver. In a liver fibrosis model, CANs ameliorated the pathological state and extracellular matrix deposition. The alleviation of liver fibrosis for COS could be attributed to the inhibition of liver cell apoptosis and liver sinusoidal endothelial cell (LSEC) capillarization. Consequently, this study highlights the improved oral bioavailability of COS and proposes a novel mechanism of COS, for better understanding its hepatoprotective effect.

Hardware Demonstration of SRDP Neuromorphic Computing with Online Unsupervised Learning Based on Memristor Synapses.

Neuromorphic computing has shown great advantages towards cognitive tasks with high speed and remarkable energy efficiency. Memristor is considered as one of the most promising candidates for the electronic synapse of the neuromorphic computing system due to its scalability, power efficiency and capability to simulate biological behaviors. Several memristor-based hardware demonstrations have been explored to achieve the capacity of unsupervised learning with the spike-rate-dependent plasticity (SRDP) learning rule. However, the learning capacity is limited and few of the memristor-based hardware demonstrations have explored the online unsupervised learning at the network level with an SRDP algorithm. Here, we construct a memristor-based hardware system and demonstrate the online unsupervised learning of SRDP networks. The neuromorphic system consists of multiple memristor arrays as the synapse and the discrete CMOS circuit unit as the neuron. Unsupervised learning and online weight update of 10 MNIST handwritten digits are realized by the constructed SRDP networks, and the recognition accuracy is above 90% with 20% device variation. This work paves the way towards the realization of large-scale and efficient networks for more complex tasks.

Effectively Clustering Single Cell RNA Sequencing Data by Sparse Representation.

Clustering analysis has been widely used in analyzing single-cell RNA-sequencing (scRNA-seq) data to study various biological problems at cellular level. Although a number of scRNA-seq data clustering methods have been developed, most of them evaluate the similarity of pairwise cells while ignoring the global relationships among cells, which sometimes cannot effectively capture the latent structure of cells. In this paper, we propose a new clustering method SPARC for scRNA-seq data. The most important feature of SPARC is a novel similarity metric that uses the sparse representation coefficients of each cell in terms of the other cells to measure the relationships among cells. In addition, we develop an outlier detection method to help parameter selection in SPARC. We compare SPARC with nine existing scRNA-seq data clustering methods on twelve real datasets. Experimental results show that SPARC achieves the state of the art performance. By further analyzing the cell similarity data derived from sparse representations, we find that SPARC is much more effective in mining high quality clusters of scRNA-seq data than two traditional similarity metrics. In conclusion, this study provides a new way to effectively cluster scRNA-seq data and achieves more accurate clustering results than the state of art methods.

Highly dispersed RuO2-biomass carbon composite made by immobilization of ruthenium and dissolution of coconut meat with octyl ammonium salicylate ionic liquid for high performance flexible supercapacitor.

Poor dispersion of metal oxide-biomass carbon composite limits its further improvement in electrochemical properties. The study reports synthesis of highly dispersed RuO2-biomass carbon nanocomposite (HD-RuO2-BC). Octyl ammonium salicylate ionic liquid was combined with Ru3+ ion to form Ru-based ionic liquid. Followed by addition of coconut meat, microwave treatment to form homogeneous solution, thermal reduction in N2 and oxidation in air in sequence. The resulting HD-RuO2-BC shows three-dimensional architecture and high Ru loading of 9.2%. RuO2 nanoparticles of 6.2 nm were uniformly dispersed in biomass carbon sheets. Excellent dispersion and small size of RuO2 nanoparticles achieve to a significant synergy between RuO2 and biomass carbon. HD-RuO2-BC electrode gives high capacitance of 907.7 F g-1 at 1 A g-1. The value is more than that of BC (150.6 F g-1) and RuO2 electrodes (584.7 F g-1), verifying that introduction of RuO2 achieves to an obviously enhanced capacitance. The symmetrical flexible supercapacitor exhibits excellent supercapacitor performances, including high capacitance (403.8 F g-1 at 1.0 A g-1), rate-capacity (223.1 F g-1 at 50 A g-1), cycling stability (98.2% capacity retention after 10,000 cycles at 50 A g-1) and energy density (378.7 Wh Kg-1at power density of 5199.2 W kg-1).

New Progress on Fiber-Based Thermoelectric Materials: Performance, Device Structures and Applications.

