Anomalous layer-dependent photoluminescence spectra of supertwisted spiral WS2.

Twisted stacking of two-dimensional materials with broken inversion symmetry, such as spiral MoTe2 nanopyramids and supertwisted spiral WS2, emerge extremely strong second- and third-harmonic generation. Unlike well-studied nonlinear optical effects in these newly synthesized layered materials, photoluminescence (PL) spectra and exciton information involving their optoelectronic applications remain unknown. Here, we report layer- and power-dependent PL spectra of the supertwisted spiral WS2. The anomalous layer-dependent PL evolutions that PL intensity almost linearly increases with the rise of layer thickness have been determined. Furthermore, from the power-dependent spectra, we find the power exponents of the supertwisted spiral WS2 are smaller than 1, while those of the conventional multilayer WS2 are bigger than 1. These two abnormal phenomena indicate the enlarged interlayer spacing and the decoupling interlayer interaction in the supertwisted spiral WS2. These observations provide insight into PL features in the supertwisted spiral materials and may pave the way for further optoelectronic devices based on the twisted stacking materials.

Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.

SGLFormer: Spiking Global-Local-Fusion Transformer with high performance.

Introduction: Spiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance. Methods: Recognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features. Results: SGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%. Discussion: With its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer.

Mesoporous and Encapsulated In2O3/Ti3C2Tx Schottky Heterojunctions for Rapid and ppb-Level NO2 Detection at Room Temperature.

Rapid and ultrasensitive detection of toxic gases at room temperature is highly desired in health protection but presents grand challenges in the sensing materials reported so far. Here, we present a gas sensor based on novel zero dimensional (0D)/two dimensional (2D) indium oxide (In2O3)/titanium carbide (Ti3C2Tx) Schottky heterostructures with a high surface area and rich oxygen vacancies for parts per billion (ppb) level nitrogen dioxide (NO2) detection at room temperature. The In2O3/Ti3C2Tx gas sensor exhibits a fast response time (4 s), good response (193.45% to 250 ppb NO2), high selectivity, and excellent cycling stability. The rich surface oxygen vacancies play the role of active sites for the adsorption of NO2 molecules, and the Schottky junctions effectively adjust the charge-transfer behavior through the conduction tunnel in the sensing material. Furthermore, In2O3 nanoparticles almost fully cover the Ti3C2Tx nanosheets which can avoid the oxidation of Ti3C2Tx, thus contributing to the good cycling stability of the sensing materials. This work sheds light on the sensing mechanism of heterojunction nanostructures and provides an efficient pathway to construct high-performance gas sensors through the rational design of active sites.

Biomimetic Hyaluronic Acid-Based Brush Polymers Modulate Chondrocyte Homeostasis via ROS/Ca2+/TRPV4.

Bionic mimics using natural cartilage matrix molecules can modulate the corresponding metabolic activity by improving the microenvironment of chondrocytes. A bionic brush polymer, HA/PX, has been found to reverse the loss of cartilage extracellular matrix (ECM) and has promising applications in the clinical treatment of osteoarthritis (OA). However, the unknown bioremediation mechanism of HA/PX severely hinders its clinical translation. In OA, the massive loss of the ECM may be attributed to a decrease in transient receptor potential vanilloid 4 (TRPV4) activity, which affects reactive oxygen species (ROS) clearance and [Ca2+]i signaling, initiating downstream catabolic pathways. In this study, we investigated the bioremediation mechanism of HA/PX in a model of interleukin 1ß (IL-1ß)-induced inflammation. Through TRPV4, HA/PX reduced ROS accumulation in chondrocytes and enhanced [Ca2+]i signaling, reflecting a short-term protection capacity for chondrocytes. In addition, HA/PX balanced the metabolic homeostasis of chondrocytes via TRPV4, including promoting the secretion of type II collagen (Col-II) and aggrecan, the major components of the ECM, and reducing the expression of matrix metal-degrading enzyme (MMP-13), exerting long-term protective effects on chondrocytes. Molecular dynamics (MD) simulations showed that HA/PX could act as a TRPV4 activator. Our results suggest that HA/PX can regulate chondrocyte homeostasis via ROS/Ca2+/TRPV4, thereby improving cartilage regeneration. Because the ECM is a prevalent feature of various cell types, HA/PX holds promising potential for improving regeneration and disease modification for not only cartilage-related healthcare but many other tissues and diseases.

