SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues.

MOTIVATION: RNA N6-methyladenosine (m6A) in Homo sapiens plays vital roles in a variety of biological functions. Precise identification of m6A modifications is thus essential to elucidation of their biological functions and underlying molecular-level mechanisms. Currently available high-throughput single-nucleotide-resolution m6A modification data considerably accelerated the identification of RNA modification sites through the development of data-driven computational methods. Nevertheless, existing methods have limitations in terms of the coverage of single-nucleotide-resolution cell lines and have poor capability in model interpretations, thereby having limited applicability. RESULTS: In this study, we present CLSM6A, comprising a set of deep learning-based models designed for predicting single-nucleotide-resolution m6A RNA modification sites across eight different cell lines and three tissues. Extensive benchmarking experiments are conducted on well-curated datasets and accordingly, CLSM6A achieves superior performance than current state-of-the-art methods. Furthermore, CLSM6A is capable of interpreting the prediction decision-making process by excavating critical motifs activated by filters and pinpointing highly concerned positions in both forward and backward propagations. CLSM6A exhibits better portability on similar cross-cell line/tissue datasets, reveals a strong association between highly activated motifs and high-impact motifs, and demonstrates complementary attributes of different interpretation strategies. AVAILABILITY AND IMPLEMENTATION: The webserver is available at http://csbio.njust.edu.cn/bioinf/clsm6a. The datasets and code are available at https://github.com/zhangying-njust/CLSM6A/.

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.

MOTIVATION: Multiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm's performance. RESULTS: Here, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: MDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

MOTIVATION: The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. RESULTS: Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. AVAILABILITY AND IMPLEMENTATION: PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning.

MOTIVATION: Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed. RESULTS: To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning. AVAILABILITY AND IMPLEMENTATION: MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/.

Regulatory networks of senescence-associated gene-transcription factors promote degradation in Moso bamboo shoots.

Bamboo cultivation, particularly Moso bamboo (Phyllostachys edulis), holds significant economic importance in various regions worldwide. Bamboo shoot degradation (BSD) severely affects productivity and economic viability. However, despite its agricultural consequences, the molecular mechanisms underlying BSD remain unclear. Consequently, we explored the dynamic changes of BSD through anatomy, physiology and the transcriptome. Our findings reveal ruptured protoxylem cells, reduced cell wall thickness and the accumulation of sucrose and reactive oxygen species (ROS) during BSD. Transcriptomic analysis underscored the importance of genes related to plant hormone signal transduction, sugar metabolism and ROS homoeostasis in this process. Furthermore, BSD appears to be driven by the coexpression regulatory network of senescence-associated gene transcription factors (SAG-TFs), specifically PeSAG39, PeWRKY22 and PeWRKY75, primarily located in the protoxylem of vascular bundles. Yeast one-hybrid and dual-luciferase assays demonstrated that PeWRKY22 and PeWRKY75 activate PeSAG39 expression by binding to its promoter. This study advanced our understanding of the molecular regulatory mechanisms governing BSD, offering a valuable reference for enhancing Moso bamboo forest productivity.

Soil bacterial community diversity and life strategy during slope restoration on an uninhabited island: changes under the aggregate spray-seeding technique.

AIMS: We investigated the effects of the aggregate spray-seeding (ASS) technique on soil bacterial community diversity, life strategies, and seasonal change. METHODS AND RESULTS: Soil from six plots with original vegetation (CK, n = 6) was compared to soil from 15 plots with spray-seeding restoration (SR, n = 15) using environmental DNA sequencing. The bacterial Shannon and Chao1 indices of SR soils were significantly greater (P < 0.05) than those of CK soils. The Chao1 index for the SR soil bacterial community was significantly greater in summer (P < 0.05) than in winter. The ratio of the relative abundance of bacterial K-strategists to r-strategists (K/r) and the DNA guanine-cytosine (GC) content in the SR soil were significantly lower (P < 0.05) than those in the CK soil. Principal coordinate analysis revealed significant differences between the SR and CK bacterial communities. The GC content was positively correlated with the K/r ratio. Soil conductivity was negatively associated with the K/r ratio and GC content, indicating that ionic nutrients were closely related to bacterial life strategies. CONCLUSIONS: The ASS technique improved soil bacterial diversity, altered community composition, and favored bacterial r-strategists.