With the rapid development of wearable electronics, looking for flexible and wearable generators as their self-power systems has proved an extensive task. Fiber-based thermoelectric generators (FTEGs) are promising candidates for these self-powered systems that collect energy from the surrounding environment or human body to sustain wearable electronics. In this work, we overview performances and device structures of state-of-the-art fiber-based thermoelectric materials, including inorganic fibers (e.g., carbon fibers, oxide fibers, and semiconductor fibers), organic fibers, and hybrid fibers. Moreover, potential applications for related thermoelectric devices are discussed, and future developments in fiber-based thermoelectric materials are also briefly expected.

Bibliometric Analysis of Current Status on Bioremediation of Petroleum Contaminated Soils during 2000-2019.

Petroleum contaminated soils have become a great concern worldwide. Bioremediation has been widely recognized as one of the most promising technologies and has played an important role in solving the issues of petroleum contaminated soils. In this study, a bibliometric analysis using VOSviewer based on Web of Science data was conducted to provide an overview on the field of bioremediation of petroleum contaminated soils. A total of 7575 articles were analyzed on various aspects of the publication characteristics, such as publication output, countries, institutions, journals, highly cited papers, and keywords. An evaluating indicator, h-index, was applied to characterize the publications. The pace of publishing in this field increased steadily over last 20 years. China accounted for the most publications (1476), followed by the United States (1032). The United States had the highest h-index (86) and also played a central role in the collaboration network among the most productive countries. The Chinese Academy of Sciences was the institution with the largest number of papers (347) and cooperative relations (52). Chemosphere was the most productive journal (360). Our findings indicate that the influence of developing countries has increased over the years, and researchers tend to publish articles in high-quality journals. At present, mainstream research is centered on biostimulation, bioaugmentation, and biosurfactant application. Combined pollution of petroleum hydrocarbons and heavy metals, microbial diversity monitoring, biosurfactant application, and biological combined remediation technology are considered future research hotspots.

Boosting scRNA-seq data clustering by cluster-aware feature weighting.

BACKGROUND: The rapid development of single-cell RNA sequencing (scRNA-seq) enables the exploration of cell heterogeneity, which is usually done by scRNA-seq data clustering. The essence of scRNA-seq data clustering is to group cells by measuring the similarities among genes/transcripts of cells. And the selection of features for cell similarity evaluation is of great importance, which will significantly impact clustering effectiveness and efficiency. RESULTS: In this paper, we propose a novel method called CaFew to select genes based on cluster-aware feature weighting. By optimizing the clustering objective function, CaFew obtains a feature weight matrix, which is further used for feature selection. The genes have large weights in at least one cluster or the genes whose weights vary greatly in different clusters are selected. Experiments on 8 real scRNA-seq datasets show that CaFew can obviously improve the clustering performance of existing scRNA-seq data clustering methods. Particularly, the combination of CaFew with SC3 achieves the state-of-art performance. Furthermore, CaFew also benefits the visualization of scRNA-seq data. CONCLUSION: CaFew is an effective scRNA-seq data clustering method due to its gene selection mechanism based on cluster-aware feature weighting, and it is a useful tool for scRNA-seq data analysis.

Sodium citrate as a self-sacrificial sodium compensation additive for sodium-ion batteries.

Commercial sodium citrate is proposed as the self-sacrificial cathode additive for the first time to offset the initial sodium loss. The optimum additive can obviously increase the energy density of the as-constructed hard carbon//Na3V2(PO4)2F3/rGO full-cell by 28.9% without sacrificing its other electrochemical properties, showing promising application prospects in sodium ion batteries.

Post-Treating the Precursor Intermediate Film by a Cooling Stage for Fabricating Efficient Formamidinium-Based Perovskite Solar Cells.

Formamidinium lead iodide (FAPbI3) is a category of perovskite material with an ideal band gap and high thermal stability, which can be efficiently prepared by two-step spin-coating. Spin-coating organic salts and transforming intermediate phase at the second step involves a components' reaction and state transition, thus playing a crucial role in the film quality formed afterward and optoelectronic properties of the fabricated perovskite solar cells (PSCs). In this paper, a cooling stage (CO) is used to post-treat the as-prepared precursor after the second spin-coating. The procedure of intermediate phase transferring to other state is found to be retarded; hence, the appearing velocity of perovskite nucleation is decreased. As a result, components react more adequately and larger perovskite grains with fewer defects are obtained; charge transport as well as carrier recommbination behaviors are therefore optimized. The PSCs based on the CO process achieved a champion power conversion efficiency (PCE) of 21.51% with enhanced stability. Moreover, CO treatment is observed to be beneficial for improving the film quality of perovskite in large-area preparation, which we anticipate can be further extended to the commercialized application of PSCs.