Andrias davidianus bone peptides alleviates hyperuricemia-induced kidney damage in vitro and in vivo

Hyperuricemia(HUA)is a vital risk factor for chronic kidney diseases(CKD)and development of functional foods capable of protecting CKD is of importance.This paper aimed to explore the amelioration effects and mechanism of Andrias davidianus bone peptides(ADBP)on HUA-induced kidney damage.In the present study,we generated the standard ADBP which contained high hydrophobic amino acid and low molecular peptide contents.In vitro results found that ADBP protected uric acid(UA)-induced HK-2 cells from damage by modulating urate transporters and antioxidant defense.In vivo results indicated that ADBP effectively ameliorated renal injury in HUA-induced CKD mice,evidenced by a remarkable decrease in serum UA,creatinine and blood urea nitrogen,improving kidney UA excretion,antioxidant defense and histological kidney deterioration.Metabolomic analysis highlighted 14 metabolites that could be selected as potential biomarkers and attributed to the amelioration effects of ADBP on CKD mice kidney dysfunction.Intriguingly,ADBP restored the gut microbiome homeostasis in CKD mice,especially with respect to the elevated helpful microbial abundance,and the decreased harmful bacterial abundance.This study demonstrated that ADBP displayed great nephroprotective effects,and has great promise as a food or functional food ingredient for the prevention and treatment of HUA-induced CKD.

Label-free in-vivo classi-cation and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment,which renders it a valuable tool for both scientific research and clinical applications.However,the conventional approach for improving classification accuracy often involves labeling cells with fluorescence,which can lead to potential phototoxicity.This study proposes a label-free in-vivo flow cytometry technique,called dynamic YOLOv4(D-YOLOv4),which improves classification accuracy by integrating absorption intensity fluctuation modulation(AIFM)into YOLOv4 to demodulate the temporal features of moving red blood cells(RBCs)and platelets.Using zebrafish as an experimental model,the D-YOLOv4 method achieved average precisions(APs)of 0.90 for RBCs and 0.64 for thrombocytes(similar to platelets in mammals),resulting in an overall AP of 0.77.These scores notably surpass those attained by alternative network models,thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivoflow cytometry,which holds promise for diverse in-vivo cell classification applications.

Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity

Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.

An aqueous magnesium-ion hybrid supercapacitor operated at-50℃

The recent advances in aqueous magnesium-ion hybrid supercapacitor(MHSC)have attracted great attention as it brings together the benefits of high energy density,high power density,and synchronously addresses cost and safety issues.However,the freeze of aqueous electrolytes discourages aqueous MHSC from operating at low-temperature conditions.Here,a low-concentration aqueous solution of 4 mol L^(-1) Mg(ClO_(4))_(2) is devised for its low freezing point(-67℃)and ultra-high ionic conductivity(3.37 mS cm^(-1) at-50℃).Both physical characterizations and computational simulations revealed that the Mg(ClO_(4))_(2) can effectively disrupt the original hydrogen bond network among water molecules via transmuting the electrolyte structure,thus yielding a low freezing point.Thus,the Mg(ClO_(4))_(2) electrolytes endue aqueous MHSC with a wider temperature operation range(-50℃–25℃)and a higher energy density of 103.9 Wh kg^(-1) at 3.68 kW kg^(-1) over commonly used magnesium salts(i.e.,MgSO_(4) and Mg(NO_(3))_(2))electrolytes.Furthermore,a quasi-solid-state MHSC based on polyacrylamide-based hydrogel electrolyte holds superior low-temperature performance,excellentflexibility,and high safety.This work pioneers a convenient,cheap,and eco-friendly tactic to procure low-temperature aqueous magnesium-ion energy storage device.

Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging problem.To this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video clips.The authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual inputs.The outputs of audio and visual branches are combined in a decision fusion module.As humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified keyword.Moreover,the position of the keyword is localised in the attention map without additional position labels.Exper-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy conditions.The code is available at https://github.com/jialeren/AVKT.

Recent progress about transmission electron microscopy characterizations on lithium-ion batteries

With the rapid development of portable electronics,new energy vehicles,and smart grids,ion batteries are becoming one of the most widely used energy storage devices,while the safety concern of ion batteries has always been an urgent problem to be solved.To develop a safety-guaranteed battery,the characterization of the internal structure is indispensable,where electron microscopy plays a crucial role.Based on this,this paper summarizes the application of transmission electron microscopy(TEM)in battery safety,further concludes and analyzes the aspects of dendrite growth and solid electrolyte interface(SEI)formation that affect the safety of ion batteries,and emphasizes the importance of electron microscopy in battery safety research and the potential of these techniques to promote the future development of this field.These advanced electron microscopy techniques and their prospects are also discussed.

