Relationship between the use of smart medical services and mental health status

In this editorial,I comment on the article by Zhang et al.To emphasize the importance of the topic,I discuss the relationship between the use of smart medical devices and mental health.Smart medical services have the potential to positively influence mental health by providing monitoring,insights,and inter-ventions.However,they also come with challenges that need to be addressed.Understanding the primary purpose for which individuals use these smart tech-nologies is essential to tailoring them to specific mental health needs and prefe-rences.

Spectrum-Effect Relationship Between High Performance Liquid Chromatography Fingerprints and Anticoccidial Activities of a Compound Chinese Medicine

Quality control and screening of active substances in traditional Chinese medicines have been performed using fingerprint analysis. The spectrum-effect relationship between chromatography fingerprints and efficacy of herbal drugs is considered as a potentially useful method for determining active ingredients in complex mixtures. The study was designed to develop a method for determining the bioactive components of a compound Chinese medicine called Tiefeng based on spectrum-effect relationships between high-performance liquid chromatography （HPLC） fingerprints and anticoccidial activities. Four peaks of the established HPLC fingerprint indicate the main bioactive components of this medicine. In addition, pharrnacodynamic atlas was defined and used to assess the anticoccidial activity of Tiefeng from different sources for the first time. We found that the level of anticoccidial activity of Tiefeng was consistent with the degree of similarity between the pharmacodynamic atlas and chromatogram of any sample. Furthermore, effect of this medicine was related with the main active constituents, along with the origin and the harvesting time.

Spectrum-effect relationship between HPLC fingerprints and bioactive components of Radix Hedysari on increasing the peak bone mass of rat

The traditional Chinese medicine of Radix Hedysari plays an important role in invigorating gas for ascending, benefiting blood for promoting production of fluid, and promoting circulation for removing obstruction in collaterals, which is consistent with the principle of treatment for osteoporosis. This study is designed to investigate the bioactive components on increasing peak bone mass (PBM) by exploring the spectrum-effect relationship between chromatography fingerprints and effect. Multiple indicators are selected to evaluate the pharmacological activity. In fingerprints, 21 common peaks are obtained, five of which are identified. Furthermore, gray relational analysis (GRA) is a quantitative method of gray system theory and is used to describe the correlation degree of common peaks and pharmacological activities with relational value. 21 components are then divided into three different regions, of which ononin and calycosin play an extremely significant role in increasing PBM. In addition, factor analysis and hierarchical cluster analysis (HCA) are used to screen the optimal producing area for Radix Hedysari. This provides a comprehensive and efficient method to improve the quality evaluation of Radix Hedysari, confirming the bioactive components for PBM-enhancement and further develop its medicinal value.

Relationship between Kawasaki disease and abdominal pain

This editorial presents an analysis of an article recently published in the World Journal of Clinical Cases.Kawasaki disease(KD)is a well-known pediatric vasculitis characterized by fever,rash,conjunctivitis,oral mucosal changes,and swelling of the extremities.This editorial aims to delve into the intricate relationship between KD and abdominal pain,drawing insights from recent research findings to provide a comprehensive understanding and potential avenues for future investigation.

Detection of the Relationship between Imipenem Susceptible and Non-Susceptible Clinical Isolates of Acinetobacter Baumannii by Repetitive Element PCR-Mediated DNA Fingerprinting in an Egyptian Hospital

In this work, the authors aimed to detect the clonal relatedness of the isolated imipenem-susceptible and non-susceptible Acenitobacter baumanii. This study was conducted from September 2008 through August 2009 in Aboelreech-Elmounira paediatric-Cairo University-teaching hospital in Egypt. All the isolated acenitobacter species were identified by standard laboratory procedures. The clonal relationship of the A. baumanii （the most common detected clinical type） was studied by biotyping and AST and then confirmed using rep-PCR with primers aimed at repetitive extragenic palindromic sequences and enterobacterial repetitive intergenic consensus sequences. A total of 100 A. baumanii isolates out of 104 acenitobacter species were recovered from different clinical samples. Sixty two percent of the isolates were resistant to imipenem. The resulting rep-PCR patterns oftheA, baumanii strains revealed 8 clones, 3 clones found in the imipenem resistant group, and 5 clones in imipenem sensitive group with statistically significant clonal distribution in both groups （P-value 0.00）. Clonality was proved in imipenem resistant group with an alarming predominance of clone 1 representing 80.6% of IMP-R isolates. In accordance the prevalence of resistant acenitobacter strains seems to be correlated with inappropriate antibiotic use. These results call for strict compliance of coordinated strategy of infection control measures and judicious use of antimicrobials which is likely to effectively deal with this serious public health issue.