Radiographic outcomes of lateral sinus floor elevation at sites without perforations and sites with perforations managed with a resorbable membrane: A retrospective study.

AIM: To evaluate the radiographic outcomes of lateral sinus floor elevation with simultaneous implant placement at sites without sinus membrane perforation (SMP) and sites with SMP managed with a resorbable membrane. MATERIALS AND METHODS: One hundred and thirty-nine patients and 170 implants (56 perforation, 114 non-perforation) were included. Cone-beam computed tomography (CBCT) images were taken before surgery (T0), immediately after surgery (T1) and 6 months after surgery (T2). Post-operative augmentation parameters, including endo-sinus bone gain (ESBG) along the implant axis, mean new bone height (NBH) surrounding the implant and augmentation volume (AV), were measured at T1 and T2. RESULTS: At T1, there were no significant differences in ESBG, NBH and AV between the two groups. At T2, although ESBG did not significantly differ between the two groups, NBH (8.50 ± 1.99 mm vs. 9.99 ± 2.52 mm, p = .039) and AV (519.37 ± 258.38 mm3 vs. 700.99 ± 346.53 mm3, p < .001) were significantly lower in the perforation group. The shrinkage of graft material from T1 to T2, including ΔESBG (p = .002), ΔNBH (p < .001) and ΔAV (p < .001), was higher in the perforation group. CONCLUSIONS: SMP during LSFE with simultaneous implant placement is associated with greater resorption of the grafted area at a 6-month follow-up.

Effect of oxide interactions on chromium speciation transformation during simulated municipal solid waste incineration.

Chromium released during municipal solid waste incineration (MSWI) is toxic and carcinogenic. The removal of chromium from simulated MSWI flue gas by four sorbents (CaO, bamboo charcoal (BC), powdered activated carbon (PAC), and Al2O3) and the effects of four oxides (SiO2, Al2O3, Fe2O3, and CaO) on chromium speciation transformation were investigated. The results showed that the removal rates of total Cr by the four sorbents were Al2O3 < CaO < PAC < BC, while the removal rates of Cr(VI) by the four sorbents were Al2O3 < PAC < BC < CaO. CaO had a strong oxidizing effect on Cr(III), while BC and PAC had a better-reducing effect on Cr(VI). SiO2 was better for the reduction of Na2CrO4 and K2CrO4 above 1000°C due to its strong acidity, and the addition of CaO significantly inhibited the reduction of Cr(VI). MgCrO4 decomposed above 700°C to form MgCr2O4, and the reaction between MgCrO4 and oxides also existed in the form of a more stable trivalent spinel. Furthermore, when investigating the effect of oxides on the oxidation of Cr(III) in CrCl3, it was discovered that CaO promoted the conversion of Cr(III) to Cr(VI), while the presence of chlorine caused chromium to exist in the form of Cr(V), and increasing the content of CaO and extending the heating time facilitated the oxidation of Cr(III). In addition, silicate, aluminate, and ferrite were generated after the addition of SiO2, Al2O3, and Fe2O3, which reduced the alkalinity of CaO and had an important role in inhibiting the oxidation of Cr(III). The acidic oxides can not only promote the reduction of Cr(VI) but also have an inhibitory effect on the oxidation of Cr(III) ascribed to alkali metals/alkaline earth metals, and the proportion of acidic oxides can be increased moderately to reduce the generation of harmful substances in the hazardous solid waste heat treatment.

A Cut-to-Link Strategy for Cubane-Based Heterometallic Sulfide Clusters with Giant Third-Order Nonlinear Optical Response.

