Magicking an oxyhalide interface for 4.8 V-tolerant high-nickel cathodes in all-solid-state lithium-ion batteries.

All-solid-state lithium batteries (ASSBs) have received increasing attentions as one promising candidate for the next-generation energy storage devices. Among various solid electrolytes, sulfide-based ASSBs combined with layered oxide cathodes have emerged due to the high energy density and safety performance, even at high-voltage conditions. However, the interface compatibility issues remain to be solved at the interface between the oxide cathode and sulfide electrolyte. To circumvent this issue, we propose a simple but effective approach to magic the adverse surface alkali into a uniform oxyhalide coating on LiNi0.8Co0.1Mn0.1O2 (NCM811) via a controllable gas-solid reaction. Due to the enhancement of the close contact at interface, the ASSBs exhibit improved kinetic performance across a broad temperature range, especially at the freezing point. Besides, owing to the high-voltage tolerance of the protective layer, ASSBs demonstrate excellent cyclic stability under high cutoff voltages (500 cycles ~ 94.0% at 4.5 V, 200 cycles ~ 80.4% at 4.8 V). This work provides insights into using a high voltage stable oxyhalide coating strategy to enhance the development of high energy density ASSBs.

MFPINC: prediction of plant ncRNAs based on multi-source feature fusion.

Non-coding RNAs (ncRNAs) are recognized as pivotal players in the regulation of essential physiological processes such as nutrient homeostasis, development, and stress responses in plants. Common methods for predicting ncRNAs are susceptible to significant effects of experimental conditions and computational methods, resulting in the need for significant investment of time and resources. Therefore, we constructed an ncRNA predictor(MFPINC), to predict potential ncRNA in plants which is based on the PINC tool proposed by our previous studies. Specifically, sequence features were carefully refined using variance thresholding and F-test methods, while deep features were extracted and feature fusion were performed by applying the GRU model. The comprehensive evaluation of multiple standard datasets shows that MFPINC not only achieves more comprehensive and accurate identification of gene sequences, but also significantly improves the expressive and generalization performance of the model, and MFPINC significantly outperforms the existing competing methods in ncRNA identification. In addition, it is worth mentioning that our tool can also be found on Github ( https://github.com/Zhenj-Nie/MFPINC ) the data and source code can also be downloaded for free.

Main Group SnN4O Single Sites with Optimized Charge Distribution for Boosting the Oxygen Reduction Reaction.

Transition metal single-atom catalysts (SACs) have been regarded as possible alternatives to platinum-based materials due to their satisfactory performance of the oxygen reduction reaction (ORR). By contrast, main-group metal elements are rarely studied due to their unfavorable surface and electronic states. Herein, a main-group Sn-based SAC with penta-coordinated and asymmetric first-shell ligands is reported as an efficient and robust ORR catalyst. The introduction of the vertical oxygen atom breaks the symmetric charge balance, modulating the binding strength to oxygen intermediates and decreasing the energy barrier for the ORR process. As expected, the prepared Sn SAC exhibits outstanding ORR activity with a high half-wave potential of 0.912 V (vs RHE) and an excellent mass activity of 13.1 A mgSn-1 at 0.850 V (vs RHE), which surpasses that of commercial Pt/C and most reported transition-metal-based SACs. Additionally, the reported Sn SAC shows excellent ORR stability due to the strong interaction between Sn sites and the carbon support with oxygen atom as the bridge. The excellent ORR performance of Sn SAC was also proven by both liquid- and solid-state zinc-air battery (ZAB) measurements, indicating its great potential in practical applications.

From Oxygen Redox to Sulfur Redox: A Paradigm for Li-Rich Layered Cathodes.

