Polysaccharide from fermented mycelium of Inonotus obliquus attenuates the ulcerative colitis and adjusts the gut microbiota in mice.

Ulcerative colitis (UC) is a disease characterized by chronic inflammation of the colon. Polysaccharides not only have biological activities but also can regulate gut microbiota to alleviate the symptoms of UC. In this study, polysaccharide extracted from mycelium of Inonotus obliquus (IOP) was prescribed to treat UC induced by dextran sodium sulfate (DSS) in mice. Compared to model control group (MC), IOP-Low, IOP-Medium and IOP-High (IOP-L, IOP-M and IOP-H) treatment groups increased the body weight rate by 6.0%-9.6%, colon length by 8.57%-25.14% and superoxide dismutase (SOD) activity by 53.8-110.4 U/mg, while decreased the malondialdehyde (MDA) content by 37.4%-64.8%, myeloperoxidase (MPO) activity by 29.0%-46.9%, and the concentration of nitric oxide (NO) by 24.8-35.6 µmol/L. IOP treatment also promoted the secretion of interleukin (IL)-10 but suppressed those of interleukin (IL)-6, interleukin (IL)-1ß and tumor necrosis factor (TNF)-α. Simultaneously, analysis of high-throughput sequencing indicated that IOP reduced the ratio of Firmicutes to Bacteroidetes (F/B) at phylum level, and increased the relative abundance of Bacteroides and Lactobacillus at genus level. In brief, IOP may be a promising alternative medicine for UC remedy by regulating the anti-inflammatory level, the anti-oxidative ability and the gut microbiota composition.

Efficacy of a WeChat-Based Multimodal Digital Transformation Management Model in New-Onset Mild to Moderate Hypertension: Randomized Clinical Trial.

BACKGROUND: The advantages of multimodal digitally transformed mobile health management for patients diagnosed with mild to moderate hypertension are not yet established. OBJECTIVE: We aim to evaluate the therapeutic benefits of a novel WeChat-based multimodal digital transforming management model in mobile health blood pressure (BP) management. METHODS: This randomized controlled clinical trial included 175 individuals with new-onset mild to moderate hypertension who were admitted to our center between September and October 2022. The patients were randomly assigned to either the multimodal intervention group (n=88) or the usual care group (n=87). The primary composite outcome was home and office BP differences after 6 months. The major secondary outcomes were 6-month quality-of-life scores, including the self-rating anxiety scale, self-rating depression scale, and Pittsburgh Sleep Quality Index. RESULTS: The mean home BP decreased from 151.74 (SD 8.02)/94.22 (SD 9.32) to 126.19 (SD 8.45)/82.28 (SD 9.26) mm Hg in the multimodal intervention group and from 150.78 (SD 7.87)/91.53 (SD 9.78) to 133.48 (SD 10.86)/84.45 (SD 9.19) mm Hg in the usual care group, with a mean difference in systolic blood pressure and diastolic blood pressure of -8.25 mm Hg (95% CI -11.71 to -4.78 mm Hg; P<.001) and -4.85 mm Hg (95% CI -8.41 to -1.30 mm Hg; P=.008), respectively. The mean office BP decreased from 153.64 (SD 8.39)/93.56 (SD 8.45) to 127.81 (SD 8.04)/ 82.16 (SD 8.06) mm Hg in the multimodal intervention group and from 151.48 (SD 7.14)/(91.31 (SD 9.61) to 134.92 (SD 10.11)/85.09 (SD 8.26) mm Hg in the usual care group, with a mean difference in systolic blood pressure and diastolic blood pressure of -9.27 mm Hg (95% CI -12.62 to -5.91 mm Hg; P<.001) and -5.18 mm Hg (95% CI -8.47 to -1.89 mm Hg; P=.002), respectively. From baseline to 6 months, home BP control <140/90 mm Hg was achieved in 64 (72.7%) patients in the multimodal intervention group and 46 (52.9%) patients in the usual care group (P=.007). Meanwhile, home BP control <130/80 mm Hg was achieved in 32 (36.4%) patients in the multimodal intervention group and 16 (18.4%) patients in the usual care group (P=.008). After 6 months, there were significant differences in the quality-of-life total and graded scores, including self-rating anxiety scale scores (P=.04), self-rating depression scale scores (P=.03), and Pittsburgh Sleep Quality Index scores (P<.001), in the multimodal intervention group compared with the usual care group. CONCLUSIONS: The WeChat-based multimodal intervention model improved the BP control rates and lowered the BP levels more than the usual care approach. The multimodal digital transforming management model for hypertension represents an emerging medical practice that utilizes the individual's various risk factor profiles for primary care and personalized therapy decision-making in patients with hypertension. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200063550; https://www.chictr.org.cn/showproj.html?proj=175816.

