Navigating the pathways:TAR-DNA-binding-protein-43 aggregation,axonal transport,and local synthesis in amyotrophic lateral sclerosis pathology

Neurons are highly polarized cells with axons reaching over a meter long in adult humans.To survive and maintain their proper function,neurons depend on specific mechanisms that regulate spatiotemporal signaling and metabolic events,which need to be carried out at the right place,time,and intensity.Such mechanisms include axonal transport,local synthesis,and liquid-liquid phase separations.Alterations and malfunctions in these processes are correlated to neurodegenerative diseases such as amyotrophic lateral sclerosis(ALS).

Advancing healthcare through laboratory on a chip technology:Transforming microorganism identification and diagnostics

In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.

Templated synthesis of transition metal phosphide electrocatalysts for oxygen and hydrogen evolution reactions

Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts.TMPs have been produced in various morphologies,including hollow and porous nanostructures,which are features deemed desirable for electrocatalytic materials.Templated synthesis routes are often responsible for such morphologies.This paper reviews the latest advances and existing challenges in the synthesis of TMP-based OER and HER catalysts through templated methods.A comprehensive review of the structure-property-performance of TMP-based HER and OER catalysts prepared using different templates is presented.The discussion proceeds according to application,first by HER and further divided among the types of templates used-from hard templates,sacrificial templates,and soft templates to the emerging dynamic hydrogen bubble template.OER catalysts are then reviewed and grouped according to their morphology.Finally,prospective research directions for the synthesis of hollow and porous TMP-based catalysts,such as improvements on both activity and stability of TMPs,design of environmentally benign templates and processes,and analysis of the reaction mechanism through advanced material characterization techniques and theoretical calculations,are suggested.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

Synthesis and Modulation of Low-Dimensional Transition Metal Chalcogenide Materials via Atomic Substitution

In recent years,low-dimensional transition metal chalcogenide(TMC)materials have garnered growing research attention due to their superior electronic,optical,and catalytic properties compared to their bulk counterparts.The controllable synthesis and manipulation of these materials are crucial for tailoring their properties and unlocking their full potential in various applications.In this context,the atomic substitution method has emerged as a favorable approach.It involves the replacement of specific atoms within TMC structures with other elements and possesses the capability to regulate the compositions finely,crystal structures,and inherent properties of the resulting materials.In this review,we present a comprehensive overview on various strategies of atomic substitution employed in the synthesis of zero-dimensional,one-dimensional and two-dimensional TMC materials.The effects of substituting elements,substitution ratios,and substitution positions on the structures and morphologies of resulting material are discussed.The enhanced electrocatalytic performance and photovoltaic properties of the obtained materials are also provided,emphasizing the role of atomic substitution in achieving these advancements.Finally,challenges and future prospects in the field of atomic substitution for fabricating low-dimensional TMC materials are summarized.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Recent advances in cobalt phosphide-based materials for electrocatalytic water splitting:From catalytic mechanism and synthesis method to optimization design

Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.

Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Efficient electrocatalytic urea synthesis from CO_(2)and nitrate over the scale-up produced FeNi alloy-decorated nanoporous carbon

Electrocatalytic urea synthesis provides a favorable strategy for conventional energy-consuming urea synthesis,but achieving large-scale catalyst synthesis with high catalytic efficiency remains challenging.Herein,we developed a simple method for the preparation of a series of FeNi-alloy-based catalysts,named FeNi@nC-T(n represents the content of nanoporous carbon as 1,3,5,7 or 9 g and T=900,950,1000 or 1100°C),for highly performed urea synthesis via NO_(3)−and CO_(2)co-reduction.The FeNi@7C-1000 achieved a high urea yield of 1041.33 mmol h^(−1)gFeNi^(−1)with a Faradaic efficiency of 15.56%at–1.2 V vs.RHE.Moreover,the scale-up synthesized FeNi@7C-950-S(over 140 g per batch)was achieved with its high catalytic performance and high stability maintained.Mechanism investigation illuminated that the Ni and Fe sites catalyze and stabilize the key*CO and*N intermediates and minimize the C–N coupling reaction barriers for highly efficient urea synthesis.

