Single-cell sequencing technology in diabetic wound healing:New insights into the progenitors-based repair strategies

Diabetes mellitus(DM),an increasingly prevalent chronic metabolic disease,is characterised by prolonged hyperglycaemia,which leads to long-term health consequences.Although much effort has been put into understanding the pathogenesis of diabetic wounds,the underlying mechanisms remain unclear.The advent of single-cell RNA sequencing(scRNAseq)has revolutionised biological research by enabling the identification of novel cell types,the discovery of cellular markers,the analysis of gene expression patterns and the prediction of develop-mental trajectories.This powerful tool allows for an in-depth exploration of pathogenesis at the cellular and molecular levels.In this editorial,we focus on progenitor-based repair strategies for diabetic wound healing as revealed by scRNAseq and highlight the biological behaviour of various healing-related cells and the alteration of signalling pathways in the process of diabetic wound healing.ScRNAseq could not only deepen our understanding of the complex biology of diabetic wounds but also identify and validate new targets for inter-vention,offering hope for improved patient outcomes in the management of this challenging complication of DM.

Challenges and Limitations in Speech Recognition Technology:A Critical Review of Speech Signal Processing Algorithms,Tools and Systems

Speech recognition systems have become a unique human-computer interaction(HCI)family.Speech is one of the most naturally developed human abilities;speech signal processing opens up a transparent and hand-free computation experience.This paper aims to present a retrospective yet modern approach to the world of speech recognition systems.The development journey of ASR(Automatic Speech Recognition)has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper.A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented,along with a brief discussion of various modern-day developments and applications in this domain.This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing.Since speech recognition has a vast potential in various industries like telecommunication,emotion recognition,healthcare,etc.,this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution.

Terahertz shaping technology based on coherent beam combining

The generation of terahertz(THz) waves by focusing a femtosecond pulsed laser beam at a distance is able to overcome the strong absorption properties of air and has rapidly attracted the attention of industry. However, the poor directionality of the THz wave radiation generated by this method is not conducive to THz wave applications. By controlling the morphology of the ultrafast laser-excited plasma filament and its electron density distribution through coherent beam combining technology, we achieve direct THz beam shaping and are able to obtain THz wave radiation of Gaussian or arbitrary transverse distribution. The novel experimental approach proposed in this paper opens up the research field of direct THz wave shaping using plasma. Moreover, it innovates multi-parameter convergence algorithms and, by doing so, has the potential to find beam patterns with higher energy conversion efficiency and break the energy limit of THz waves emitted by lasers at high power.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

裂纹加筋和缺口加筋对钢加筋板极限强度的影响

This paper numerically evaluates the effect of the crack position on the ultimate strength of stiffened panels.Imperfections such as notches and cracks in aged marine stiffened panels can reduce their ultimate strength.To investigate the effect of crack length and position,a series of nonlinear finite element analyses were carried out and two cases were considered,i.e.,case 1 with thin stiffeners and case 2 with thick stiffeners.In both cases,the stiffeners have the same cross-section area.To have a basis for comparison,the intact panels were modeled as well.The cracks and notches were in the longitudinal and transverse direction and were assumed to be in the middle part of the panel.The cracks and notches were assumed to be through the thickness and there is neither crack propagation nor contact between crack faces.Based on the numerical results,longitudinal cracks affect the behavior of the stiffened panels in the postbuckling region.When the stiffeners are thinner,they buckle first and provide no reserved strength after plate buckling.Thus,cracks in the stiffeners do not affect the ultimate strength in the case of the thinner stiffeners.Generally,when stiffeners are thicker,they affect the postbuckling behavior more.In that case,cracks in the stiffeners affect the buckling and failure modes of the stiffened panels.The effect of notch was also studied.In contrast to the longitudinal crack in stiffeners,a notch in the stiffeners reduces the ultimate strength of the stiffened panel for both slender and thick stiffeners.

Phenolic Profile and Volatiles of in vitro Propagated Lavandula angustifolia Mill.Seedlings