Although the synthesis of low-dimensional metal sulfides by assembling cluster-based units is expected to promote the development of optical materials and models of enzyme active centers such as dinitrogenase, it is faced with limited assembly methodology. Herein we present a cut-to-link strategy to generate high-nuclearity assemblies, inspired by the formation of a Z-type dimer of the W-S-Cu analogues of PN cluster through in situ release of active linkers. Four new compounds with structures based on the same {Tp*WS3Cu3} incomplete cubane-like units were obtained using varied combinations of mild reagents. Open-aperture Z-scan measurements demonstrated the highest-nuclearity complex has the largest nonlinear optical absorption coefficient among discrete cluster-based materials reported to date. This approach enables building high-nuclearity metal sulfide clusters through cluster-based building blocks and opens a way to the design and exploration of materials based on well-identified building blocks.

Causal effects of maternal exposure to PM2.5 during pregnancy on depression symptoms in adolescence: Identifying vulnerable windows and subpopulations in a national cohort study.

Few studies have examined the causal relationship between chronic exposure to air pollutants during pregnancy and depression in adolescent offspring. In addition, it has not been investigated whether exposure is most harmful to adolescents in certain populations and at certain stages of pregnancy. A total of 1975 adolescents from 1632 families from the China Family Panel Study, a representative national longitudinal cohort, were included in this study. We used high-resolution satellite retrieval data to assess the PM2.5 exposure of mothers during pregnancy. Specifically, we employed a two-stage instrumental variable model (IV-2SLS) within the counterfactual causal inference framework, and selected and validated appropriate instruments, thereby mitigating potentially biased results arising from bi-direction between dependent and independent variables. This approach allowed us to explore the causal relationship between maternal PM2.5 exposure during pregnancy and adolescent depression symptoms. The endogeneity of air pollution during pregnancy and the need for a causal model were suggested by the results of the model comparisons. Using the IV-2SLS model, we found that maternal exposure to PM2.5 during pregnancy exacerbates depressive symptoms in the offspring during adolescence (ß = 0.2, 95% CI: 0.05-0.34). We also found that exposure during the first trimester may cause greater harm. Adolescents with low household income, being male, irregular exercise habits, living in rural areas, and having mothers with poorer mental status may be more vulnerable. The findings suggest that maternal exposure to PM2.5 during pregnancy may have a negative impact on the depression symptoms of offspring in adolescence and that more attention should be paid to vulnerable populations and the window of vulnerability.

[Application Status and Prospect of Rehabilitation Assessment Technology].

Rehabilitation assessment is the basis and important part of rehabilitation diagnosis and treatment. At present, clinical evaluation is usually carried out by observation method and scale method. At the same time, researchers monitor patients' physical condition data through sensor system and other equipment as a supplement. The purpose of this study is to review the application and development of objective rehabilitation assessment technology in clinical practice, and to discuss its limitations and strategies to provide reference for related research.

Repositioning of the bone window in lateral sinus floor elevation with simultaneous implant placement: A retrospective radiographic study.

OBJECTIVES: To retrospectively evaluate whether repositioning the bone window leads to a better outcome of three-dimensional sinus augmentation in lateral sinus floor elevation (LSFE) with simultaneous implant placement. METHODS: 34 patients with a total of 40 implants (14: test group, 26: control group) receiving LSFE with simultaneous implant placement were included in this retrospective research. CBCT images were taken before surgery, immediately and 6 months after surgery. The two-dimensional augmentation parameters, including apical bone height (ABH), endo-sinus bone gain (ESBG), and palatal/buccal bone height (PBH/BBH), and three-dimensional parameters, including augmentation volume (AV) and palatal/buccal augmentation volume (PAV/BAV), were measured. The lateral defect length (LDL) and lateral window length (LWL) were also measured to evaluate the lateral antrostomy recovery. RESULTS: At the 6-month follow-up, the reduction rates at ABH, ESBG, and BBH of the test group (ABH: 10.41% ± 30.30%, ESBG: 2.55% ± 8.91%, BBH: 2.50% ± 8.65%) were significantly lower than those of the control group (ABH: 25.10% ± 22.02%, ESBG: 11.47% ± 9.79%, BBH: 7.10% ± 5.37%; p < .05). In addition, the test group showed better three-dimensional augmentation stability on the buccal side (BAV reduction: 15.51% ± 10.86% vs. 27.15% ± 12.61%; p < .05). Moreover, the LDL/LWL ratio of the test group was significantly lower than that of the control group (p < .05). CONCLUSION: Within the limitations of this study, repositioning of the bone window in LSFE with simultaneous implant placement could contribute to endo-sinus augmentation stability on the buccal side at the 6-month follow-up. Moreover, it would also facilitate recovery of the lateral antrostomy defect.