Hierarchical Lotus-Seedpod-Derived Porous Activated Carbon Encapsulated with NiCo2S4 for a High-Performance All-Solid-State Asymmetric Supercapacitor.

Converting biowaste into carbon-based supercapacitor materials provides a new solution for high-performance and environmentally friendly energy storage applications. Herein, the hierarchical PAC/NiCo2S4 composite structure was fabricated through the combination of activation and sulfuration treatments. The PAC/NiCo2S4 electrode garnered advantages from its hierarchical structure and hollow architecture, resulting in a notable specific capacitance (1217.2 F g-1 at 1.25 A g-1) and superior cycling stability. Moreover, a novel all-solid-state asymmetric supercapacitor (ASC) was successfully constructed, utilizing PAC/NiCo2S4 as the cathode and PAC as the anode. The resultant device exhibited exceptionally high energy (49.7 Wh kg-1) and power density (4785.5 W kg-1), indicating the potential of this biomass-derived, hierarchical PAC/NiCo2S4 composite structure for employment in high-performance supercapacitors.

Size Distribution Control of Copper Nanoparticles and Oxides: Effect of Wet-Chemical Redox Cycling.

In this work, we studied the effect of liquid-phase redox cycling on the size of Cu nanoparticles and oxides. The mixed solution of sodium hydroxide and ammonium persulfate was applied as the oxidation system at room temperature, and ascorbic acid was used as reduction agent at 80 °C in the cycling process. It was found that pristine copper particles with average size of around 800 nm and wide distribution from 300 to 1300 nm could be turned into the resulting particles with the average size of around 162.3 nm with the distribution from 75 to 250 nm after 5 redox cycles. It was also observed that uniform copper oxide nanowires formed after 5 oxidation cycles could be easily reduced into fine copper nanoparticles. The critical tuning factors including the precursor size, morphology, defects, reaction time, and the way of adding oxidant were investigated. It was suggested that the synergetic driving effect of chemical reduction and nanostructure thermodynamic instability in solution accounted for the size reformation of the copper nanoparticles. This proposed method of size-shrinking could be developed as a general strategy for large-scale tuning the properties of copper nanoparticles for wide applications and extended to other metal particles as well.

Transmitting Pulse Encoding for Beyond-PRT Retransmitting Deception Jamming Detection in Spaceborne Synthetic Aperture Radar (SAR).

Retransmitting deception jamming (RDJ) degrades and misleads the Synthetic Aperture Radar (SAR) image interpretation by forming false targets. The beyond-Pulse Repetition Time (PRT) RDJ enlarges the effective jamming area without constraining the jammer location to reduce the spaceborne SAR working effectiveness. In order to detect the beyond-PRT RDJ and enhance the working efficiency in electronic countermeasure environment, the transmitting pulse encoding method for use in spaceborne SAR is proposed based on the geometry and signal models of beyond-PRT RDJ. Optimum binary codes with maximum number of detection windows are determined by the encoding procedure. The detected area is found to be proportional to the code length and the encoding efficiencies of even and odd codes are analyzed. The simulation results validate the effectiveness of the transmitting pulse encoding method for beyond-PRT RDJ detection in spaceborne SAR.

The Study on Single-Event Effects and Hardening Analysis of Frequency Divider Circuits Based on InP HBT Process.

The single-event effects (SEEs) of frequency divider circuits and the radiation tolerance of the hardened circuit are studied in this paper. Based on the experimental results of SEEs in InP HBTs, a transient current model for sensitive transistors is established, taking into account the influence of factors such as laser energy, base-collector junction voltage, and radiation position. Moreover, the SEEs of the (2:1) static frequency divider circuit with the InP DHBT process are simulated under different laser energies by adding the transient current model at sensitive nodes. The effect of the time relationship between the pulsed laser and clock signal are discussed. Changes in differential output voltage and the degradation mechanism of unhardened circuits are analyzed, which are mainly attributed to the cross-coupling effect between the transistors in the differential pair. Furthermore, the inverted output is directly connected to the input, leading to a feedback loop and causing significant logic upsets. Finally, an effective hardened method is proposed to provide redundancy and mitigate the impacts of SEEs on the divider. The simulation results demonstrate a notable improvement in the radiation tolerance of the divider.