An effective in vitro propagation protocol was designed for Lavandula angustifolia Miller,a medicinal aromatic plant that is a prominent source of volatile organic compounds(VOCs).Murashige and Skoog media were supplemented with various concentrations of Plant Growth Regulators(PGRs),and the growth parameters of the nodal segments were examined.Nodal explants formed callus when they were supplemented with 2 mg/L of 6-Benzylaminopurine(BAP).The superior hormonal concentration of Murashige and Skoog(MS)media for the proliferation of shoots from callus cultures(39.33%)was 5 mg/L of 2-Isopentenyl adenine(2iP),and the favorable media for the growth of L.angustifolia callus cultures was 1 and 2 mg/L of BAP,with a 98%for-mation rate in each case.The callus cultures and in vitro propagated L.angustifolia seedlings obtained from var-ious PGR concentrations of MS media were exposed to qualitative and quantitative analysis in terms of phenolic profiles,flavonoids,High-performance liquid chromatography(HPLC)analysis of phenolic acids,and headspace-SPME analysis for volatiles.Such analysis revealed that micropropagated seedlings grown in media containing 1 mg/L of 6-Furfurylaminopurine(KIN)accumulated the highest yield(11.95±0.01 mg GAE/g)of phenolic acids.In contrast,the lowest concentration(2.17±0.04 mg GAE/g)was detected in 0.5 mg/L of BAP+0.5 mg/L of Naphthaleneacetic acid(NAA)media.The plantlets grown in 0.5 mg/L of BAP+0.5 mg/L of NAA media showed the highestflavonoid yield(31.67±0.06μg/g QE/g).In contrast,callus samples exhibited the lowest yield(11.59±0.02μg/g QE/g)offlavonoids in MS media supplemented with a concentration of 0.5 mg/L of BAP.HPLC analysis revealed the variability of phenolic acid contents within the callus cultures as well as plantlets,with gallic acid,4-OH benzoic acid,chlorogenic acid,vanillic acid,caffeic acid,cinnamic acid,and rosmarinic acid being the prominent constituents.The presence of twenty-two chemicals was revealed by headspace-SPME analysis.Eucalyptol,nonanal,borneol,carvone,andβ-caryophyllene were the most abundant.This study demonstrated that micropropagation of L.angustifolia may be an effective method to produce large numbers of genetically identical plantlets for the production of high-value bio compounds.

New possibility for PET plastic recycling by a tailored hydrolytic enzyme

Plastic waste puts a huge burden on the ecosystem due to the current lack of mature recycling technology.Poly(ethylene terephthalate)(PET)is one of the most produced plastics in the world.Enzymatic decomposition holds the promise of recovering monomers from PET plastic,and the monomers can be used to regenerate new PET products.However,there are still limitations in the activity and thermal stability of the existing PET hydrolases.The recent study by Lu et al.introduced a novel PET hydrolase via machine learning-aided engineering.The obtained PET hydrolase showed excellent activity and thermal stability in the hydrolysis of PET and is capable of directly degrading large amounts of postconsumer PET products.This approach provides an effective method for recycling PET waste and is expected to improve the current state of plastic pollution worldwide.

Burden of routine orthopedic implant removal a single center retrospective study

BACKGROUND Open reduction and internal fixation represent prevalent orthopedic procedures,sparking ongoing discourse over whether to retain or remove asymptomatic implants.Achieving consensus on this matter is paramount for orthopedic surgeons.This study aims to quantify the impact of routine implant removal on patients and healthcare facilities.A retrospective analysis of implant removal cases from 2016 to 2022 at King Fahad Hospital of the University(KFHU)was conducted and subjected to statistical scrutiny.Among these cases,44%necessitated hospitalization exceeding one day,while 56%required only a single day.Adults exhibited a 55%need for extended hospital stays,contrasting with 22.8%among the pediatric cohort.The complication rate was 6%,with all patients experiencing at least one complication.Notably,34.1%required sick leave and 4.8%exceeded 14 d.General anesthesia was predominant(88%).Routine implant removal introduces unwarranted complications,particularly in adults,potentially prolonging hospitalization.This procedure strains hospital resources,tying up the operating room that could otherwise accommodate critical surgeries.Clearly defined institutional guidelines are imperative to regulate this practice.AIM To measure the burden of routine implant removal on the patients and hospital.METHODS This is a retrospective analysis study of 167 routine implant removal cases treated at KFHU,a tertiary hospital in Saudi Arabia.Data were collected in the orthopedic department at KFHU from February 2016 to August 2022,which includes routine asymptomatic implant removal cases across all age categories.Nonroutine indications such as infection,pain,implant failure,malunion,nonunion,restricted range of motion,and prominent hardware were excluded.Patients who had external fixators removed or joints replaced were also excluded.RESULTS Between February 2016 and August 2022,360 implants were retrieved;however,only 167 of those who met the inclusion criteria were included in this study.The remaining implants were rejected due to exclusion criteria.Among the cases,44%required more than one day in the hospital,whereas 56%required only one day.55%of adults required more than one day of hospitalization,while 22.8%of pediatric patients required more than one day of inpatient care.The complication rate was 6%,with each patient experiencing at least one complication.Sick leave was required in 34.1%of cases,with 4.8%requiring more than 14 d.The most common type of anesthesia used in the surgeries was general anesthesia(88%),and the mean(SD)surgery duration was 77.1(54.7)min.CONCLUSION Routine implant removal causes unnecessary complications,prolongs hospital stays,depletes resources and monopolizing operating rooms that could serve more critical procedures.