Tree-Structured Data Clustering-Driven Neural Network for Intra Prediction in Video Coding.

Intra prediction is a crucial part of video compression, which utilizes local information in images to eliminate spatial redundancy. As the state-of-the-art video coding standard, Versatile Video Coding (H.266/VVC) employs multiple directional prediction modes in intra prediction to find the texture trend of local areas. Then the prediction is made based on reference samples in the selected direction. Recently, neural network-based intra prediction has achieved great success. Deep network models are trained and applied to assist the HEVC and VVC intra modes. In this paper, we propose a novel tree-structured data clustering-driven neural network (dubbed TreeNet) for intra prediction, which builds the networks and clusters the training data in a tree-structured manner. Specifically, in each network split and training process of TreeNet, every parent network on a leaf node is split into two child networks by adding or subtracting Gaussian random noise. Then data clustering-driven training is applied to train the two derived child networks using the clustered training data of their parent. On the one hand, the networks at the same level in TreeNet are trained with non-overlapping clustered datasets, and thus they can learn different prediction abilities. On the other hand, the networks at different levels are trained with hierarchically clustered datasets, and thus they will have different generalization abilities. TreeNet is integrated into VVC to assist or replace intra prediction modes to test its performance. In addition, a fast termination strategy is proposed to accelerate the search of TreeNet. The experimental results demonstrate that when TreeNet is used to assist the VVC Intra modes, TreeNet with depth = 3 can bring an average of 3.78% bitrate saving (up to 8.12%) over VTM-17.0. If TreeNet with the same depth replaces all VVC intra modes, an average of 1.59% bitrate saving can be reached.

Multiscale Attention Fusion for Depth Map Super-Resolution Generative Adversarial Networks.

Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue. To solve this problem, inspired by the recent excellent results achieved in color image super-resolution by generative adversarial networks, we propose a depth map super-resolution framework with generative adversarial networks using multiscale attention fusion. Fusion of the color features and depth features at the same scale under the hierarchical fusion attention module effectively measure the guiding effect of the color image on the depth map. The fusion of joint color-depth features at different scales balances the impact of different scale features on the super-resolution of the depth map. The loss function of a generator composed of content loss, adversarial loss, and edge loss helps restore clearer edges of the depth map. Experimental results on different types of benchmark depth map datasets show that the proposed multiscale attention fusion based depth map super-resolution framework has significant subjective and objective improvements over the latest algorithms, verifying the validity and generalization ability of the model.

Ratiometric photoacoustic imaging of endogenous HNO in vivo for assessing prodrug release and liver injury.

The effective imaging of endogenous HNO is highly crucial for pathology research and medical development due to its important pharmacological activity in biological systems. Here, a ratiometric photoacoustic probe in response to HNO was rationally developed to effectively assess HNO prodrug release and liver injury in vivo.

Detection of oxytetracycline in milk using a novel carbon dots-based fluorescence probe via facile pyrolysis synthesis.

Amphiphilic blue-fluorescence carbon dots (B-CDs) were synthesized via pyrolysis method with citric acid and oleamine as precursors. B-CDs are monodispersed in ethanol, toluene, and ultrapure water with the average particle sizes of 3.33 nm, 2.05 nm, and 4.12 nm, respectively. The maximum emission wavelength of the B-CDs excitation at 370 nm is located at 459 nm. The B-CDs have good optical properties with excellent photostability. The fluorescence quantum yield (QY) of the as-prepared CDs is as high as 30.17%. The fluorescence of B-CDs is quenched because of static quenching by oxytetracycline. A high selective and sensitive fluorescence probe for detecting oxytetracycline was constructed with a linear range of 1.52-27.60 µg/mL and the detection limit of 0.33 µg/mL. The B-CDs-based fluorescence probe can be applied to analyze oxytetracycline in milk; the recoveries and relative standard are satisfactory. Furthermore, the B-CDs were exploited for imaging of SH-SY5Y cells. The results demonstrate that as-synthesized CDs can serve as a cellular imaging reagent owing to remarkable bioimaging performance. This work provides a new strategy for the detection of oxytetracycline in food.

