Decoding molecular mechanisms:brain aging and Alzheimer's disease

The complex morphological,anatomical,physiological,and chemical mechanisms within the aging brain have been the hot topic of research for centuries.The aging process alters the brain structure that affects functions and cognitions,but the worsening of such processes contributes to the pathogenesis of neurodegenerative disorders,such as Alzheimer's disease.Beyond these observable,mild morphological shifts,significant functional modifications in neurotransmission and neuronal activity critically influence the aging brain.Understanding these changes is important for maintaining cognitive health,especially given the increasing prevalence of age-related conditions that affect cognition.This review aims to explore the age-induced changes in brain plasticity and molecular processes,differentiating normal aging from the pathogenesis of Alzheimer's disease,thereby providing insights into predicting the risk of dementia,particularly Alzheimer's disease.

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image processing.Medical science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous diseases.Key to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and treatment.This study makes a small contribution to endoscopic image classification.The proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and Xception.Additionally,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature vectors.The fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)classifier.The Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were used.Performance assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private dataset.This approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these systems.In this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly detection.On the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,respectively.The results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.

Veratrilla baillonii Franch alleviate the symptoms of diabetes in type 2 diabetic rats induced by high-fat diet and streptozotocin

Our previous research studies have shown that Veratrilla baillonii Franch,a food supplement used by ethnic minorities in Southwest China,has multiple pharmacological activities,such as detoxification,antiinflammatory,antioxidant,and anti-insulin resistance.However,the detailed signal pathways for its salutary effect on damages in multiple organs due to type 2 diabetes mellitus(T2DM)remains unclear.The current study is to evaluate the therapeutic effects of V.baillonii on T2DM rats and to explore the underlying mechanisms.The T2DM rat model was successfully established by a high-sugar and high-fat diet(HFD)combination with intraperitoneal injection of a small dose of streptozotocin(STZ,35 mg/kg).Biochemical analysis and histopatholgical examinations were conducted to evaluate the anti-diabetic potential of water extracts of V.baillonii(WVBF).The results showed that the WVBF treatment can improve hyperglycemia and insulin resistance,ameliorate the liver,kidney and pancreas injuries via decreasing inflammatory cytokines such as IL-6 and TNF-α,and oxidative damages.Further investigation suggested that WVBF modulates the signal transductions of the IRS1/PI3K/AKT/GLUT4 and AMPK pathways.These findings demonstrate potentials of WVBF in the treatment of T2DM and possible mechanisms for its hepatoprotective activities.

Optimizing Bearing Fault Detection:CNN-LSTM with Attentive TabNet for Electric Motor Systems

Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.

A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

Performance Analysis of ZF and RZF in Low-Resolution ADC/DAC Massive MIMO Systems

Large number of antennas and higher bandwidth usage in massive multiple-input-multipleoutput(MIMO)systems create immense burden on receiver in terms of higher power consumption.The power consumption at the receiver radio frequency(RF)circuits can be significantly reduced by the application of analog-to-digital converter(ADC)of low resolution.In this paper we investigate bandwidth efficiency(BE)of massive MIMO with perfect channel state information(CSI)by applying low resolution ADCs with Rician fadings.We start our analysis by deriving the additive quantization noise model,which helps to understand the effects of ADC resolution on BE by keeping the power constraint at the receiver in radar.We also investigate deeply the effects of using higher bit rates and the number of BS antennas on bandwidth efficiency(BE)of the system.We emphasize that good bandwidth efficiency can be achieved by even using low resolution ADC by using regularized zero-forcing(RZF)combining algorithm.We also provide a generic analysis of energy efficiency(EE)with different options of bits by calculating the energy efficiencies(EE)using the achievable rates.We emphasize that satisfactory BE can be achieved by even using low-resolution ADC/DAC in massive MIMO.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

Slip Effects on Casson Nanofluid over a Stretching Sheet with Activation Energy:RSM Analysis

The current study is dedicated to presenting the Casson nanofluid over a stretching surface with activation energy.In order to make the problem more realistic,we employed magnetic field and slip effects on fluid flow.The governing partial differential equations(PDEs)were converted to ordinary differential equations(ODEs)by similarity variables and then solved numerically.The MATLAB built-in command‘bvp4c’is utilized to solve the system of ODEs.Central composite factorial design based response surface methodology(RSM)is also employed for optimization.For this,quadratic regression is used for data analysis.The results are concluded bymeans of tables and pictorial representations.The present study discloses that the temperature profile increases with enhancement in Ha,Nr,Nb,and Nt and it shows opposite behavior forλ.The included parameters show same trend for heat transfer rate(Nux).It is also concluded thatδshould bemaximum for any value ofNb and Nt to maximize the heat transfer rate.