A large-scale cold plasma jet: generation mechanism and application effect

Atmospheric pressure cold plasma jets(APCPJs) typically exhibit a slender, conical structure,which imposes limitations on their application for surface modification due to the restricted treatment area. In this paper, we introduce a novel plasma jet morphology known as the large-scale cold plasma jet(LSCPJ), characterized by the presence of both a central conical plasma jet and a peripheral trumpet-like diffuse plasma jet. The experimental investigations have identified the factors influencing the conical and the trumpet-like diffuse plasma jet, and theoretical simulations have shed light on the role of the flow field and the electric field in shaping the formation of the LSCPJ. It is proved that, under conditions of elevated helium concentration, the distributions of impurity gas particles and the electric field jointly determine the plasma jet’s morphology. High-speed ICCD camera images confirm the dynamic behavior of plasma bullets in LSCPJ, which is consistent with the theoretical analysis. Finally, it is demonstrated that when applied to the surface treatment of silicone rubber, LSCPJ can achieve a treatment area over 28 times larger than that of APCPJ under equivalent conditions. This paper uncovers the crucial role of impurity gases and electric fields in shaping plasma jet morphology and opens up the possibility of efficiently diversifying plasma jet generation effects through external electromagnetic fields. These insights hold the promise of reducing the generation cost of plasma jets and expanding their applications across various industrial sectors.

Multiple-dimensioned defect engineering for graphite felt electrode of vanadium redox flow battery

The scarcity of wettability,insufficient active sites,and low surface area of graphite felt(GF)have long been suppressing the performance of vanadium redox flow batteries(VRFBs).Herein,an ultra-homogeneous multipledimensioned defect,including nano-scale etching and atomic-scale N,O codoping,was used to modify GF by the molten salt system.NH_(4)Cl and KClO_(3) were added simultaneously to the system to obtain porous N/O co-doped electrode(GF/ON),where KClO_(3) was used to ultra-homogeneously etch,and O-functionalize electrode,and NH4Cl was used as N dopant,respectively.GF/ON presents better electrochemical catalysis for VO_(2)+/VO_(2)+ and V3+/V2+ reactions than only O-functionalized electrodes(GF/O)and GF.The enhanced electrochemical properties are attributed to an increase in active sites,surface area,and wettability,as well as the synergistic effect of N and O,which is also supported by the density functional theory calculations.Further,the cell using GF/ON shows higher discharge capacity,energy efficiency,and stability for cycling performance than the pristine cell at 140 mA cm^(−2) for 200 cycles.Moreover,the energy efficiency of the modified cell is increased by 9.7% from 55.2% for the pristine cell at 260 mA cm^(−2).Such an ultra-homogeneous etching with N and O co-doping through“boiling”molten salt medium provides an effective and practical application potential way to prepare superior electrodes for VRFB.

Call for knowledge systematization:An example from lithium ion batteries recycling

Scientific research is one of the primary ways to drive human civilization forward.Countless research institutions and researchers worldwide are dedicated to exploring the unknown,resulting in a flood of research findings[1].For instance,Web of Science alone collects over 10,000 academic papers on a daily basis.While each research field typically has around 10%of review articles,these mainly focus on a limited portion of the subject,failing to provide a comprehensive and real-time updated overview.As a result,researchers or students in a particular field may spend several months,or even longer,comprehending the state of research that has developed over just a few years.The lack of systematic background often leads to redundant or similar studies,which is highly detrimental to the progress of science and technology.

Incombustible solid polymer electrolytes:A critical review and perspective

Since the advent of the solid-state batteries,employing solid polymer electrolytes(SPEs)to replace routine flammable liquid electrolytes is regarded to be one of the most promising solutions in pursing highenergy-density battery systems.SPEs with superior thermal stability,good processability,and high mechanical modulus obtain increasing attentions.However,SPE-based batteries are not impenetrable due to their decomposition and combustibility under extreme conditions.Researchers believe incorporating appropriate flame-retardant additives/solvents/fragments into SPEs can intrinsically reduce their flammability to solve the battery safety issues.In this review,the recent research progress of incombustible SPEs,with special emphasis on flame-retardant structural design,is summarized.Specifically,a brief introduction of flame-retardant mechanism,evaluation index for safety of SPEs,and a detailed overview of the latest advances on diverse-types SPEs in various battery systems are highlighted.The deep insight into thermal ru naway process,the free-standing incombustible GPEs,and the ratio nal design of pouch cell structures may be the main directions to motivate revolutionary next-generation for safety batteries.