The utilization of anionic redox chemistry provides an opportunity to further improve the energy density of Li-ion batteries, particularly for Li-rich layered oxides. However, oxygen-based hosts still suffer from unfavorable structural rearrangement, including the oxygen release and transition metal (TM)-ion migration, in association with the tenuous framework rooted in the ionicity of the TM-O bonding. An intrinsic solution, by using a sulfur-based host with strong TM-S covalency, is proposed here to buffer the lattice distortion upon the highly activating sulfur redox process, and it achieves howling success in stabilizing the host frameworks. Experimental results demonstrate the prolonged preservation of the layered sulfur lattice, especially the honeycomb superlattice, during the Li+ extraction/insertion process in contrast to the large structural degeneration in Li-rich oxides. Moreover, the Li-rich sulfide cathodes exhibited a negligible overpotential of 0.08 V and a voltage drop of 0.13 mV/cycle, while maintaining a substantial reversible capacity upon cycling. These superior electrochemical performances can be unambiguously ascribed to the much shorter trajectories of sulfur in comparison to those of oxygen revealed by molecular dynamics simulations at a large scale (∼30 nm) and a long time scale (∼300 ps) via high-dimensional neural network potentials during the delithiation process. Our findings highlight the importance of stabilizing host frameworks and establish general guidance for designing Li-rich cathodes with durable anionic redox chemistry.

Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network.

BACKGROUND: More and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress associations. RESULTS: In this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (Graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold cross-validation. CONCLUSIONS: The robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction.

PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.

The precise identification of associations between diseases and drugs is paramount for comprehending the etiology and mechanisms underlying parasitic diseases. Computational approaches are highly effective in discovering and predicting disease-drug associations. However, the majority of these approaches primarily rely on link-based methodologies within distinct biomedical bipartite networks. In this study, we reorganized a fundamental dataset of parasitic disease-drug associations using the latest databases, and proposed a prediction model called PDDGCN, based on a multi-view graph convolutional network. To begin with, we fused similarity networks with binary networks to establish multi-view heterogeneous networks. We utilized neighborhood information aggregation layers to refine node embeddings within each view of the multi-view heterogeneous networks, leveraging inter- and intra-domain message passing to aggregate information from neighboring nodes. Subsequently, we integrated multiple embeddings from each view and fed them into the ultimate discriminator. The experimental results demonstrate that PDDGCN outperforms five state-of-the-art methods and four compared machine learning algorithms. Additionally, case studies have substantiated the effectiveness of PDDGCN in identifying associations between parasitic diseases and drugs. In summary, the PDDGCN model has the potential to facilitate the discovery of potential treatments for parasitic diseases and advance our comprehension of the etiology in this field. The source code is available at https://github.com/AhauBioinformatics/PDDGCN .

The effect of ice-cold water spray following the model for symptom management on postoperative thirst in patients admitted to intensive care unit: A randomized controlled study.

BACKGROUND: Postoperative thirst is common in patients admitted to the intensive care unit. Existing methods like wet cotton swabs or oral care prove ineffectual or operationally intricate. Currently, an efficacious postoperative thirst alleviation method remains elusive. Exploring a prompt, safe, and efficacious solution is of paramount importance. OBJECTIVE: To assess the effect of ice-cold water spray applied following a symptom management model on postoperative thirst and to establish a framework for mitigating thirst in intensive care unit patients. RESEARCH DESIGN: Single-center randomized controlled study. SETTING: Surgical intensive care unit in a university-affiliated hospital. MAIN OUTCOME MEASURES: 56 intensive care unit patients were selected and equally randomized. The experimental group received ice-cold water spray in conjunction with eight symptom management strategies, while the control group underwent standard care involving wet cotton swabs. Thirst intervention was initiated 0.5 hours after postoperative extubation, followed by subsequent interventions at 2-hour, 4-hour, and 6-hour intervals post-extubation. Thirst intensity, oral comfort, and the duration of relief from thirst were assessed and compared between groups before and 0.5 hours after each thirst intervention. RESULTS: Across different interventions, the experimental group exhibited superior scores in thirst intensity and oral comfort compared to the control group. Additionally, the nursing time required to alleviate thirst in the experimental group was significantly shorter than that in the control group (P < 0.01). CONCLUSION: Ice-cold water spray following the model for symptom management can effectively mitigate the postoperative thirst intensity in intensive care unit patients, improve oral comfort, and reduce the nursing time for relieving thirst. IMPLICATIONS FOR CLINICAL PRACTICE: Clinical nurses can employ ice-cold water spray following the model for symptom management to ameliorate postoperative thirst intensity in ICU patients while enhancing oral comfort. Furthermore, the utilization of ice-cold water spray can reduce the nursing time required for relieving postoperative thirst in intensive care unit patients.