Compressed sensing based dynamic MR image reconstruction by using 3D-total generalized variation and tensor decomposition: k-t TGV-TD.

PURPOSE: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording. METHODS: The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D total generalized variation (3D-TGV) algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed with the HOSVD method, and the localized image sparsity is achieved by the 3D-TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac perfusion and cine MR datasets) are used to evaluate the performance of the proposed method. RESULTS: Compared with the state-of-art methods, such as k-t SLR, 3D-TGV, HOSVD based tensor decomposition and low-rank plus sparse method, the proposed k-t TGV-TD method can offer improved reconstruction accuracy in terms of higher peak SNR (PSNR) and structural similarity index (SSIM). The proposed k-t TGV-TD method can achieve significantly better and stable reconstruction results than state-of-the-art methods in terms of both PSNR and SSIM, especially for cardiac perfusion MR dataset. CONCLUSIONS: This work proved that the k-t TGV-TD method was an effective sparse representation way for DCMRI, which was capable of significantly improving the reconstruction accuracy with different acceleration factors.

Succession of the Resident Soil Microbial Community in Response to Periodic Inoculations.

To maintain the beneficial effects of microbial inoculants on plants and soil, repeated inoculation represents a promising option. Until now, the impacts of one-off inoculation on the native microbiome have been explored, but it remains unclear how long and to what extent the periodic inoculations would affect the succession of the resident microbiome in bulk soil. Here, we examined the dynamic responses of plant growth, soil functions, and the resident bacterial community in the bulk soil to periodic inoculations of phosphate-solubilizing and N2-fixing bacteria alone or in combination. Compared to single-strain inoculation, coinoculation better stimulated plant growth and soil nutrients. However, the benefits from inoculants did not increase with repeated inoculations and were not maintained after transplantation to a different site. In response to microbial inoculants, three patterns of shifts in the bacterial composition were observed: fold increase, fold decrease, and resilience. The periodic inoculations impacted the succession course of resident bacterial communities in bulk soil, mainly driven by changes in soil pH and nitrate, resulting in the development of three main cluster types throughout the investigation. The single and mixed inoculants transiently modulated the variation in the resident community in association with soil pH and the C/N ratio, but finally, the community established and showed resilience to subsequent inoculations. Consequently, the necessity of repeated inoculations should be reconsidered, and while the different microbial inoculants showed distinct impacts on resident microbiome succession, the communities ultimately exhibited resilience.IMPORTANCE Introducing beneficial microbes to the plant-soil system is an environmentally friendly approach to improve the crop yield and soil environment. Numerous studies have attempted to reveal the impacts of inoculation on the rhizosphere microbiome. However, little is known about the effectiveness of periodic inoculations on soil functioning. In addition, the long-term impact of repeated inoculations on the native community remains unclear. Here, we track the succession traits of the resident microbiome in the bulk soil across a growing season and identify the taxon clusters that respond differently to periodic inoculation. Crucially, we compare the development of the resident community composition with and without inoculation, thus providing new insight into the interactions between resident microbes and intruders. Finally, we conclude that initial inoculation plays a more important role in influencing the whole system, and the native microbial community exhibits traits of resilience, but no resistance, to the subsequent inoculations.

Dendrobium officinale flos increases neurotrophic factor expression in the hippocampus of chronic unpredictable mild stress-exposed mice and in astrocyte primary culture and potentiates NGF-induced neuronal differentiation in PC12 cells.