HPLC Fingerprinting and Spectrum-antitumor Effect Relationship for Discrimination between Mylabris phalerata Pallas and Mylabris cichorii Linnaeus

Objective： Evaluation of discrimination between two Mylabris Species based on HPLC fingerprinting andspectrum-antitumor effect relationship. Methods： In this study, a simple and efficient high-performance liquidchromatography （HPLC） method integrating with chemometric analysis and spectrum-antitumor effect relationship wasdeveloped for discrimination between two species of Mylabris： Mylabris phalerata Pallas （MP） and Mylabris cichoriiLinnaeus （MC）. Results： In the fingerprint analysis, 14 characteristic peaks were selected to assess the differencesbetween MP and MC using the similarity and pattern recognition analysis using PCA and OPLS-DA. The HPLCchromatograms of samples from 10 regions of China showed differences between MP and MC, and 7 characteristicchemical markers were found. In the spectrum-antitumor effect relationship analysis, 4 activity markers played a vitalrole in decreasing the IC50 and might be the antitumor components of Mylabris by grey relational analysis andmultivariate linear regression analysis. The chemometric analysis in combination with spectrum-effect relationshipresults indicated that peaks 2 （cytosine）, 4 （unknown） and 14 （unknown） were important differential markers fordistinguishing the two species of Mylabris. Conclusion： The method is applicable, credible and more efficient todiscriminate MP and MC, and will offer a new way for facilitating quality control of insect medicines.

Geographical origin identification of winter jujube(Ziziphus jujuba Dongzao')by using multi-element fingerprinting with chemometrics

Winter jujube(Ziziphus jujuba'Dongzao')is greatly appreciated by consumers for its excellent quality,but brand infringement frequently occurs in the market.Here,we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer(ICP-MS).As a result,16 elements(Mg,K,Mn,Cu,Zn,Mo,Ba,Be,As,Se,Cd,Sb,Ce,Er,Tl,and Pb)exhibited significant differences in samples from different producing areas.Supervised linear discriminant analysis(LDA)and orthogonal projection to latent structures discriminant analysis(OPLS-DA)showed better performance in identifying the origin of samples than unsupervised principal component analysis(PCA).LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64%in the testing set,respectively.By using the multilayer perceptron(MLP)and C5.0,the prediction accuracy of the models could reach 96.36 and 91.06%,respectively.Based on the above four chemometric methods,Cd,Tl,Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube.Overall,this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics,and may also provide reference for establishing the origin traceability system of other fruits.

An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

A UWB/IMU-Assisted Fingerprinting Localization Framework with Low Human Efforts

With the rapid development of smart phone,the location-based services(LBS)have received great attention in the past decades.Owing to the widespread use of WiFi and Bluetooth devices,Received Signal Strength Indication(RSSI)fingerprintbased localization method has obtained much development in both academia and industries.In this work,we introduce an efficient way to reduce the labor-intensive site survey process,which uses an UWB/IMU-assisted fingerprint construction(UAFC)and localization framework based on the principle of Automatic radio map generation scheme(ARMGS)is proposed to replace the traditional manual measurement.To be specific,UWB devices are employed to estimate the coordinates when the collector is moved in a reference point(RP).An anchor self-localization method is investigated to further reduce manual measurement work in a wide and complex environment,which is also a grueling,time-consuming process that is lead to artificial errors.Moreover,the measurements of IMU are incorporated into the UWB localization algorithm and improve the label accuracy in fingerprint.In addition,the weighted k-nearest neighbor(WKNN)algorithm is applied to online localization phase.Finally,filed experiments are carried out and the results confirm the effectiveness of the proposed approach.

CMAES-WFD:Adversarial Website Fingerprinting Defense Based on Covariance Matrix Adaptation Evolution Strategy

Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.

BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

An Active Deception Defense Model Based on Address Mutation and Fingerprint Camouflage

The static and predictable characteristics of cyber systems give attackers an asymmetric advantage in gathering useful information and launching attacks.To reverse this asymmetric advantage,a new defense idea,called Moving Target Defense(MTD),has been proposed to provide additional selectable measures to complement traditional defense.However,MTD is unable to defeat the sophisticated attacker with fingerprint tracking ability.To overcome this limitation,we go one step beyond and show that the combination of MTD and Deception-based Cyber Defense(DCD)can achieve higher performance than either of them.In particular,we first introduce and formalize a novel attacker model named Scan and Foothold Attack(SFA)based on cyber kill chain.Afterwards,we develop probabilistic models for SFA defenses to provide a deeper analysis of the theoretical effect under different defense strategies.These models quantify attack success probability and the probability that the attacker will be deceived under various conditions,such as the size of address space,and the number of hosts,attack analysis time.Finally,the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.Also,the defense strategy of combining address mutation and fingerprint camouflage can achieve a better defense effect than the single address mutation.