DNA flexible chain modified MOFs as a versatile platform for chemoenzymatic cascade reactions in glucose catalysis.

Glucose oxidase (GOD) is widely used in the pharmaceutical industry, fermentation products and glucose biosensors for its essential role in catalyzing the conversion of glucose to gluconic acid and hydrogen peroxide (H2O2). As H2O2 is the by-product and will have a toxic effect on glucose oxidase, so introducing another enzyme that could consume H2O2 to form an enzymatic cascade reaction is a practical solution. However, this decision will lead to extra expenses and complex condition optimization such as the specific mass ratio, temperature and pH to improve the activity, stability and recyclability. Herein, we describe a mild and versatile strategy by anchoring GOD on carboxyl-activated MOF (Cu-TCPP(Fe)) through DNA-directed immobilization (DDI) technology. Robust MOF nanosheets were utilized as not only the carrier for the immobilization of GOD, but also a peroxidase-like catalyst for the decomposition of H2O2 to reduce its harmful impacts. In this work, the immobilized GOD retained 55.78% of its initial activity after being used for 7 times. More than 60% of the immobilized enzyme's catalytic activity was still maintained after 96 h of being stored at 50 â„ƒ. This study provides a new idea for preparing immobilized enzymes with enhanced stability, fast diffusion and high activity, which can be used in fields such as biocatalysis and biotechnology.

High Entropy-Induced Kinetics Improvement and Phase Transition Suppression in K-Ion Battery Layered Cathodes.

Layered oxides are widely accepted to be promising cathode candidate materials for K-ion batteries (KIBs) in terms of their rich raw materials and low price, while their further applications are restricted by sluggish kinetics and poor structural stability. Here, the high-entropy design concept is introduced into layered KIB cathodes to address the above issues, and an example of high-entropy layered K0.45Mn0.60Ni0.075Fe0.075Co0.075Ti0.10Cu0.05Mg0.025O2 (HE-KMO) is successfully prepared. Benefiting from the high-entropy oxide with multielement doping, the developed HE-KMO exhibits half-metallic oxide features with a narrow bandgap of 0.19 eV. Increased entropy can also reduce the surface energy of the {010} active facets, resulting in about 2.6 times more exposure of the {010} active facets of HE-KMO than the low-entropy K0.45MnO2 (KMO). Both can effectively improve the kinetics in terms of electron conduction and K+ diffusion. Furthermore, high entropy can inhibit space charge ordering during K+ (de)insertion, and the transition metal-oxygen covalent interaction of HE-KMO is also enhanced, leading to suppressed phase transition of HE-KMO in 1.5-4.2 V and better electrochemical stability of HE-KMO (average capacity drop of 0.20%, 200 cycles) than the low-entropy KMO (average capacity drop of 0.41%, 200 cycles) in the wide voltage window.

A prototype of dual-ion conductor for all-solid-state lithium batteries.

All-solid-state batteries (ASSBs) represent a promising battery strategy to achieve high energy density with great safety. However, inadequate kinetic property and poor interfacial compatibility remain great challenges, which impede their practical application. A prototype of dual-ion conductor of Li+ synchronized with Cu+ unlocks a four-electron redox reaction with high reversibility and fast kinetics. As a result, the constructed ASSB exhibited a high reversible capacity of 603.0 mA·hour g-1 and an excellent cycling retention of 93.2% over 1500 cycles. Moreover, because of the ion highway connecting active materials and catholytes constructed by dual-ion conductor, remarkable temperature tolerance (-60°C) and excellent rate performance (231.6 mA·hour g-1 at 20 mA cm-2) were achieved. The superior electrochemical performance can be ascribed to the migration pathway with small energy barrier and low tortuosity once the Cu+ introduced into Li6PS5Cl. This work creates a unique perspective of ASSBs with dual-ion conducting strategy, thus inspiring a potential developing strategy of state-of-the-art ASSBs.

