An Efficient Numerical Scheme for Biological Models in the Frame of Bernoulli Wavelets

This article considers three types of biological systems:the dengue fever disease model,the COVID-19 virus model,and the transmission of Tuberculosis model.The new technique of creating the integration matrix for the Bernoulli wavelets is applied.Also,the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme(BWCM).All three models are in the form system of coupled ordinary differential equations without an exact solution.These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme.The numerical wave distributions of these governing models are obtained by solving the algebraic equations via the Newton-Raphson method.The results obtained from the developed strategy are compared to several schemes such as the Runge Kutta method,and ND solver in mathematical software.The convergence analyses are discussed through theorems.The newly implemented Bernoulli wavelet method improves the accuracy and converges when it is compared with the existing methods in the literature.

Establishment of injury models in studies of biological effects induced by microwave radiation

Microwave radiation has been widely used in various fields,such as communication,industry,medical treatment,and military applications.Microwave radiation may cause injuries to both the structures and functions of various organs,such as the brain,heart,reproductive organs,and endocrine organs,which endanger human health.Therefore,it is both theoretically and clinically important to conduct studies on the biological effects induced by microwave radiation.The successful establishment of injury models is of great importance to the reliability and reproducibility of these studies.In this article,we review the microwave exposure conditions,subjects used to establish injury models,the methods used for the assessment of the injuries,and the indicators implemented to evaluate the success of injury model establishment in studies on biological effects induced by microwave radiation.

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.

Combination of Neuro-Fuzzy Network Models with Biological Knowledge for Reconstructing Gene Regulatory Networks

Inferring gene regulatory networks from large-scale expression data is an important topic in both cellular systems and computational biology. The inference of regulators might be the core factor for understanding actual regulatory conditions in gene regulatory networks, especially when strong regulators do work significantly. In this paper, we propose a novel approach based on combining neuro-fu^zy network models with biological knowledge to infer strong regulators and interrelated fuzzy rules. The hybrid neuro-fuzzy architecture can not only infer the fuzzy rules, which are suitable for describing the regulatory conditions in regulatory nctworks＋ but also explain the meaning of nodes and weight value in the neural network. It can get useful rules automatically without lhctitious judgments. At the same time, it does not add recursive layers to the model, and the model can also strengthen the relationships among genes and reduce calculation. We use the proposed approach to reconstruct a partial gene regulatory network of yeast, The results show that this approach can work effectively.

Apple of Sodom (<em>Calatropis procera</em>) Callus Extract, a Novel Skincare Active and Its Biological Activity in Skin Models When Combined with Dead Sea Water

Background: Calotropis procera (C. procera), is an authentic plant naturally grown in the flora of Dead Sea region. Despite its toxicity, C. procera presents healing properties. However, it has not been implemented yet in cosmetics as an active ingredient. Objective: The biological effects of C. procera callus extract on skin were elucidated solely and in combination with Dead Sea water (DSW). Methods: The capability of C. procera extract to protect against skin inflammation and irritation was tested on ex vivo human skin organ culture by LPS and SDS addition respectively. Viability and cytokine secretion were evaluated. The combination of C. procera extract with Dead Sea water was tested on full thickness skin equivalents. Gene expression and relevant biochemical markers for glycolysis, hypoxia and extracellular matrix balance were tested. Results: C. procera extract exhibits a protective biological activity against skin irritation and inflammation at the biochemical level. Furthermore, a combination of C. procera extract and DSW demonstrates a potential contribution for skin wellbeing via enhance energy production, resistance to hypoxia and extracellular matrix balance. Conclusions: Topical application of C. procera callus extract might support skin balance and wellbeing at the molecular level. Hence, it is recommended for new cosmetic formulae as standalone or in combination with Dead Sea water, in the effort to achieve anti-aging bio-activity that is working beyond skin aging symptoms, especially via skin calming effects and skin energy enhancement.