Competition mechanism of exciton decay channels in the stacked multilayer tungsten sulfide.

The competition mechanism of exciton decay channels in the multilayer TMDs remains poorly understood. Here, the exciton dynamics in the stacked WS2 was studied. The exciton decay processes are divided into the fast and slow decay processes, which are dominated by the exciton-exciton annihilation (EEA) and defect-assisted recombination (DAR), respectively. The lifetime of EEA is on the order of hundreds of femtoseconds (400∼1100 fs). It is decreased initially, followed by an increase with adding layer thickness, which can be attributed to the competition between phonon-assisted effect and defect effect. The lifetime of DAR is on the timescale of hundreds of picoseconds (200∼800 ps), which is determined by the defect density especially in a high injected carrier density.

Designing a Brightness-Restored Rhodamine Derivative by the Ortho-Compensation Effect for Assessing Drug-Induced Acute Kidney Injury.

Effective monitoring of essential bioindicators with high-contrast fluorescence imaging is highly crucial to reveal the pathological progression of diseases. However, most reported probes based on asymmetric amino-rhodamine (ARh) derivatives are often limited in practical application due to the low signal-to-noise ratios. Herein, a new fluorophore, 3-methoxy-amino-rhodamine (3-MeOARh), with improved fluorescence quantum yield (0.51 in EtOH) is designed and synthesized by introducing methoxy group in the ortho-position of amino in asymmetric amino-rhodamine. Notably, the good properties of the ortho-compensation effect further effectively enable the construction of an activatable probe with a high signal-to-noise ratio. As a proof of concept, the probe (3-MeOARh-NTR) was successfully synthesized for nitroreductase detection with high selectivity, excellent sensitivity, and good stability. More importantly, the relationship between drug-induced kidney hypoxia and elevated nitroreductase concentration was first uncovered in living tissues through high-contrast imaging. Therefore, the study presents the activatable probe for kidney hypoxia imaging while highlighting the 3-MeOARh structure with a satisfactory signal-to-noise ratio. It is believed that 3-MeOARh can serve as an efficient platform for activatable probe construction to reveal the pathological progression of different diseases.

Aggrephagy-related LncRNAs index: A predictor for HCC prognosis, immunotherapy efficacy, and chemosensitivity.

BACKGROUND: Due to the complexity and heterogeneity of hepatocellular carcinoma, the existing clinical staging criterias are insufficient to accurately reflect the tumor microenvironment and predict the prognosis of HCC patients. Aggrephagy, as a type of selective autophagy, is associated with various phenotypes of malignant tumors. OBJECTIVE: This study aimed to identify and validate a prognostic model based on aggrephagy-related LncRNAs to assess the prognosis and immunotherapeutic response of HCC patients. METHODS: Based on the TCGA-LIHC cohort, aggrephagy-related LncRNAs were identified. Univariate Cox regression analysis and lasso and multivariate Cox regression were used to construct a risk-scoring system based on eight ARLs. CIBERSORT, ssGSEA, and other algorithms were used to evaluate and present the immune landscape of tumor microenvironment. RESULTS: The high-risk group had a worse overall survival (OS) than the low-risk group. Patients in the high-risk group are more likely to benefit from immunotherapy because of their high infiltration level and high immune checkpoint expression. CONCLUSION: The ARLs signature is a powerful predictor of prognosis for HCC patients, and the nomogram based on this model can help clinicians accurately determine the prognosis of HCC patients and screen for specific subgroups of patients who are more sensitive to immunotherapy and chemotherapy.

Midline catheter tip position and catheter-related complications in antimicrobial therapy: A multi-center randomized controlled trial.