Quantifying source-sink relationships in leaf-color modified rice genotypes during grain filling

Leaf-color modification can affect canopy photosynthesis,with potential effects on rice yield and yield components.Modulating source-sink relationships through crop management is often used to improve crop productivity.This study investigated whether and how modifying leaf color alters source-sink relationships and whether current crop cultivation practices remain applicable for leaf-color modified genotypes.Periodically collected data of total biomass and nitrogen(N)accumulation in rice genotypes of four genetic backgrounds and their leaf-color modified variants(greener or yellower)were analyzed,using a recently established modelling method to quantify the source-sink(im)balance during grain filling.Among all leaf-color variants,only one yellower-leaf variant showed a higher source capacity than its normal genotype.This was associated with greater post-flowering N-uptake that prolonged the functional leaf-N duration,and this greater post-flowering N-uptake was possible because of reduced pre-flowering N-uptake.A density experiment showed that current management practices(insufficient planting density accompanied by abundant N application)are unsuitable for the yellower-leaf genotype,ultimately limiting its yield potential.Leaf-color modification affects source-sink relationships by regulating the N trade-off between pre-and post-flowering uptake,as well as N translocation between source and sink organs.To best exploit leaf-color modification for improving crop productivity,adjustments of crop management practices are required.

Analysis of GC×GC fingerprints from medicinal materials using a novel contour detection algorithm:A case of Curcuma wenyujin

This study introduces an innovative contour detection algorithm,PeakCET,designed for rapid and efficient analysis of natural product image fingerprints using comprehensive two-dimensional gas chromatogram(GC×GC).This method innovatively combines contour edge tracking with affinity propagation(AP)clustering for peak detection in GC×GC fingerprints,the first in this field.Contour edge tracking signif-icantly reduces false positives caused by“burr”signals,while AP clustering enhances detection accuracy in the face of false negatives.The efficacy of this approach is demonstrated using three medicinal products derived from Curcuma wenyujin.PeakCET not only performs contour detection but also employs inter-group peak matching and peak-volume percentage calculations to assess the compositional similarities and differences among various samples.Furthermore,this algorithm compares the GC×GC fingerprints of Radix/Rhizoma Curcumae Wenyujin with those of products from different botanical origins.The findings reveal that genetic and geographical factors influence the accumulation of secondary metabolites in various plant tissues.Each sample exhibits unique characteristic components alongside common ones,and vari-ations in content may influence their therapeutic effectiveness.This research establishes a foundational data-set for the quality assessment of Curcuma products and paves the way for the application of computer vision techniques in two-dimensional(2D)fingerprint analysis of GC×GC data.

Certis: Cloud Asset Management & Threat Evaluation Using Behavioral Fingerprinting at Application Layer

This paper introduces Certis, a powerful framework that addresses the challenges of cloud asset tracking, management, and threat detection in modern cybersecurity landscapes. It enhances asset identification and anomaly detection through SSL certificate parsing, cloud service provider integration, and advanced fingerprinting techniques like JARM at the application layer. Current work will focus on cross-layer malicious behavior identification to further enhance its capabilities, including minimizing false positives through AI-based learning techniques. Certis promises to offer a powerful solution for organizations seeking proactive cybersecurity defenses in the face of evolving threats.

What diameter?What height?Influence of measures of average tree size on area-based allometric volume relationships

Volume is an important attribute used in many forest management decisions.Data from 83 fixed-area plots located in central New Brunswick,Canada,are used to examine how different measures of stand-level diameter and height influence volume prediction using a stand-level variant of Honer's(1967)volume equation.When density was included in the models(Volume=f(Diameter,Height,Density))choice of diameter measure was more important than choice of height measure.When density was not included(Volume=f(Diameter,Height)),the opposite was true.For models with density included,moment-based estimators of stand diameter and height performed better than all other measures.For models without density,largest tree estimators of stand diameter and height performed better than other measures.The overall best equation used quadratic mean diameter,Lorey's height,and density(root mean square error=5.26 m^3·ha^(-1);1.9%relative error).The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha(root mean square error=32.08 m^(3)·ha^(-1);11.8%relative error).The results of this study have some important implications for height subsampling and LiDAR-derived forest inventory analyses.