N,S,Br co-doped carbon dots:One-step synthesis and fluorescent detection of 6-mercaptopurine in tablet

6-mercaptopurine(6-MP),a purine derivative(3,7-dihydropurine-6-thione),has been utilized as an effective immunosuppressive drug for clinically treating leukemia and other autoimmune diseases[1].6-MP and its corresponding metabolites can suppress the function of RnaseH,and thus they are cytotoxic and threaten the human health[2].Therefore,the accurate quantification of 6-MP is crucial.To date,researchers continue to expend considerable effort in developing 6-MP detection methods.Fluorescence analysis eliminates disadvantages,such as toxic solvents,expensive equipment.

Synthesis Methods and Property Control of Two-Dimensional Magnetic Materials

Two-dimensional(2D)magnetic materials have been demonstrated to have excellent chemical,optical,electrical,and magnetic properties,particularly in the development of multifunctional electronic and spin electronic devices,showcasing tremendous potential.Therefore,corresponding synthesis techniques for 2D magnetic materials that offer high quality,high yield,low cost,time-saving,and simplicity are highly desired.This review provides a comprehensive overview of recent research advances in preparation of magnetic 2D materials,with a particular focus on the preparation methods employed.Moreover,the characteristics and applications of these magnetic materials are also discussed.Finally,the challenges and prospects of synthesis methods for magnetic 2D materials are briefly addressed.This review serves as a guiding reference for the controlled synthesis of 2D magnetic materials.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots.

Fluid identification and tight oil layer classification for the southwestern Mahu Sag,Junggar Basin using NMR logging-based spectrum decomposition

The intricate distribution of oil and water in tight rocks makes pinpointing oil layers challenging.While conventional identification methods offer potential solutions,their limited accuracy precludes them from being effective in their applications to unconventional reservoirs.This study employed nuclear magnetic resonance(NMR)spectrum decomposition to dissect the NMR T_(2)spectrum into multiple subspectra.Furthermore,it employed laboratory NMR experiments to ascertain the fluid properties of these sub-spectra,aiming to enhance identification accuracy.The findings indicate that fluids of distinct properties overlap in the T_(2)spectra,with bound water,movable water,bound oil,and movable oil appearing sequentially from the low-value zone to the high-value zone.Consequently,an oil layer classification scheme was proposed,which considers the physical properties of reservoirs,oil-bearing capacity,and the characteristics of both mobility and the oil-water two-phase flow.When applied to tight oil layer identification,the scheme's outcomes align closely with actual test results.A horizontal well,deployed based on these findings,has produced high-yield industrial oil flow,underscoring the precision and dependability of this new approach.

Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology

The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”.

Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm

A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors.

Synthesis of mordenite by solvent-free method and its application in the dimethyl ether carbonylation reaction

Mordenite with different Si/Al ratios were synthesized by solvent-free method and used for dimethyl ether(DME)carbonylation reaction.The influence of Si/Al ratio in the feedstock on the structure,porosity and acid sites were systematically investigated.The characterization results showed that with the increase of Si/Al ratio in the feedstock,part of silicon species fail to enter the skeleton and the specific surface area and pore volume of the samples decreased.The amount of weak acid and medium strong acid decreased alongside with the increasing Si/Al ratio,and the amount of strong acid slightly increased.The Al atoms preferentially enter the strong acid sites in the 8 member ring(MR)channel during the crystallization process.The high Si/Al ratio sample had more acid sites located in the 8 MR channel,leading to more active sites for carbonylation reaction and higher catalytic performance.Appropriately increasing the Si/Al ratio was beneficial for the improvement of carbonylation reaction activity over the mordenite(MOR)catalyst.