Robust blind image watermarking based on interest points

Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.

Underwater image clarifying based on human visual colour constancy using double-opponency

Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water.Such images with degradation cannot meet the needs of underwater operations.The main problem in classic underwater image restoration or enhancement methods is that they consume long calcu-lation time,and often,the colour or contrast of the result images is still unsatisfied.Instead of using the complicated physical model of underwater imaging degradation,we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double-opponency.Firstly,the original image is converted to the LMS space.Then the signals are linearly combined,and Gaussian convolutions are per-formed to imitate the function of receptive fields(RFs).Next,two RFs with different sizes work together to constitute the double-opponency response.Finally,the underwater light is estimated to correct the colours in the image.Further contrast stretching on the luminance is optional.Experiments show that the proposed method can obtain clarified underwater images with higher quality than before,and it spends significantly less time cost compared to other previously published typical methods.

Precise region semantics-assisted GAN for pose-guided person image generation

Generating a realistic person's image from one source pose conditioned on another different target pose is a promising computer vision task.The previous mainstream methods mainly focus on exploring the transformation relationship between the keypoint-based source pose and the target pose,but rarely investigate the region-based human semantic information.Some current methods that adopt the parsing map neither consider the precise local pose-semantic matching issues nor the correspondence between two different poses.In this study,a Region Semantics-Assisted Generative Adversarial Network(RSA-GAN)is proposed for the pose-guided person image gen-eration task.In particular,a regional pose-guided semantic fusion module is first devel-oped to solve the imprecise match issue between the semantic parsing map from a certain source image and the corresponding keypoints in the source pose.To well align the style of the human in the source image with the target pose,a pose correspondence guided style injection module is designed to learn the correspondence between the source pose and the target pose.In addition,one gated depth-wise convolutional cross-attention based style integration module is proposed to distribute the well-aligned coarse style information together with the precisely matched pose-guided semantic information to-wards the target pose.The experimental results indicate that the proposed RSA-GAN achieves a 23%reduction in LPIPS compared to the method without using the seman-tic maps and a 6.9%reduction in FID for the method with semantic maps,respectively,and also shows higher realistic qualitative results.

Considerations of advanced aqueous batteries recycling:A perspective

1.Introduction Battery technology,serving as a basis for a rechargeable society,has developed rapidly over a century[1].The progress can be roughly divided into three stages,with the initial two mainly focusing on aqueous battery systems.Prior to 1859,research primarily focused on the conceptualization of batteries and the development of primary(non-rechargeable)cells[2].In the subsequent150 years,rechargeable battery technology emerged。

GraphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in time.For unmanned MMS,one of the key technologies is situation understanding.However,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation understanding.In order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big data.Nevertheless,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,Graph Spatial-Temporal Generative Adversarial Network(GraphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and interactions.Extensive experiments show the effectiveness and robustness of the proposed method.

Sand control mechanism of radial well filled with phase change material in hydrate reservoir

Radial well filled with phase change material has been proposed as a novel sand control method for hydrate exploitation.In order to reveal the sand control mechanism,CFD-DEM coupling method is applied to simulate the migration,settlement,and blockage processes of sand particles in the radial well.The obtained results indicate that three scenarios have been recognized for sand particles passing through sand control medium,based on the diameter ratio of sand control medium to sand particle(D_(d)):fully passing(D_(d)=8.75-22.5),partially passing and partially blocked(D_(d)=3.18-5.63),and completely blocked(D_(d)=2.18-3.21).After being captured by the sand control medium,sand particles can block pores,which increases fluid flow resistance and causes a certain pressure difference in the radial well.The pressure in the radial well should be lower than the hydrate phase equilibrium pressure during sand control design,for the purpose of promoting hydrate decomposition,and sand capture.The length of the radial well should be optimized based on the reservoir pore pressure,production pressure difference,bottom hole pressure,and the pressure gradient in the radial well.It should be noticed that the sand control medium leads to a decrease in permeability after sand particles captured.Even the permeability is reduced to several hundred millidarcy,it is still sufficient to ensure the effective flow of gas and water after hydrate decomposition.Increasing fluid velocity reduces the blocking capacity of the sand control medium,mainly because of deterioration in bridging between sand particles.