Sabatier Relations in Electrocatalysts Based on High-entropy Alloys with Wide-distributed d-band Centers for Li-O2 Batteries.

Li-O2 battery (LOB) is a promising "beyond Li-ion" technology with ultrahigh theoretical energy density (3457 Wh kg-1 ), while currently impeded by the sluggish cathodic kinetics of the reversible gas-solid reaction between O2 and Li2 O2 . Despite many catalysts are developed for accelerating the conversion process, the lack of design guidance for achieving high performance makes catalysts exploring aleatory. The Sabatier principle is an acknowledged theory connecting the scaling relationship with heterogeneous catalytic activity, providing a tradeoff strategy for the topmost performance. Herein, a series of catalysts with wide-distributed d-band centers (i.e., wide range of adsorption strength) are elaborately constructed via high-entropy strategy, enabling an in-depth study of the Sabatier relations in electrocatalysts for LOBs. A volcano-type correlation of d-band center and catalytic activity emerges. Both theoretical and experimental results indicate that a moderate d-band center with appropriate adsorption strength propels the catalysts up to the top. As a demonstration of concept, the LOB using FeCoNiMnPtIr as catalyst provides an exceptional energy conversion efficiency of over 80 %, and works steadily for 2000 h with a high fixed specific capacity of 4000 mAh g-1 . This work certifies the applicability of Sabatier principle as a guidance for designing advanced heterogeneous catalysts assembled in LOBs.

Nonlinear error reduction for phase-shifting profilometry considering periodicity and symmetry of a phase histogram.

Phase-shifting profilometry is extensively utilized for three-dimensional (3D) measurement. However, because of gamma nonlinearity, the image intensities of the captured fringe patterns are regrettably distorted. An effective nonlinear error reduction method without requiring parameter estimation is presented in this paper. Differing from the traditional whole-period phase histogram equalization (PHE) method, our method takes into account not only the periodicity but also the symmetry of the phase histogram. Taking a three-step phase-shifting algorithm as an example, the phase error frequency triples the fringe frequency; thus, we first propose a 1/3-period PHE method. Moreover, since the phase error distribution is sinusoidal with symmetry, we further propose a 1/6-period PHE method. Simulations and experiments both indicate that the 1/6-period PHE method, compared with the whole-period PHE and 1/3-period PHE methods, can further reduce the nonlinear error.

Construction of co-immobilized multienzyme systems using DNA-directed immobilization technology and multifunctionalized nanoparticles.

The multienzyme co-immobilization systems with high cascade catalytic efficiency and selectivity have attracted considerable attention. In this study, through DNA-directed immobilization (DDI) technology, two model enzymes, glucose oxidase (GOD) and horseradish peroxide (HRP) were co-immobilized on the multifunctional silica nanoparticles (DDI enzyme). In addition to the directional distribution promoted by DNA complementary chains, the multienzyme system allowed the control of the stoichiometric ratio of the enzymes by adjusting the ratio of amino/carboxyl groups. The optimal mole ratio of GOD/HRP was 1:2, while the protein loading amount could reach 8.06 mg·g-1. Compared with the conventional direct adsorption, the catalytic activity of the DDI enzyme was 2.49 times higher. Moreover, with the enhancement of thermal and mechanical stability, the DDI enzyme could still retain at least 50% of its initial activity after 12 cycles. Accompanied by an excellent response and good selectivity, the constructed multienzyme systems simultaneously showed the potential as a glucose detector. Therefore, based on the DDI technology, the highly efficient multienzyme co-immobilization system could be further extended for a wider range of research fields.

