Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Assessing the possibility of using large language models in ocular surface diseases

AIM:To assess the possibility of using different large language models(LLMs)in ocular surface diseases by selecting five different LLMS to test their accuracy in answering specialized questions related to ocular surface diseases:ChatGPT-4,ChatGPT-3.5,Claude 2,PaLM2,and SenseNova.METHODS:A group of experienced ophthalmology professors were asked to develop a 100-question singlechoice question on ocular surface diseases designed to assess the performance of LLMs and human participants in answering ophthalmology specialty exam questions.The exam includes questions on the following topics:keratitis disease(20 questions),keratoconus,keratomalaciac,corneal dystrophy,corneal degeneration,erosive corneal ulcers,and corneal lesions associated with systemic diseases(20 questions),conjunctivitis disease(20 questions),trachoma,pterygoid and conjunctival tumor diseases(20 questions),and dry eye disease(20 questions).Then the total score of each LLMs and compared their mean score,mean correlation,variance,and confidence were calculated.RESULTS:GPT-4 exhibited the highest performance in terms of LLMs.Comparing the average scores of the LLMs group with the four human groups,chief physician,attending physician,regular trainee,and graduate student,it was found that except for ChatGPT-4,the total score of the rest of the LLMs is lower than that of the graduate student group,which had the lowest score in the human group.Both ChatGPT-4 and PaLM2 were more likely to give exact and correct answers,giving very little chance of an incorrect answer.ChatGPT-4 showed higher credibility when answering questions,with a success rate of 59%,but gave the wrong answer to the question 28% of the time.CONCLUSION:GPT-4 model exhibits excellent performance in both answer relevance and confidence.PaLM2 shows a positive correlation(up to 0.8)in terms of answer accuracy during the exam.In terms of answer confidence,PaLM2 is second only to GPT4 and surpasses Claude 2,SenseNova,and GPT-3.5.Despite the fact that ocular surface disease is a highly specialized discipline,GPT-4 still exhibits superior performance,suggesting that its potential and ability to be applied in this field is enormous,perhaps with the potential to be a valuable resource for medical students and clinicians in the future.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Progress in experimental models to investigate the in vivo and in vitro antidiabetic activity of drugs

Diabetes mellitus is one of the world's most prevalent and complex metabolic disorders,and it is a rapidly growing global public health issue.It is characterized by hyperglycemia,a condition involving a high blood glucose level brought on by deficiencies in insulin secretion,decreased activity of insulin,or both.Prolonged effects of diabetes include cardiovascular problems,retinopathy,neuropathy,nephropathy,and vascular alterations in both macro-and micro-blood vessels.In vivo and in vitro models have always been important for investigating and characterizing disease pathogenesis,identifying targets,and reviewing novel treatment options and medications.Fully understanding these models is crucial for the researchers so this review summarizes the different experimental in vivo and in vitro model options used to study diabetes and its consequences.The most popular in vivo studies involves the small animal models,such as rodent models,chemically induced diabetogens like streptozotocin and alloxan,and the possibility of deleting or overexpressing a specific gene by knockout and transgenic technologies on these animals.Other models include virally induced models,diet/nutrition induced diabetic animals,surgically induced models or pancreatectomy models,and non-obese models.Large animals or non-rodent models like porcine(pig),canine(dog),nonhuman primate,and Zebrafish models are also outlined.The in vitro models discussed are murine and human beta-cell lines and pancreatic islets,human stem cells,and organoid cultures.The other enzymatic in vitro tests to assess diabetes include assay of amylase inhibition and inhibition ofα-glucosidase activity.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Effects of mesenchymal stem cell on dopaminergic neurons,motor and memory functions in animal models of Parkinson's disease:a systematic review and meta-analysis

Parkinson’s disease is chara cterized by the loss of dopaminergic neurons in the substantia nigra pars com pacta,and although restoring striatal dopamine levels may improve symptoms,no treatment can cure or reve rse the disease itself.Stem cell therapy has a regenerative effect and is being actively studied as a candidate for the treatment of Parkinson’s disease.Mesenchymal stem cells are considered a promising option due to fewer ethical concerns,a lower risk of immune rejection,and a lower risk of teratogenicity.We performed a meta-analysis to evaluate the therapeutic effects of mesenchymal stem cells and their derivatives on motor function,memory,and preservation of dopamine rgic neurons in a Parkinson’s disease animal model.We searched bibliographic databases(PubMed/MEDLINE,Embase,CENTRAL,Scopus,and Web of Science)to identify articles and included only pee r-reviewed in vivo interve ntional animal studies published in any language through J une 28,2023.The study utilized the random-effect model to estimate the 95%confidence intervals(CI)of the standard mean differences(SMD)between the treatment and control groups.We use the systematic review center for laboratory animal expe rimentation’s risk of bias tool and the collaborative approach to meta-analysis and review of animal studies checklist for study quality assessment.A total of 33studies with data from 840 Parkinson’s disease model animals were included in the meta-analysis.Treatment with mesenchymal stem cells significantly improved motor function as assessed by the amphetamine-induced rotational test.Among the stem cell types,the bone marrow MSCs with neurotrophic factor group showed la rgest effect size(SMD[95%CI]=-6.21[-9.50 to-2.93],P=0.0001,I^(2)=0.0%).The stem cell treatment group had significantly more tyrosine hydroxylase positive dopamine rgic neurons in the striatum([95%CI]=1.04[0.59 to 1.49],P=0.0001,I^(2)=65.1%)and substantia nigra(SMD[95%CI]=1.38[0.89 to 1.87],P=0.0001,I^(2)=75.3%),indicating a protective effect on dopaminergic neurons.Subgroup analysis of the amphetamine-induced rotation test showed a significant reduction only in the intracranial-striatum route(SMD[95%CI]=-2.59[-3.25 to-1.94],P=0.0001,I^(2)=74.4%).The memory test showed significant improvement only in the intravenous route(SMD[95%CI]=4.80[1.84 to 7.76],P=0.027,I^(2)=79.6%).Mesenchymal stem cells have been shown to positively impact motor function and memory function and protect dopaminergic neurons in preclinical models of Parkinson’s disease.Further research is required to determine the optimal stem cell types,modifications,transplanted cell numbe rs,and delivery methods for these protocols.