Control of GaN inverted pyramids growth on c-plane patterned sapphire substrates

Growth of gallium nitride(GaN)inverted pyramids on c-plane sapphire substrates is benefit for fabricating novel devices as it forms the semipolar facets.In this work,GaN inverted pyramids are directly grown on c-plane patterned sapphire substrates(PSS)by metal organic vapor phase epitaxy(MOVPE).The influences of growth conditions on the surface morphol-ogy are experimentally studied and explained by Wulff constructions.The competition of growth rate among{0001},{1011},and{1122}facets results in the various surface morphologies of GaN.A higher growth temperature of 985°C and a lowerⅤ/Ⅲratio of 25 can expand the area of{}facets in GaN inverted pyramids.On the other hand,GaN inverted pyramids with almost pure{}facets are obtained by using a lower growth temperature of 930℃,a higherⅤ/Ⅲratio of 100,and PSS with pattern arrangement perpendicular to the substrate primary flat.

未热解厚热障涂层的隔热能力和黏接强度

对比研究使用未热解粉末制备的厚热障涂层(UPTTBC)和传统TBC、厚TBC(TTBC)及密集垂直裂纹(DVC)TTBC 4种涂层的隔热能力和黏接强度。采用降温法测试涂层的隔热能力;根据ASTM C633标准,采用拉拔试验检测涂层的黏接强度。结果显示,UPTTBC和DVC TTBC的黏接强度分别比TTBC高35%和25%。这可能是由于UPTTBC结构中存在亚微米尺度的未熔化区域,起到了阻止裂纹扩展的作用。此外,隔热测试的结果表明,DVCTTBC涂层的隔热能力最差,为0.28℃/μm,而UPTTBC涂层的隔热能力最强,为0.40℃/μm。由于UPTTBCs显微组织中存在亚微米区域,使涂层展现出优越的隔热能力。相反,DVCTTBC因其致密的结构,具有最低的隔热能力。

A comparative in vitro study on the effect of SGLT2 inhibitors on chemosensitivity to doxorubicin in MCF-7 breast cancer cells

Cancer frequently develops resistance to the majority of chemotherapy treatments.This study aimed to examine the synergistic cytotoxic and antitumor effects of SGLT2 inhibitors,specifically Canagliflozin(CAN),Dapagliflozin(DAP),Empagliflozin(EMP),and Doxorubicin(DOX),using in vitro experimentation.The precise combination of CAN+DOX has been found to greatly enhance the cytotoxic effects of doxorubicin(DOX)in MCF-7 cells.Interestingly,it was shown that cancer cells exhibit an increased demand for glucose and ATP in order to support their growth.Notably,when these medications were combined with DOX,there was a considerable inhibition of glucose consumption,as well as reductions in intracellular ATP and lactate levels.Moreover,this effect was found to be dependent on the dosages of the drugs.In addition to effectively inhibiting the cell cycle,the combination of CAN+DOX induces substantial modifications in both cell cycle and apoptotic gene expression.This work represents the initial report on the beneficial impact of SGLT2 inhibitor medications,namely CAN,DAP,and EMP,on the responsiveness to the anticancer properties of DOX.The underlying molecular mechanisms potentially involve the suppression of the function of SGLT2.

Band structures of strained kagome lattices

Materials with kagome lattices have attracted significant research attention due to their nontrivial features in energy bands.We theoretically investigate the evolution of electronic band structures of kagome lattices in response to uniaxial strain using both a tight-binding model and an antidot model based on a periodic muffin-tin potential.It is found that the Dirac points move with applied strain.Furthermore,the flat band of unstrained kagome lattices is found to develop into a highly anisotropic shape under a stretching strain along y direction,forming a partially flat band with a region dispersionless along ky direction while dispersive along kx direction.Our results shed light on the possibility of engineering the electronic band structures of kagome materials by mechanical strain.

A step to the decentralized real-time timekeeping network

The composite time scale(CTS) provides an accurate and stable time-frequency reference for modern science and technology. Conventional CTS always features a centralized network topology, which means that the CTS is accompanied by a local master clock. This largely restricts the stability and reliability of the CTS. We simulate the restriction and analyze the influence of the master clock on the CTS. It proves that the CTS's long-term stability is also positively related to that of the master clock, until the region dominated by the frequency drift of the H-maser(averaging time longer than ~10~5s).Aiming at this restriction, a real-time clock network is utilized. Based on the network, a real-time CTS referenced by a stable remote master clock is achieved. The experiment comparing two real-time CTSs referenced by a local and a remote master clock respectively reveals that under open-loop steering, the stability of the CTS is improved by referencing to a remote and more stable master clock instead of a local and less stable master clock. In this way, with the help of the proposed scheme, the CTS can be referenced to the most stable master clock within the network in real time, no matter whether it is local or remote, making democratic polycentric timekeeping possible.