Intensified Adjuvant Treatment for High-Risk Resected Cutaneous Angiosarcoma of the Head and Neck.

OBJECTIVE: Previous studies have highlighted the poor survival of patients with cutaneous angiosarcoma of the head and neck. Therapeutic options are limited, and effective treatment strategies are yet to be discovered. The objective of this study is to evaluate overall survival following intensified adjuvant treatment for high-risk resected angiosarcoma of the head and neck. STUDY DESIGN: Retrospective observational. SETTING: National Cancer Database (NCDB). METHODS: Patients diagnosed with nonmetastatic cutaneous angiosarcoma of the head and neck from 2004 to 2016 were identified by NCDB. We retrospectively compared demographics and overall survival between patients who received surgery and radiation therapy (SR) and patients who received surgery and chemoradiation (SRC). The χ2 test, Kaplan-Meier method, and Cox regression models were used to analyze data. RESULTS: A total of 249 patients were identified, of which 79.5% were treated with surgery and radiation alone and 20.5% were treated with surgery and chemoradiation. The addition of chemotherapy, regardless of the sequence of administration, was not associated with significantly higher overall survival. Factors associated with worse survival in both groups included positive nodal status and positive margins. Patients with positive nodes had higher overall survival with radiation doses >50.4 Gy compared to ≤50.4 Gy (hazard ratio: 2.93, confidence interval: 1.60-5.36, p < 0.001). CONCLUSION: Adjuvant chemotherapy was not significantly associated with higher overall survival for resected nonmetastatic angiosarcoma of the head and neck. Higher radiation doses appear to be prognostic for high-risk diseases.

Platinum Nanoparticles Loaded Graphitic Carbon Nitride Nanosheets with Enhanced Peroxidase-like Activity for H2O2 and Oxidase-Based Sensing.

Platinum nanoparticles (PtNPs) are classical peroxidase-like nanozyme; self-agglomeration of nanoparticles leads to the undesirable reduction in stability and catalytic activity. Herein, a hybrid peroxidase-like nanocatalyst consisting of PtNPs in situ growing on g-C3N4 nanosheets with enhanced peroxidase-mimic catalytic activity (PtNP@g-C3N4 nanosheets) was prepared for H2O2 and oxidase-based colorimetric assay. g-C3N4 nanosheets can be used as carriers to solve the problem of poor stability of PtNPs. We observed that the catalytic ability could be maintained for more than 90 days. PtNP@g-C3N4 nanosheets could quickly catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB), and the absorbance of blue color oxidized TMB (oxTMB) showed a robust linear relationship with the concentration of H2O2 (the detection limit (LOD): 3.33 µM). By utilizing H2O2 as a mediator, this strategy can be applied to oxidase-based biomolecules (glucose, organophosphorus, and so on, that generate or consume hydrogen peroxide) sensing. As a proof of concept, a sensitive assay of cholesterol that combined PtNP@g-C3N4 nanosheets with cholesterol oxidase (ChOx) cascade catalytic reaction was constructed with an LOD of 9.35 µM in a widespread range from 10 to 800 µM (R2 = 0.9981). In addition, we also verified its ability to detect cholesterol in fetal bovine serum. These results showed application prospect of PtNP@g-C3N4 nanosheets-based colorimetry in sensing and clinical medical detection.

Management of Localized Prostate Cancer in Men With Human Immunodeficiency Virus: Analysis of a Large Retrospective Cohort.