Knee osteoarthritis:A review of animal models and intervention of traditional Chinese medicine

Background:Knee osteoarthritis(KOA)characterized by degeneration of knee cartilage and subsequent bone hyperplasia is a prevalent joint condition primarily affecting aging adults.The pathophysiology of KOA remains poorly understood,as it involves complex mechanisms that result in the same outcome.Consequently,researchers are interested in studying KOA and require appropriate animal models for basic research.Chinese herbal compounds,which consist of multiple herbs with diverse pharmacological properties,possess characteristics such as multicomponent,multipathway,and multitarget effects.The potential benefits in the treatment of KOA continue to attract attention.Purpose:This study aims to provide a comprehensive overview of the advantages,limitations,and specific considerations in selecting different species and methods for KOA animal models.This will help researchers make informed decisions when choosing an animal model.Methods:Online academic databases(e.g.,PubMed,Google Scholar,Web of Science,and CNKI)were searched using the search terms“knee osteoarthritis,”“animal models,”“traditional Chinese medicine,”and their combinations,primarily including KOA studies published from 2010 to 2023.Results:Based on literature retrieval,this review provides a comprehensive overview of the methods of establishing KOA animal models;introduces the current status of advantages and disadvantages of various animal models,including mice,rats,rabbits,dogs,and sheep/goats;and presents the current status of methods used to establish KOA animal models.Conclusion:This study provides a review of the animal models used in recent KOA research,discusses the common modeling methods,and emphasizes the role of traditional Chinese medicine compounds in the treatment of KOA.

Neurophysiological, histological, and behavioral characterization of animal models of distraction spinal cord injury: a systematic review

Distraction spinal cord injury is caused by some degree of distraction or longitudinal tension on the spinal cord and commonly occurs in patients who undergo corrective operation for severe spinal deformity.With the increased degree and duration of distraction,spinal cord injuries become more serious in terms of their neurophysiology,histology,and behavior.Very few studies have been published on the specific characteristics of distraction spinal cord injury.In this study,we systematically review 22 related studies involving animal models of distraction spinal cord injury,focusing particularly on the neurophysiological,histological,and behavioral characteristics of this disease.In addition,we summarize the mechanisms underlying primary and secondary injuries caused by distraction spinal cord injury and clarify the effects of different degrees and durations of distraction on the primary injuries associated with spinal cord injury.We provide new concepts for the establishment of a model of distraction spinal cord injury and related basic research,and provide reference guidelines for the clinical diagnosis and treatment of this disease.

Immunobiology of COVID-19: Mechanistic and therapeutic insights from animal models

The distribution of the immune system throughout the body complicates in vitro assessments of coronavirus disease 2019(COVID-19)immunobiology,often resulting in a lack of reproducibility when extrapolated to the whole organism.Consequently,developing animal models is imperative for a comprehensive understanding of the pathology and immunology of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.This review summarizes current progress related to COVID-19 animal models,including non-human primates(NHPs),mice,and hamsters,with a focus on their roles in exploring the mechanisms of immunopathology,immune protection,and long-term effects of SARS-CoV-2 infection,as well as their application in immunoprevention and immunotherapy of SARS-CoV-2 infection.Differences among these animal models and their specific applications are also highlighted,as no single model can fully encapsulate all aspects of COVID-19.To effectively address the challenges posed by COVID-19,it is essential to select appropriate animal models that can accurately replicate both fatal and non-fatal infections with varying courses and severities.Optimizing animal model libraries and associated research tools is key to resolving the global COVID-19 pandemic,serving as a robust resource for future emerging infectious diseases.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discrete Cosine Transform(DCT),and Fast Fourier Transform(FFT)are considered and compared for the image communication task.The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels,while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels.

U-Net Models for Representing Wind Stress Anomalies over the Tropical Pacific and Their Integrations with an Intermediate Coupled Model for ENSO Studies

El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.