BACKGROUND: The use of midline catheters (MCs) in intravenous therapy has increased over the last few years; however, scientific evidence is scarce. The recommendations for its specific tip position and safe use in antimicrobial therapy are not well established, which increases the risk of catheter-related complications. OBJECTIVE: This study aimed to provide evidence for selecting MC tip positions for safe use in antimicrobial therapy. DESIGN: This prospective, randomized controlled trial compared catheter-related complications with different tip positions. Participants were assigned to three different catheter tip groups, and the relationship between the tip position and catheter-related complications was observed during antimicrobial therapy. SETTING: Multicenter trial based in intravenous therapy centers at six Chinese hospitals. PARTICIPANTS: A fixed-point continuous convenience sampling method was used to enroll 330 participants. Three different study groups with equal numbers of participants (n = 110) were established using a randomization technique. METHODS: The incidence of catheter-related complications and catheter retention time was compared among the three groups. The catheter measurement data between the three groups were compared using one-way ANOVA or the Kruskal-Wallis tests. Counting data were compared using chi-square tests, Fisher's exact tests, and Kruskal-Wallis tests. Post-hoc tests were conducted to compare the incidence of complications among the three groups. We followed a time-to-event analysis approach and used Kaplan-Meier curves and log-rank tests to analyze the relationship between catheter-related complications and different tip positions. RESULTS: The total incidence of catheter-related complications in Experimental Groups 1 and 2 as well as the control group were 10.09%, 17.98%, and 33.73%, respectively. Statistically significant differences existed between the groups (p < 0.0001). In pairwise comparisons of the three groups, significant differences were evident in the incidence of complications between Experimental Group 1 and the control group (RD 19.40%, confidence interval 7.71-31.09). No statistical significance in the incidence of complications between Experimental Group 1 and Experiment Group 2 (RD -4.93%, confidence interval -14.80-4.95) and in the incidence of complications between Experimental Group 2 and the control group (RD 14.47%, confidence interval 1.82-27.12) were noted. CONCLUSION: Catheter-related complications were reduced when the tip of the Midline Catheter was located in the subclavian or axillary vein of the chest wall. TRIAL REGISTRATION: NCT04601597(https://clinicaltrials.gov/ct2/show/NCT04601597). Registration date: September 1, 2020.

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums.

Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex "black box" structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.

Recent Progress in Hydrogel-Based Synthetic Cartilage: Focus on Lubrication and Load-Bearing Capacities.

Articular cartilage (AC), which covers the ends of bones in joints, particularly the knee joints, provides a robust interface to maintain frictionless movement during daily life due to its remarkable lubricating and load-bearing capacities. However, osteoarthritis (OA), characterized by the progressive degradation of AC, compromises the properties of AC and thus leads to frayed and rough interfaces between the bones, which subsequently accelerates the progression of OA. Hydrogels, composed of highly hydrated and interconnected polymer chains, are potential candidates for AC replacement due to their physical and chemical properties being similar to those of AC. In this review, we summarize the recent progress of hydrogel-based synthetic cartilage, or cartilage-like hydrogels, with a particular focus on their lubrication and load-bearing properties. The different formulations, current limitations, and challenges of such hydrogels are also discussed. Moreover, we discuss the future directions of hydrogel-based synthetic cartilage to repair and even regenerate the damaged AC.

De Novo Design of Activatable Photoacoustic/Fluorescent Probes for Imaging Acute Lung Injury In Vivo.

Effective monitoring of the physiological progression of acute lung injury (ALI) in real time is crucial for early theranostics to reduce its high mortality. In particular, activatable fluorescence and photoacoustic molecule probes have attracted attention to assess ALI by detecting related indicators. However, the existing fluorophores often encounter issues of low retention in the lungs and slow clearance from the body, which compromise the probe's actual capability for in situ imaging by intravenous injection in vivo. Herein, a novel near-infrared hemicyanines fluorophore (FJH) bearing a quaternary ammonium group was first developed by combining with the rational design and screening strategy. The properties of good hydrophilicity and blood circulation effectively enable FJH accumulation for lung imaging. Inspired by the high retention efficiency, the probe FJH-C that turns on fluorescence and photoacoustic signals in response to the ALI indicator (esterase) was subsequently synthesized. Notably, the probe FJH-C successfully achieved the selectivity and sensitivity toward esterase in vitro and in living cells. More importantly, FJH-C can be further used to assess lipopolysaccharides and silica-induced ALI through the desired fluo-photoacoustic signal. Therefore, this study not only shows the first activatable probe for real-time imaging of lung function but also highlights the fluorophore structure with high lung retention. It is believed that FJH and FJH-C can serve as an efficient platform to reveal the pathological progression of other lung diseases for early diagnosis and medical intervention.