Algorithm for Visualization of Zero Divisor Graphs of the Ring ℤn Using MAPLE Coding

This research investigates the comparative efficacy of generating zero divisor graphs (ZDGs) of the ring of integers ℤn modulo n using MAPLE algorithm. Zero divisor graphs, pivotal in the study of ring theory, depict relationships between elements of a ring that multiply to zero. The paper explores the development and implementation of algorithms in MAPLE for constructing these ZDGs. The comparative study aims to discern the strengths, limitations, and computational efficiency of different MAPLE algorithms for creating zero divisor graphs offering insights for mathematicians, researchers, and computational enthusiasts involved in ring theory and mathematical computations.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Enhancing Communication Accessibility:UrSL-CNN Approach to Urdu Sign Language Translation for Hearing-Impaired Individuals

Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

Rice-wheat comparative genomics:Gains and gaps

Rice and wheat provide nearly 40%of human calorie and protein requirements.They share a common ancestor and belong to the Poaceae(grass)family.Characterizing their genetic homology is crucial for developing new cultivars with enhanced traits.Several wheat genes and gene families have been characterized based on their rice orthologs.Rice–wheat orthology can identify genetic regions that regulate similar traits in both crops.Rice–wheat comparative genomics can identify candidate wheat genes in a genomic region identified by association or QTL mapping,deduce their putative functions and biochemical pathways,and develop molecular markers for marker-assisted breeding.A knowledge of gene homology facilitates the transfer between crops of genes or genomic regions associated with desirable traits by genetic engineering,gene editing,or wide crossing.

Diabetes and Hypertension Are Associated with Food Insecurity in a Cameroonian Population: A Case-Control Study

Diabetes and hypertension are the most prevalent cardiovascular risk factors. Recent studies showed an increase in the prevalence of food insecurity in our country. The aim of this study was to assess how food insecurity affects the dietary habits, socio-demographic characteristics and metabolic profile of individuals with diabetes or hypertension. This case-control study was conducted among diabetic and hypertensive participants (cases) and diabetic and hypertensive normal (controls) during the screening campaigns for nutrition-related chronic diseases. The sociodemographic, clinical and biochemical parameters of the participants were analyzed. Logistic regression analyses were performed to identify factors associated with diabetes and hypertension in the study population. Bivariate analyses showed that male gender (OR = 1.972;95% CI: 1.250 - 3.089), regular alcohol consumption (OR = 2.012;95% CI: 1.294 - 3.130), low fruit consumption (OR = 1.590;95% CI: 1.016 - 2.488), low dietary diversity (OR = 2.915;95% CI: 1.658 - 5.127) and abdominal obesity (OR = 1.893, CI 95% 1.203 - 2.978) were significantly associated with hypertension. In addition, low fruit consumption (OR = 1.829;95% CI 1.092 - 3.064), low legume consumption (OR = 3.515;95% CI 1.861 - 6.635), and hypertriglyceridaemia (OR = 2.241, 95% CI 1.139 - 4.408) were significantly associated with diabetes. The indirect association observed between food insecurity and diabetes and hypertension suggests the need for nutritional policies aimed at popularizing the production and consumption of fruits and legumes. Similarly, health services need to be aware and informed of the important role that food insecurity can play in the development of diabetes and hypertension.

Roles of Mutant TP53 Gene in Cancer Development and Progression

TP53 is a tumor suppressor gene that is mutated in most cancer types and has been extensively studied in cancer research.p53 plays a critical role in regulating the expression of target genes and is involved in key processes such as apoptosis,cell cycle regulation,and genomic stability,earning it the title“guardian of the genome.”Numerous studies have demonstrated p53’s influence on and regulation of autophagy,ferroptosis,the tumor microenvironment,and cell metabolism,all of which contribute to tumor suppression.Alterations in p53,specifically mutant p53(mutp53),not only impair its tumor-suppressing functions but also enhance oncogenic characteristics.Recent data indicate that mutp53 is strongly associated with poor prognosis and advanced cancers,making it an ideal target for the development of novel cancer therapies.This review summarizes the post-translational modifications of p53,the mechanisms of mutp53 accumulation,and its gain-of-function,based on previous findings.Additionally,this review discusses its impact on metabolic homeostasis,ferroptosis,genomic instability,the tumor microenvironment,and cancer stem cells,and highlights recent advancements in mutp53 research.