Ice-Enabled Transfer of Graphene on Copper Substrates Enhanced by Electric Field and Cu2O.

Graphene films grown by the chemical vapor deposition (CVD) method suffer from contamination and damage during transfer. Herein, an innovative ice-enabled transfer method under an applied electric field and in the presence of Cu2O (or Cu2O-Electric-field Ice Transfer, abbreviated as CEIT) is developed. Ice serves as a pollution-free transfer medium while water molecules under the electric field fully wet the graphene surface for a bolstered adhesion force between the ice and graphene. Cu2O is used to reduce the adhesion force between graphene and copper. The combined methodology in CEIT ensures complete separation and clean transfer of graphene, resulting in successfully transferred graphene to various substrates, including polydimethylsiloxane (PDMS), Teflon, and C4F8 without pollution. The graphene obtained via CEIT is utilized to fabricate field-effect transistors with electrical performances comparable to that of intrinsic graphene characterized by small Dirac points and high carrier mobility. The carrier mobility of the transferred graphene reaches 9090 cm2 V-1 s-1, demonstrating a superior carrier mobility over that from other dry transfer methods. In a nutshell, the proposed clean and efficient transfer method holds great potential for future applications of graphene.

Modulation of wetting state switching of droplets on superhydrophobic microstructured surfaces by external electric field.

HYPOTHESIS: Electrowetting on conventional dielectrics requires direct fluid-electrode contact to generate strong electric fields at the three-phase contact line to modulate the wetting. Since the electric field alters wetting, the modulation of wetting can be achieved by applying an external electric field through insulated electrodes, preventing the liquid from contacting the electrodes. EXPERIMENT: A simple and efficient method for non-contact between the fluid and the electrode external electric field modulation of fluid wetting was proposed. The switching ability of droplets on microgroove surfaces from Cassie-Baxter to Wenzel wetting state under an external electric field was used to drive and quantify the relationship between wetting, contact angle, and the applied voltage. FINDINGS: Applying an external electric field modulates the wetting of deionized water, ionic liquids, and high-viscosity liquids on microgrooves. The wetting degree of liquid can be controlled by adjusting the external voltage parameters. The finite element simulations revealed that the Maxwell force drove this process. The effects of substrate size and liquid properties on wetting behavior were also examined. Post-application cross-sectional imaging showed the formation of a conformal interface, highlighting the relevance of the proposed method in advanced adaptive shape fabrication and microfluidic control, among other applications.

AdipoRon ameliorates the progression of heart failure with preserved ejection fraction via mitigating lipid accumulation and fibrosis.

INTRODUCTION: Obesity and imbalance in lipid homeostasis contribute greatly to heart failure with preserved ejection fraction (HFpEF), the dominant form of heart failure. Few effective therapies exist to control metabolic alterations and lipid homeostasis. OBJECTIVES: We aimed to investigate the cardioprotective roles of AdipoRon, the adiponectin receptor agonist, in regulating lipid accumulation in the two-hit HFpEF model. METHODS: HFpEF mouse model was induced using 60 % high-fat diet plus L-NAME drinking water. Then, AdipoRon (50 mg/kg) or vehicle were administered by gavage to the two-hit HFpEF mouse model once daily for 4 weeks. Cardiac function was evaluated using echocardiography, and Postmortem analysis included RNA-sequencing, untargeted metabolomics, transmission electron microscopy and molecular biology methods. RESULTS: Our study presents the pioneering evidence that AdipoR was downregulated and impaired fatty acid oxidation in the myocardia of HFpEF mice, which was associated with lipid metabolism as indicated by untargeted metabolomics. AdipoRon, orally active synthetic adiponectin receptor agonist, could upregulate AdipoR1/2 (independently of adiponectin) and reduce lipid droplet accumulation, and alleviate fibrosis to restore HFpEF phenotypes. Finally, AdipoRon primarily exerted its effects through restoring the balance of myocardial fatty acid intake, transport, and oxidation via the downstream AMPKα or PPARα signaling pathways. The protective effects of AdipoRon in HFpEF mice were reversed by compound C and GW6471, inhibitors of AMPKα and PPARα, respectively. CONCLUSIONS: AdipoRon ameliorated the HFpEF phenotype by promoting myocardial fatty acid oxidation, decreasing fatty acid transport, and inhibiting fibrosis via the upregulation of AdipoR and the activation of AdipoR1/AMPKα and AdipoR2/PPARα-related downstream pathways. These findings underscore the therapeutic potential of AdipoRon in HFpEF. Importantly, all these parameters get restored in the context of continued mechanical and metabolic stressors associated with HFpEF.

CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors.

Therapeutic agents targeting cytokine-cytokine receptor (CK-CKR) interactions lead to the disruption in cellular signaling and are effective in treating many diseases including tumors. However, a lack of universal and quick access to annotated structural surface regions on CK/CKR has limited the progress of a structure-driven approach in developing targeted macromolecular drugs and precision medicine therapeutics. Herein we develop CytoSIP (Single nucleotide polymorphisms (SNPs), Interface, and Phenotype), a rich internet application based on a database of atomic interactions around hotspots in experimentally determined CK/CKR structural complexes. CytoSIP contains: (1) SNPs on CK/CKR; (2) interactions involving CK/CKR domains, including CK/CKR interfaces, oligomeric interfaces, epitopes, or other drug targeting surfaces; and (3) diseases and phenotypes associated with CK/CKR or SNPs. The database framework introduces a unique tri-level SIP data model to bridge genetic variants (atomic level) to disease phenotypes (organism level) using protein structure (complexes) as an underlying framework (molecule level). Customized screening tools are implemented to retrieve relevant CK/CKR subset, which reduces the time and resources needed to interrogate large datasets involving CK/CKR surface hotspots and associated pathologies. CytoSIP portal is publicly accessible at https://CytoSIP.biocloud.top , facilitating the panoramic investigation of the context-dependent crosstalk between CK/CKR and the development of targeted therapeutic agents.

Fabrication of Hierarchical MOF-Derived NiCo2S4@Mo-Doped Co-LDH Arrays for High-Energy-Density Asymmetric Supercapacitors.

The rational fabrication of composite structures made of mixed components has shown great potential for boosting the energy density of supercapacitors. Herein, an elaborate hierarchical MOF-derived NiCo2S4@Mo-doped Co-LDH arrays hybrid electrode was fabricated through a step-wise method. By leveraging the synergistic effects of a uniform array of NiCo2S4 nanowires as the core and an MOF-derived porous shell, the NiCo2S4@Mo-doped Co-LDH hybrid electrode demonstrates an exceptional specific capacitance of 3049.3 F g-1 at 1 A g-1. Even at a higher current density of 20 A g-1, the capacitance remains high at 2458.8 F g-1. Moreover, the electrode exhibits remarkable cycling stability, with 91% of the initial capacitance maintained after 10,000 cycles at 10 A g-1. Additionally, the as-fabricated asymmetric supercapacitor (ASC) based on the NiCo2S4@Mo-doped Co-LDH electrode achieves an impressive energy density of 97.5 Wh kg-1 at a power density of 835.6 W kg-1. These findings provide a promising approach for the development of hybrid-structured electrodes, enabling the realization of high-energy-density asymmetric supercapacitors.

Constructing a Porous Structure on the Carbon Fiber Surface for Simultaneously Strengthening and Toughening the Interface of Composites.

The demand for both strength and toughness is perpetual in fiber-reinforced composites. Unfortunately, both properties are often mutually exclusive. As the mechanical properties of the composites are highly dependent on their interfacial properties, engineering interfaces between the fiber and matrix would be vital to overcome the conflict between strength and toughness. Herein, inspired by the physical interfacial architecture of grassroots-reinforced soil composites, a porous carbon nanotube-Mg(OH)2/MgO hybrid structure was constructed on the fiber surface via water electrolysis reaction and electrophoretic deposition process. The effects of the porous structure on the fiber filaments' mechanical properties, as well as the thickness on the interfacial properties, were all investigated. The results showed that fully covered porous structures on the fiber surface slightly enhanced the reliability of a single fiber in terms of mechanical properties by bridging the surface defects on the fiber. The interfacial shear strength and toughness of the porous structure-coated fiber/resin composite reached up to 92.3 MPa and 121.2 J/m2, respectively. These values were 61.30 and 121.98% higher than those of pristine fiber/resin composites, respectively. The strengthening effect was ascribed to the synergistic effects that improved numerous interfacial bonding areas and mechanical interlocking morphologies. The toughening mechanism was related to crack deflection, microcrack generation, and fracture of the porous structure during interfacial failure. Additional numerical studies by finite element analysis further proved the enhancement mechanism. Overall, the proposed method looks promising for producing advanced carbon fiber-reinforced polymer composites with excellent strength and toughness.