Elliptical vibration chiseling:a novel process for texturing ultra-high-aspect-ratio microstructures on the metallic surface

High-aspect-ratio metallic surface microstructures are increasingly demanded in breakthrough applications,such as high-performance heat transfer enhancement and surface plasmon devices.However,the fast and cost-effective fabrication of high-aspect-ratio microstructures on metallic surfaces remains challenging for existing techniques.This study proposes a novel cutting-based process,namely elliptical vibration chiseling(EV-chiseling),for the high-efficiency texturing of surface microstructures with an ultrahigh aspect ratio.Unlike conventional cutting,EV-chiseling superimposes a microscale EV on a backward-moving tool.The tool chisels into the material in each vibration cycle to generate an upright chip with a high aspect ratio through material deformation.Thanks to the tool’s backward movement,the chip is left on the material surface to form a microstructure rather than falling off.Since one microstructure is generated in one vibration cycle,the process can be highly efficient using ultrafast(>1 kHz)tool vibration.A finite element analysis model is established to explore the process mechanics of EV-chiseling.Next,a mechanistic model of the microstructured surface generation is developed to describe the microstructures’aspect ratio dependency on the process parameters.Then,surface texturing tests are performed on copper to verify the efficacy of EV-chiseling.Uniformed micro ribs with a spacing of 1–10μm and an aspect ratio of 2–5 have been successfully textured on copper.Compared with the conventional EV-cutting that uses a forward-moving tool,EV-chiseling can improve the aspect ratio of textured microstructure by up to 40 times.The experimental results also verify the accuracy of the developed surface generation model of microstructures.Finally,the effects of elliptical trajectory,depth of cut,tool shape,and tool edge radius on the surface generation of micro ribs have been discussed.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Tuning synergy between nickel and iron in Ruddlesden-Popper perovskites through controllable crystal dimensionalities towards enhanced oxygenevolving activity and stability

Ni-Fe-based oxides are among the most promising catalysts developed to date for the bottleneck oxygen evolution reaction(OER)in water electrolysis.However,understanding and mastering the synergy of Ni and Fe remain challenging.Herein,we report that the synergy between Ni and Fe can be tailored by crystal dimensionality of Ni,Fe-contained Ruddlesden-Popper(RP)-type perovskites(La_(0.125)Sr_(0.875))n+1(Ni_(0.25)Fe_(0.75))nO3n+1(n=1,2,3),where the material with n=3 shows the best OER performance in alkaline media.Soft X-ray absorption spectroscopy spectra before and after OER reveal that the material with n=3 shows enhanced Ni/Fe-O covalency to boost the electron transfer as compared to those with n=1 and n=2.Further experimental investigations demonstrate that the Fe ion is the active site and the Ni ion is the stable site in this system,where such unique synergy reaches the optimum at n=3.Besides,as n increases,the proportion of unstable rock-salt layers accordingly decreases and the leaching of ions(especially Sr^(2+))into the electrolyte is suppressed,which induces a decrease in the leaching of active Fe ions,ultimately leading to enhanced stability.This work provides a new avenue for rational catalyst design through the dimensional strategy.

Residual alkali-evoked cross-linked polymer layer for anti-air-sensitivity LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)cathode

High-energy density lithium-ion batteries(LIBs)with layered high-nickel oxide cathodes(LiNi_(x)Co_(y)Mn_(1-x-y)O_(2),x≥0.8)show great promise in consumer electronics and vehicular applications.However,LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)faces challenges related to capacity decay caused by residual alkalis owing to high sensitivity to air.To address this issue,we propose a hazardous substances upcycling method that fundamentally mitigates alkali content and concurrently induces the emergence of an anti-air-sensitive layer on the cathode surface.Through the neutralization of polyacrylic acid(PAA)with residual alkalis and then coupling it with 3-aminopropyl triethoxysilane(KH550),a stable and ion-conductive cross-linked polymer layer is in situ integrated into the LiNi_(0.89)Co_(0.06)Mn_(0.05)O_(2)(NCM)cathode.Our characterization and measurements demonstrate its effectiveness.The NCM material exhibits impressive cycling performance,retaining 88.4%of its capacity after 200 cycles at 5 C and achieving an extraordinary specific capacity of 170.0 mA h g^(-1) at 10 C.Importantly,this layer on the NCM efficiently suppresses unfavorable phase transitions,severe electrolyte degradation,and CO_(2)gas evolution,while maintaining commendable resistance to air exposure.This surface modification strategy shows widespread potential for creating air-stable LiNi_(x)Co_(y)Mn_(1-x-y)O_(2)cathodes,thereby advancing high-performance LIBs.