Synthesis of Silver Nanoparticles from Honeybees and Its Antibacterial Potential

Honeybees (Apis mellifera) are important pollinators of flowering plants and agricultural crops contributing annually to billions of dollars in revenues to crop production. Honeybees have an average lifespan between 8 weeks to 5 years. Dead honeybees are abundantly available in beehives and can be utilized as an alternative source to synthesize nanoparticles. In recent years, biologically synthesized nanoparticles have been preferred over their chemical counterparts. However, honeybee-based-green synthesis of nanoparticles has not been explored yet. Herein, we report the biosynthesis of silver nanoparticles from honeybees and its antibacterial activity. The synthesis of silver nanoparticles was monitored visually through a gradual change in color. Furthermore, the biosynthesized nanoparticles were confirmed and characterized by UV-visible spectroscopy. Scanning Electron Microscope was utilized to analyze the average size and morphologies of the biosynthesized nanoparticles. Subsequently, the antibacterial potential of the biosynthesized silver nanoparticles was tested against selected Gram-positive and Gram-negative bacterial strains. It was found that a distinct color change from yellow to brown in the reaction solution suggested the formation of silver nanoparticles. The biosynthesized nanoparticles exhibited absorption maxima at 430 nm. The SEM analysis confirmed the spherical and cuboidal shape of the biosynthesized silver nanoparticles with a size range between 10 - 40 nm. Furthermore, the biosynthesized silver nanoparticles exhibited strong antimicrobial potential against tested Gram-positive and Gram-negative bacteria strains by aggregating on the cell surface. This study showcases the biomedical and agricultural applications of biosynthesized silver nanoparticles from honeybee wings. .

Rapid Green Synthesis of Silver Nanoparticles by Reishi and Their Antibacterial Activity and Mechanisms

Nanotechnology is a rapidly growing field in biomedical engineering with references to efficiency, safety, and cost-effective approaches. Herein, the objective of this study was to examine an innovative approach to rapidly synthesis silver nanoparticles from an aqueous extract of medicinal mushroom Ganoderma lucidum (also known as reishi). The structural and dimensional dispersion of the biosynthesized silver nanoparticles from reishi was confirmed by UV-Vis spectrophotometer (UV-Vis) and Scanning Electron Microscopy (SEM) analysis. Additionally, the biosynthesized silver nanoparticles from resihi were used to explore their potential antimicrobial activity against Staphylococcus aureus and Micrococcus luteus and Escherichia coli and Klebsiella pneumoniae. The results from this study revealed that the silver nanoparticles mediated by reishi adopted a spherical shape morphology with sizes, less than 100 nm and revealed strong absorption plasmon band at 440 nm. Furthermore, the biosynthesized silver nanoparticles from reishi exhibited antibacterial activity against the tested S. aureus and M. luteus and E. coli and K. pneumoniae by altering their morphology which signifies their biomedical potential.

Identification of miRNAs and target genes regulating catechin biosynthesis in tea (Camellia sinensis)

Micro RNAs（miRNAs） are endogenous non-protein-coding small RNAs that play crucial and versatile regulatory roles in plants. Using a computational identification method, we identified 55 conserved miRNAs in tea（Camellia sinensis） by aligning miRNA sequences of different plant species with the transcriptome library of tea strain 1005. We then used quantitative real-time PCR（qRT-PCR） to analyze the expression of 31 identified miRNAs in tea leaves of different ages, thereby verifying the existence of these miRNAs and confirming the reliability of the computational identification method. We predicted which miRNAs were involved in catechin synthesis using ps RNAtarget Software based on conserved miRNAs and catechin synthesis pathway-related genes. Then, we used RNA ligase-mediated rapid amplification of c DNA ends（RLM-RACE） to obtain seven miRNAs cleaving eight catechin synthesis pathway-related genes including chalcone synthase（CHS）, chalcone isomerase（CHI）, dihydroflavonol 4-reductase（DFR）, anthocyanidin reductase（ANR）, leucoanthocyanidin reductase（LAR）, and flavanone 3-hydroxylase（F3H）. An expression analysis of miRNAs and target genes revealed that miR529d and miR156 g-3 p were negatively correlated with their targets CHI and F3H, respectively. The expression of other miRNAs was not significantly related to their target genes in the catechin synthesis pathway. The RLM-RACE results suggest that catechin synthesis is regulated by miRNAs that can cleave genes involved in catechin synthesis.