Strain induced magnetic hysteresis in MoS2 and WS2 monolayers with symmetric double sulfur vacancy defects.

It has been found that magnetism in two-dimensional (2D) transition metal dichalcogenides can be realized by properly introducing vacancies and applying strain. However, no work has clearly clarified the modulation of such 2D magnetism under a sweeping strain. Thus we were motivated in this work to investigate the mechanical and electronic properties of the monolayer MS2 (M = Mo, W) with symmetric S vacancy defects under sweeping strain. The results show that the local structure of the M atoms in MS2 around the defect undergoes a reversible phase transition from a triangular shape (Tri-3M) with short M-M bonds, to a circular one (Cir-6M-12S) with larger M-M bonds as the planar strain increases. The critical tensile strain for the transition from Tri-3M to Cir-6M-12S are 12.53% for MoS2 and 11.46% for WS2, while the critical compressive strain for the reversal from Cir-6M-12S to Tri-3M are -3.60% and -2.16%, respectively. In particular, we find that the magnetism can be continuously modulated and undergoes a hysteresis loop behavior under the sweeping strains, with the residual magnetism being 2 µB. Our work theoretically predicts the promising prospect for exploring low-dimensional semiconductor spintronic devices working without applying a magnetic field.

ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides.

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

Engineering a Ratiometric Photoacoustic Probe with a Hepatocyte-Specific Targeting Ability for Liver Injury Imaging.

In situ imaging of biological indicators is imperative for pathological research by utilizing an activatable photoacoustic (PA) probe. However, precise imaging in actual applications is hampered by the inevitable poor accumulation and low sensitivity. Herein, an amphiphilic molecular probe (AP) was rationally constructed as proof of concept for in situ imaging of drug-induced liver injury, which consists of a hydrophilic target unit and a superoxide anion radical (O2•-)-sensitive small-molecule PA moiety. The probe AP successfully realizes the selectivity and sensitivity toward O2•- in vitro and in living cells. Notably, the amphiphilic probe AP can be selectively retained in the liver and activated toward endogenous O2•- through receptor-mediated endocytosis inside hepatocytes. By virtue of the highly efficient accumulation at the liver, AP was further applied to assess isoniazid-induced liver injury through desired ratiometric PA signals. In addition, based on the fluctuation of O2•-, the therapeutic efficacy of hepatoprotective medicines for hepatotoxicity was analyzed in vivo. Therefore, the O2•--specific probe could serve as a promising molecular tool for early diagnosis study of other liver diseases and analysis of new potential therapeutic agents.

Lossless Recompression of JPEG Images Using Transform Domain Intra Prediction.

JPEG, which was developed 30 years ago, is the most widely used image coding format, especially favored by the resource-deficient devices, due to its simplicity and efficiency. With the evolution of the Internet and the popularity of mobile devices, a huge amount of user-generated JPEG images are uploaded to social media sites like Facebook and Flickr or stored in personal computers or notebooks, which leads to an increase in storage cost. However, the performance of JPEG is far from the state-of-art coding methods. Therefore, the lossless recompression of JPEG images is urgent to be studied, which will further reduce the storage cost while maintaining the image fidelity. In this paper, a hybrid coding framework for the lossless recompression of JPEG images (LLJPEG) using transform domain intra prediction is proposed, including block partition and intra prediction, transform and quantization, and entropy coding. Specifically, in LLJPEG, intra prediction is first used to obtain a predicted block. Then the predicted block is transformed by DCT and then quantized to obtain the predicted coefficients. After that, the predicted coefficients are subtracted from the original coefficients to get the DCT coefficient residuals. Finally, the DCT residuals are entropy coded. In LLJPEG, some new coding tools are proposed for intra prediction and the entropy coding is redesigned. The experiments show that LLJPEG can reduce the storage space by 29.43% and 26.40% on the Kodak and DIV2K datasets respectively without any loss for JPEG images, while maintaining low decoding complexity.