Aggravation of Cancer,Heart Diseases and Diabetes Subsequent to COVID-19 Lockdown via Mathematical Modeling

The global populationhas beenandwill continue to be severely impacted by theCOVID-19 epidemic.The primary objective of this research is to demonstrate the future impact of COVID-19 on those who suffer from other fatal conditions such as cancer,heart disease,and diabetes.Here,using ordinary differential equations(ODEs),two mathematical models are developed to explain the association between COVID-19 and cancer and between COVID-19 and diabetes and heart disease.After that,we highlight the stability assessments that can be applied to these models.Sensitivity analysis is used to examine how changes in certain factors impact different aspects of disease.The sensitivity analysis showed that many people are still nervous about seeing a doctor due to COVID-19,which could result in a dramatic increase in the diagnosis of various ailments in the years to come.The correlation between diabetes and cardiovascular illness is also illustrated graphically.The effects of smoking and obesity are also found to be significant in disease compartments.Model fitting is also provided for interpreting the relationship between real data and the results of thiswork.Diabetic people,in particular,need tomonitor their health conditions closely and practice heart health maintenance.People with heart diseases should undergo regular checks so that they can protect themselves from diabetes and take some precautions including suitable diets.The main purpose of this study is to emphasize the importance of regular checks,to warn people about the effects of COVID-19(including avoiding healthcare centers and doctors because of the spread of infectious diseases)and to indicate the importance of family history of cancer,heart diseases and diabetes.The provision of the recommendations requires an increase in public consciousness.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

A post-modification strategy to precisely construct dual-atom sites for oxygen reduction electrocatalysis

Dual-atom catalysts(DACs) afford promising potential for oxygen reduction electrocatalysis due to their high atomic efficiency and high intrinsic activity.However,precise construction of dual-atom sites remains a challenge.In this work,a post-modification strategy is proposed to precisely fabricate DACs for oxygen reduction electrocatalysis.Concretely,a secondary metal precursor is introduced to the primary single-atom sites to introduce direct metal-metal interaction,which ensures the formation of desired atom pair structure during the subsequent pyrolysis process and allows for successful construction of DACs.The as-prepared FeCo-NC DAC exhibits superior oxygen reduction electrocatalytic activity with a half-wave potential of 0,91 V vs.reversible hydrogen electrode.Zn-air batteries equipped with the FeCo-NC DAC demonstrate higher peak power density than those with the Pt/C benchmark.More importantly,this post-modification strategy is demonstrated universal to achieve a variety of dual-atom sites.This work presents an effective synthesis methodology for precise construction of catalytic materials and propels their applications in energy-related devices.

Unraveling the Fundamental Mechanism of Interface Conductive Network Influence on the Fast‑Charging Performance of SiO‑Based Anode for Lithium‑Ion Batteries

Progress in the fast charging of high-capacity silicon monoxide(SiO)-based anode is currently hindered by insufficient conductivity and notable volume expansion.The construction of an interface conductive network effectively addresses the aforementioned problems;however,the impact of its quality on lithium-ion transfer and structure durability is yet to be explored.Herein,the influence of an interface conductive network on ionic transport and mechanical stability under fast charging is explored for the first time.2D modeling simulation and Cryo-transmission electron microscopy precisely reveal the mitigation of interface polarization owing to a higher fraction of conductive inorganic species formation in bilayer solid electrolyte interphase is mainly responsible for a linear decrease in ionic diffusion energy barrier.Furthermore,atomic force microscopy and Raman shift exhibit substantial stress dissipation generated by a complete conductive network,which is critical to the linear reduction of electrode residual stress.This study provides insights into the rational design of optimized interface SiO-based anodes with reinforced fast-charging performance.

Cost-Efficient Medium Frequency Propagation Research with Software Defined Radio

Medium Frequency radio holds significance in modern society as it supports broadcasting and individual communications in the public, government, and military sectors. Enhancing the availability and quality of these communications is only possible by enhancing the understanding of medium frequency propagation. While traditional methods of radio wave propagation research can have a high material demand and cost, software defined radio presents itself as a versatile and low-cost platform for medium frequency signal reception and data acquisition. This paper details a research effort that utilizes software defined radio to help characterize medium frequency signal strength in relation to ionospheric and solar weather propagation determinants. Signal strength data from seven medium frequency stations of unique transmission locations and varying transmission powers were retrieved in 24-hour segments via a receiving loop antenna, Airspy HF+ Discovery software defined radio, and SDR Sharp software interface network. Retrieved data sets were visualized and analyzed in MATLAB for the identification of signal strength trends, which were subsequently compared to historical ionospheric and space weather indices in pursuit of a quantifiable correlation between such indices and medium frequency signal strengths. The results of the investigation prove that software defined radio, when used in conjunction with a receiving antenna and data analysis program, provides a versatile mechanism for cost-efficient propagation research.