METTL14 regulates chromatin bivalent domains in mouse embryonic stem cells.

METTL14 (methyltransferase-like 14) is an RNA-binding protein that partners with METTL3 to mediate N6-methyladenosine (m6A) methylation. Recent studies identified a function for METTL3 in heterochromatin in mouse embryonic stem cells (mESCs), but the molecular function of METTL14 on chromatin in mESCs remains unclear. Here, we show that METTL14 specifically binds and regulates bivalent domains, which are marked by trimethylation of histone H3 lysine 27 (H3K27me3) and lysine 4 (H3K4me3). Knockout of Mettl14 results in decreased H3K27me3 but increased H3K4me3 levels, leading to increased transcription. We find that bivalent domain regulation by METTL14 is independent of METTL3 or m6A modification. METTL14 enhances H3K27me3 and reduces H3K4me3 by interacting with and probably recruiting the H3K27 methyltransferase polycomb repressive complex 2 (PRC2) and H3K4 demethylase KDM5B to chromatin. Our findings identify an METTL3-independent role of METTL14 in maintaining the integrity of bivalent domains in mESCs, thus indicating a mechanism of bivalent domain regulation in mammals.

HIPPO signaling-related signature for predicting prognosis and therapeutic response in gastric cancer.

Background: Gastric cancer (GC) is a multifactorial progressive disease with high mortality and heterogeneous prognosis. Effective prognostic biomarkers for GC were critically needed. Hippo signaling pathway is one of the critical mechanisms regulating the occurrence and development of GC, and has potential clinical application value for the prognosis and treatment of GC patients. However, there is no effective signature based on Hippo signaling pathway-related genes (HSPRGs) to predict the prognosis and treatment response of GC patients. Our study aimed to build a HSPRGs signature and explore its performance in improving prognostic assessment and drug therapeutic response in GC. Methods: Based on gene expression profiles obtained from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed HSPRGs and conducted univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a multigene risk signature. Subsequently, the Kaplan-Meier curve and receiver operating characteristic (ROC) were performed to evaluate the predictive value of the risk signature in both training and validation cohort. Furthermore, we carried out univariate and multivariate Cox regression analysis to investigate the independent prognostic factors and establish a predictive nomogram. The enriched signaling pathways in risk signature were analyzed by gene set enrichment analysis (GSEA). Tumor immune dysfunction and exclusion (TIDE) and drug sensitivity analysis were performed to depict therapeutic response in GC. Results: In total, 38 differentially expressed HSPRGs were identified, and final four genes (DLG3, TGFB3, TGFBR1, FZD6) were incorporated to build the signature. The ROC curve with average 1-, 3-, and 5-year areas under the curve (AUC) equal to .609, .634, and .639. Clinical ROC curve revealed that risk signature was superior to other clinicopathological factors in predicting prognosis. Calibration curves and C-index (.655) of nomogram showed excellent consistency. Besides, in the immunotherapy analysis, exclusion (p < 2.22 × 10-16) and microsatellite instability (p = .0058) performed significantly differences. Finally, our results suggested that patients in the high-risk group were more sensitive to specific chemotherapeutic agents. Conclusion: Results support the hypothesis that Hippo-related signature is a novel prognostic biomarker and predictor, which could help optimize GC prognostic stratification and inform clinical medication decisions.

Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN.

Thyroid nodule lesions are one of the most common lesions of the thyroid; the incidence rate has been the highest in the past thirty years. X-ray computed tomography (CT) plays an increasingly important role in the diagnosis of thyroid diseases. Nonetheless, as a result of the artifact and high complexity of thyroid CT image, the traditional machine learning method cannot be applied to CT image processing. In this paper, an end-to-end thyroid nodule automatic recognition and classification system is designed based on CNN. An improved Eff-Unet segmentation network is used to segment thyroid nodules as ROI. The image processing algorithm optimizes the ROI region and divides the nodules. A low-level and high-level feature fusion classification network CNN-F is proposed to classify the benign and malignant nodules. After each module is connected in series with the algorithm, the automatic classification of each nodule can be realized. Experimental results demonstrate that the proposed end-to-end thyroid nodule automatic recognition and classification system has excellent performance in diagnosing thyroid diseases. In the test set, the segmentation IOU reaches 0.855, and the classification output accuracy reaches 85.92%.

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning.

Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, the early diagnosis of burns lacks accuracy and difference. Therefore, we use deep learning technology to automate and standardize burn diagnosis to reduce human errors and improve burn diagnosis. First, the burn dataset with detailed burn area segmentation and burn depth labelling is created. Then, an end-to-end framework based on deep learning method for advanced burn area segmentation and burn depth diagnosis is proposed. The framework is firstly used to segment the burn area in the burn images. On this basis, the calculation of the percentage of the burn area in the total body surface area (TBSA) can be realized by extending the network output structure and the labels of the burn dataset. Then, the framework is used to segment multiple burn depth areas. Finally, the network achieves the best result with IOU of 0.8467 for the segmentation of burn and no burn area. And for multiple burn depth areas segmentation, the best average IOU is 0.5144.

Molecular Understanding of Electrochemical-Mechanical Responses in Carbon-Coated Silicon Nanotubes during Lithiation.

Carbon-coated silicon nanotube (SiNT@CNT) anodes show tremendous potential in high-performance lithium ion batteries (LIBs). Unfortunately, to realize the commercial application, it is still required to further optimize the structural design for better durability and safety. Here, the electrochemical and mechanical evolution in lithiated SiNT@CNT nanohybrids are investigated using large-scale atomistic simulations. More importantly, the lithiation responses of SiNW@CNT nanohybrids are also investigated in the same simulation conditions as references. The simulations quantitatively reveal that the inner hole of the SiNT alleviates the compressive stress concentration between a-LixSi and C phases, resulting in the SiNT@CNT having a higher Li capacity and faster lithiation rate than SiNW@CNT. The contact mode significantly regulates the stress distribution at the inner hole surface, further affecting the morphological evolution and structural stability. The inner hole of bare SiNT shows good structural stability due to no stress concentration, while that of concentric SiNT@CNT undergoes dramatic shrinkage due to compressive stress concentration, and that of eccentric SiNT@CNT is deformed due to the mismatch of stress distribution. These findings not only enrich the atomic understanding of the electrochemical-mechanical coupled mechanism in lithiated SiNT@CNT nanohybrids but also provide feasible solutions to optimize the charging strategy and tune the nanostructure of SiNT-based electrode materials.

Detection of Snore from OSAHS Patients Based on Deep Learning.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

Hybrid Model Structure for Diabetic Retinopathy Classification.

Diabetic retinopathy (DR) is one of the most common complications of diabetes and the main cause of blindness. The progression of the disease can be prevented by early diagnosis of DR. Due to differences in the distribution of medical conditions and low labor efficiency, the best time for diagnosis and treatment was missed, which results in impaired vision. Using neural network models to classify and diagnose DR can improve efficiency and reduce costs. In this work, an improved loss function and three hybrid model structures Hybrid-a, Hybrid-f, and Hybrid-c were proposed to improve the performance of DR classification models. EfficientNetB4, EfficientNetB5, NASNetLarge, Xception, and InceptionResNetV2 CNNs were chosen as the basic models. These basic models were trained using enhance cross-entropy loss and cross-entropy loss, respectively. The output of the basic models was used to train the hybrid model structures. Experiments showed that enhance cross-entropy loss can effectively accelerate the training process of the basic models and improve the performance of the models under various evaluation metrics. The proposed hybrid model structures can also improve DR classification performance. Compared with the best-performing results in the basic models, the accuracy of DR classification was improved from 85.44% to 86.34%, the sensitivity was improved from 98.48% to 98.77%, the specificity was improved from 71.82% to 74.76%, the precision was improved from 90.27% to 91.37%, and the F1 score was improved from 93.62% to 93.9% by using hybrid model structures.

Three-dimensional MoS2/Graphene Aerogel as Binder-free Electrode for Li-ion Battery.

Hybrid MoS2/reduced graphene aerogels with rich micro-pore are fabricated through a hydrothermal method, followed by freeze-drying and annealing treatment. The porous structure could act as an electrode directly, free of binder and conductive agent, which promotes an improved electron transfer, and provides a 3D network for an enhanced ion transport, thus leading to an increased capacity and stable long cycle stability performance. Notably, the specific capacity of MoS2/reduced graphene aerogel is 1041 mA h g-1 at 100 mA g-1. Moreover, reversible capacities of 667 mA h g-1 with 58.6% capacity retention are kept after 100 cycles. The outstanding performance is beneficial from the synergistic effect of the MoS2 nanostructure and graphene conductive network, as well as the binder-free design. These results provide a route to integrate transition-metal-dichalcogenides with graphene to fabricate composites with rich micro-pores and a three-dimensional network for energy storage devices.