INTRODUCTION: We aimed to characterize the clinicopathological characteristics and outcomes of HIV-positive patients with clinically localized, prostate cancer (PCa). METHODS: A retrospective study was conducted of HIV-positive patients from a single institution with elevated PSA and diagnosis of PCa by biopsy. PCa features, HIV characteristics, treatment type, toxicities, and outcomes were analyzed by descriptive statistics. Kaplan-Meier analysis was used to determine progression-free survival (PFS). RESULTS: Seventy-nine HIV-positive patients were included with a median age at PCa diagnosis of 61 years-old and median duration from HIV infection to PCa diagnosis of 21 years. The median PSA level at diagnosis and Gleason Score was 6.85 ng/mL and 7, respectively. The 5-year PFS was 82.5% with the lowest survival observed in patients treated with radical prostatectomy (RP) + radiation therapy (RT), followed by cryosurgery (CS). There were no reports of PCa-specific deaths, and the 5-year overall survival was 97.5%. CD4 count declined post-treatment in pooled treatment groups that included RT (P = .02). CONCLUSION: We present the characteristics and outcomes of the largest cohort of HIV-positive men with prostate cancer in published literature. RP and RT ± ADT is well-tolerated in HIV-positive patients with PCa as seen by the adequate biochemical control and mild toxicity. CS resulted in worse PFS compared to alternative treatments for patients within the same PCa risk group. A decline in CD4 counts was observed in patients treated RT, and further studies are needed to investigate this relationship. Our findings support the use of standard-of-care treatment for localized PCa in HIV-positive patients.

FA-Net: A Fused Feature for Multi-Head Attention Recoding Network for Pear Leaf Nutritional Deficiency Diagnosis with Visual RGB-Image Depth and Shallow Features.

Accurate diagnosis of pear tree nutrient deficiency symptoms is vital for the timely adoption of fertilization and treatment. This study proposes a novel method on the fused feature multi-head attention recording network with image depth and shallow feature fusion for diagnosing nutrient deficiency symptoms in pear leaves. First, the shallow features of nutrient-deficient pear leaf images are extracted using manual feature extraction methods, and the depth features are extracted by the deep network model. Second, the shallow features are fused with the depth features using serial fusion. In addition, the fused features are trained using three classification algorithms, F-Net, FC-Net, and FA-Net, proposed in this paper. Finally, we compare the performance of single feature-based and fusion feature-based identification algorithms in the nutrient-deficient pear leaf diagnostic task. The best classification performance is achieved by fusing the depth features output from the ConvNeXt-Base deep network model with shallow features using the proposed FA-Net network, which improved the average accuracy by 15.34 and 10.19 percentage points, respectively, compared with the original ConvNeXt-Base model and the shallow feature-based recognition model. The result can accurately recognize pear leaf deficiency images by providing a theoretical foundation for identifying plant nutrient-deficient leaves.

Identifying the "Dangshan" Physiological Disease of Pear Woolliness Response via Feature-Level Fusion of Near-Infrared Spectroscopy and Visual RGB Image.

The "Dangshan" pear woolliness response is a physiological disease that causes large losses for fruit farmers and nutrient inadequacies.The cause of this disease is predominantly a shortage of boron and calcium in the pear and water loss from the pear. This paper used the fusion of near-infrared Spectroscopy (NIRS) and Computer Vision Technology (CVS) to detect the woolliness response disease of "Dangshan" pears. This paper employs the merging of NIRS features and image features for the detection of "Dangshan" pear woolliness response disease. Near-infrared Spectroscopy (NIRS) reflects information on organic matter containing hydrogen groups and other components in various biochemical structures in the sample under test, and Computer Vision Technology (CVS) captures image information on the disease. This study compares the results of different fusion models. Compared with other strategies, the fusion model combining spectral features and image features had better performance. These fusion models have better model effects than single-feature models, and the effects of these models may vary according to different image depth features selected for fusion modeling. Therefore, the model results of fusion modeling using different image depth features are further compared. The results show that the deeper the depth model in this study, the better the fusion modeling effect of the extracted image features and spectral features. The combination of the MLP classification model and the Xception convolutional neural classification network fused with the NIR spectral features and image features extracted, respectively, was the best combination, with accuracy (0.972), precision (0.974), recall (0.972), and F1 (0.972) of this model being the highest compared to the other models. This article illustrates that the accuracy of the "Dangshan" pear woolliness response disease may be considerably enhanced using the fusion of near-infrared spectra and image-based neural network features. It also provides a theoretical basis for the nondestructive detection of several techniques of spectra and pictures.