Stem Cells and Exosomes-associated Therapeutic Applications

Exosomes are extracellular vesicles with sizes from 30 to 150 nm in diameter and modulate the transport of multiple intracellular biological molecules including proteins,nucleic acids,lipids,and metabolites.They regulate a large number of cells and are involved in different pathological and physiological activities including carcinogenesis,viral infection,cell-cell communication and immune responses as well.Stem cell-derived exosomes carry many benefits over simple stem cells in the form of easy access,freedom from tumourigenic capabilities,non-infusion toxicity,effortless preservation,and immunogenicity.Exosomes have almost the same properties and perform functions effectively in the same way as their parental cells do like adult stem cells and embryonic stem cells.Due to their pluripotent or multipotent abilities,stem cells(SCs)transform into several types of cells.In addition to other secretions,SC also give exosomes,which in turn shows therapeutic significance for many disorders,including cancer,diabetes mellitus,skin allergies and regenerative medicine.Exosomes originating from mesenchymal stem cells(MSCs)have miRNAs,lipids,and proteins that trigger diabetes and cancer situations in humans.Exosomes from SCs(sc-exos)are preferred to SC as there are fewer side effects and other challenges,including effectiveness,drug delivery,lower immunogenicity and tumourigenicity.In the current review,we summarize the data from the last 5 years'articles about exosomes and stem cell-derived microvesicles for the therapeutic potential of various diseases such as cancer,Alzheimer's disease,diabetes,and Parkinson's disease with clinical challenges and future aspects.

Exploring the Pivotal Association of AI in Cancer Stem Cells Detection and Treatment

Cancer stem cells(CSCs),or tumor-initiating cells(TICs),are cancerous cell subpopulations that remain while tumor cells propagate as a unique subset and exhibit multiple applications in several diseases.They are responsible for cancer cell initiation,development,metastasis,proliferation,and recurrence due to their self-renewal and differentiation abilities in many kinds of cells.Artificial intelligence(AI)has gained significant attention because of its vast applications in various fields including agriculture,healthcare,transportation,and robotics,particularly in detecting human diseases such as cancer.The division and metastasis of cancerous cells are not easy to identify at early stages due to their uncontrolled situations.It has provided some real-time pictures of cancer progression and relapse.The purpose of this review paper is to explore new investigations into the role of AI in cancer stem cell progression and metastasis and in regenerative medicines.It describes the association of machine learning and AI with CSCs along with its numerous applications from cancer diagnosis to therapy.This review has also provided key challenges and future directions of AI in cancer stem cell research diagnosis and therapeutic approach.

Analysis of Genetic Alterations in TP53 Gene in Breast Cancer - A Secondary Publication

Tumor protein p53 (TP53) mediates DNA repair and cell proliferation in growing cells. The TP53 gene is a tumor suppressor that regulates the expression of target genes in response to multiple cellular stress factors. Key target genes are involved in crucial cellular events such as DNA repair, cell cycle regulation, apoptosis, metabolism, and senescence. TP53 genetic variants and the activity of the wild-type p53 protein (WT-p53) have been linked to a wide range of tumorigenesis. Various genetic and epigenetic alterations, including germline and somatic mutations, loss of heterozygosity, and DNA methylation, can alter TP53 activity, potentially resulting in cancer initiation and progression. This study was designed to screen three reported mutations in the DNA-binding domain of the p53 protein in breast cancer, to evaluate the relative susceptibility and risk associated with breast cancer in the local population. Genomic DNA was isolated from 30 breast tumor tissues along with controls. Tetra and Tri ARMS PCR were performed to detect mutations in the TP53 coding region. For SNPs c.637C>T and c.733C>T, all analyzed cases were homozygous for the wild-type allele ‘C,’ while for SNP c.745A>G, all cases were homozygous for the wild-type allele ‘A.’ These results indicate no relevance of these three SNPs to cancer progression in our study cohort. Additionally, the findings from whole exon sequencing will help to predict more precise outcomes and assess the importance of TP53 gene mutations in breast cancer patients.