Dendrobium officinale flos (DOF) is the flower of Dendrobium officinale Kimura et Migo, which is usually regarded as a by-product of Dendrobii Offcinalis Caulis. Based on its use as an alternative medicine, we evaluated the antidepressant-like effect of DOF extracts on chronic, unpredictable, mild stress-induced, depression-like behaviour in mice and tested the effects of DOF on the regulation of neurotrophic factors in mouse astrocyte primary cultures and PC12 cell lines. Oral treatment with DOF ethanol extract (DOF-E) could alleviate depression-like behaviours in stress-exposed mice, as evidenced by increased sucrose consumption and decreased immobile time in a forced swim test. In the hippocampus, DOF extracts increased the expression of NGF and BDNF, both at the transcriptional and protein levels. In astrocytes, DOF-E increased the expression of NGF and BDNF via a cAMP-dependent mechanism and regulated plasminogen and matrix metallopeptidase 9 (MMP-9), which are related to the metabolic regulation of neurotrophic factors. In PC12 cells, DOF-E induced the expression of neurofilaments and potentiated the induction of neurite outgrowth upon treatment with a low dose of NGF. Based on these findings, DOF might be used as a supplement for antidepressant therapy in patients with depression.

Deep Reinforcement Learning Control of Quantum Cartpoles.

We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep learning approach performs comparably to or better than other strategies in standard control theory. Our approach also applies to measurement-feedback cooling of quantum oscillators, showing the applicability of deep learning to general continuous-space quantum control.

Atomic-Scale Dispersed Fe-Based Catalysts Confined on Nitrogen-Doped Graphene for Li-S Batteries: Polysulfides with Enhanced Conversion Efficiency.

Lithium-sulfur batteries have been considered as potential electrochemical energy-storage devices owing to their satisfactory theoretical energy density. Nonetheless, the inferior conversion efficiency of polysulfides in essence leads to fast capacity decay during the discharge/charge cycle. In this work, it is successfully demonstrated that the conversion efficiency of lithium polysulfides is remarkably enhanced by employing a well-distributed atomic-scale Fe-based catalyst immobilized on nitrogen-doped graphene (Fe@NG) as a coating of separator in lithium-sulfur batteries. The quantitative electrocatalytic efficiency of the conversion of lithium polysulfides is determined through cyclic voltammetry. It is also proven that the Fe-NX configuration with highly catalytic activity is quite beneficial for the conversion of lithium polysulfides. In addition, the adsorption and permeation experiments distinctly indicate that the strong anchoring effect, originated from the charge redistribution of N doping into the graphene matrix, inhibits the movement of lithium polysulfides. Thanks to these advantages, if the as-prepared Fe@NG catalyst is combined with polypropylene and applied as a separator (Fe@NG/PP) in Li-S batteries, a high initial capacity (1616 mA h g-1 at 0.1 C), excellent capacity retention (93 % at 0.2 C, 70 % at 2 C), and superb rate performance (820 mA h g-1 at 2 C) are achieved.

Effect of Running Speed and Midsole Type on Foot Loading in Heel-Toe Running.

The purpose of this study was to explore the immediate effects of running speed and midsole type on foot loading during heel-toe running. Fifteen healthy male college students were required to complete 3 running trials on an indoor 45-m tartan runway at 4 different speeds (3, 4, 5, and 6 m/s) using 2 different running footwear types (engineering thermoplastic polyurethane elastomer, polyurethane elastomer; and ethylene vinyl acetate, vinyl acetate). The ground reaction force and plantar pressure data were quantified. Significant speed effects were detected both in ground reaction force and plantar pressure-related data (P < .05). Vertical average loading rate was significantly less, and time to first peak occurred later for the polyurethane elastomer compared with vinyl acetate footwear (P < .05). The peak pressure of the heel, medial forefoot, central forefoot, lateral forefoot, and big toe was significantly less when subjects wore a polyurethane elastomer than vinyl acetate footwear (P < .05). Overall, our results suggested that, compared with the vinyl acetate footwear, the special polyurethane elastomer footwear that is adhered with thousands of polyurethane elastomer granules was effective at reducing the mechanical impact on the foot.