Three anti-inflammatory polysaccharides from Lonicera japonica Thunb.:insights into the structure-function relationships

This study demonstrates the feasibility of producing three polysaccharides(neutral LJP-1,acidic LJP-2 and acidic LJP-3)with significant in vitro and in vivo anti-inflammatory activities from the flowers of Lonicera japonica.The three polysaccharides differed in chemical composition,molecular weight(Mw)distribution,glycosidic linkage pattern,functional groups and morphology.They exhibited excellent protective effects(in a dose-dependent manner)in lipopolysaccharide-injured RAW264.7 macrophages and Cu SO4-damaged zebrafish via reducing NO production and inhibiting the overexpressions of inflammation-related transcription factors,inflammatory proteins and cytokines in the NF-κB/MAPK signaling pathways.Their antiinflammatory effects varied owing to their different molecular characteristics and chemical compositions.Overall,LJP-2 at 400μg/m L was the most effective.LJP-2 consisted mainly of→5)-α-L-Araf(1→,→4)-α-LGalp A(1→and→2)-α-L-Rhap(1→residues with terminal T-β-D-Glcp.Thus,honeysuckle flowers are good sources of anti-inflammatory polysaccharides,and precise fractionation enables the production of potent antiinflammatory agents for the development of functional foods and healthcare products.

AWeb Application Fingerprint Recognition Method Based on Machine Learning

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications,thereby enhancing the detection of potential threats and attacks.Traditional fingerprint recognition methods,which rely on preannotated feature matching,face inherent limitations due to the ever-evolving nature and diverse landscape of web applications.In response to these challenges,this work proposes an innovative web application fingerprint recognition method founded on clustering techniques.The method involves extensive data collection from the Tranco List,employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction.The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm,eliminating the need for preannotated labels.By transforming web applications into feature vectors and leveraging clustering algorithms,our approach accurately categorizes diverse web applications,providing comprehensive and precise fingerprint recognition.The experimental results,which are obtained on a dataset featuring various web application types,affirm the efficacy of the method,demonstrating its ability to achieve high accuracy and broad coverage.This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage,offering a robust solution to the challenges of web application fingerprint recognition.

Fingerprint Study of Polygonati Rhizoma with Steaming and Exposing to the Sun Alternatively for Different Times

[Objectives]To explore the influence of different times of steaming and exposing to the sun on the fingerprint of Polygonati Rhizoma by studying the HPLC fingerprint of Polygonati Rhizoma processed products with different times of steaming and exposing to the sun,and to provide a basis for the determination of the best processing technology of Polygonati Rhizoma.[Methods]SETSAIL II AQ-C 18(5μm×250 mm×4.6 mm)was used as the column,the column temperature was 30℃,pure water(A)and acetonitrile(B)were eluted gradually,0-10 min,B(5%-10%),10-30 min,B(10%-35%),30-40 min,B(35%-60%),40-45 min,B(60%-100%),flow rate 1 mL/min,absorption wavelength 200 nm.[Results]The relative retained peak area RSDs of the common peaks in the precision,reproducibility and stability tests were all less than 5%.There were 17 common peaks in the fingerprint of nine batches of samples,and the retention time of Peak 2 was basically the same as that of the reference peak of 5-HMF.Peak 4 mainly existed in the chromatogram of Sample 3 to Sample 5,peaks 5 and 11 mainly existed after Sample 3,peaks 9,14 and 16 mainly existed after Sample 6,and peaks 12 and 17 mainly existed after Sample 4.[Conclusions]A total of 17 common peaks were obtained,and the Peak 2 was the designated peak,and the chemical components of each processed product were different.

Bidirectional relationship between diabetes mellitus and depression:Mechanisms and epidemiology

Diabetes mellitus and depression exhibit a complex bidirectional relationship that profoundly impacts patient health and quality of life.This review explores the physiological mechanisms,including inflammation,oxidative stress,and neu-roendocrine dysregulation,that link these conditions.Psychosocial factors such as social support and lifestyle choices also contribute significantly.Epidemiological insights reveal a higher prevalence of depression among diabetics and an in-creased risk of diabetes in depressed individuals,influenced by demographic variables.Integrated management strategies combining mental health asse-ssments and personalized treatments are essential.Future research should focus on longitudinal and multi-omics studies to deepen understanding and improve therapeutic outcomes.