Biological exposure models for oil spill impact analysis

The oil spill impact analysis (OSIA) software system has been developed to supply a tool for comprehensive, quantitative environmental impact assessments resulting from oil spills. In the system, a biological component evaluates potential effects on exposed organisms based on results from a physico chemical fates component, including the extent and characteristics of the surface slick, and dissolved and total concentrations of hydrocarbons in the water column. The component includes a particle based exposure model for migratory adult fish populations, a particle based exposure model for spawning planktonic organisms (eggs and larvae), and an exposure model for wildlife species (sea birds or marine mammals). The exposure model for migratory adult fish populations simulates the migration behaviors of fish populations migrating to or staying in their feeding areas, over wintering areas or spawning areas, and determines the acute effects (mortality) and chronic accumulation (body burdens) from the dissolved contaminant. The exposure model for spawning planktonic organisms simulates the release of eggs and larvae, also as particles, from specific spawning areas during the spawning period, and determines their potential exposure to contaminants in the water or sediment. The exposure model for wild species calculates the exposure to surface oil of wildlife (bird and marine mammal) categories inhabiting the contaminated area. Compared with the earlier models in which all kinds of organisms are assumed evenly and randomly distributed, the updated biological exposure models can more realistically estimate potential effects on marine ecological system from oil spill pollution events.

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.

Prediction of tumor biological characteristics in different colorectal cancer liver metastasis animal models using^(18)F-FDG and^(18)F-FLT

Background: Positron emission tomography(PET) is a noninvasive method to characterize different metabolic activities of tumors, providing information for staging, prognosis, and therapeutic response of patients with cancer. The aim of this study was to evaluate the feasibility of18F-fludeoxyglucose(18F-FDG) and 3’-deoxy-3’-18F-fluorothymidine(18F-FLT) PET in predicting tumor biological characteristics of colorectal cancer liver metastasis.Methods: The uptake rate of18F-FDG and18F-FLT in SW480 and SW620 cells was measured via an in vitro cell uptake assay. The region of interest was drawn over the tumor and liver to calculate the maximum standardized uptake value ratio(tumor/liver) from PET images in liver metastasis model. The correlation between tracer uptake in liver metastases and VEGF, Ki67 and CD44 expression was evaluated by linear regression.Results: Compared to SW620 tumor-bearing mice, SW480 tumor-bearing mice presented a higher rate of liver metastases. The uptake rate of18F-FDG in SW480 and SW620 cells was 6.07% ± 1.19% and2.82% ± 0.15%, respectively(t = 4.69, P = 0.04); that of18F-FLT was 24.81% ± 0.45% and 15.57% ± 0.66%, respectively(t = 19.99, P < 0.001). Micro-PET scan showed that all parameters of FLT were significantly higher in SW480 tumors than those in SW620 tumors. A moderate relationship was detected between metastases in the liver and18F-FLT uptake in primary tumors(r = 0.73, P = 0.0019).18F-FLT uptake was also positively correlated with the expression of CD44 in liver metastases(r = 0.81, P = 0.0049).Conclusions: The uptake of18F-FLT in metastatic tumor reflects different biological behaviors of colon cancer cells.18F-FLT can be used to evaluate the metastatic potential of colorectal cancer in nude mice.

Biological soil crusts and their potential applications in the sand land over Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau is now experiencing ecological degradation risks as a result of climate change and human activities.The alpine grassland ecology in permafrost zones is fragile and susceptible to deterioration due to its high altitude,low temperature,and limited oxygen,which complicates the repair of damaged land.Biological soil crusts(BSCs)are crucial for land restoration in plateau regions because they can thrive in harsh conditions and have environmentally beneficial traits.Inoculated biological soil crust(IBSC)has shown success in low-altitude desert regions,but may not be easily duplicated to the plateau environment.Therefore,it is essential to do a comprehensive and multifaceted analysis of the basic theoretical comprehension and practical application of BSCs on the Tibetan Plateau.This review article aims to provide a brief summary of the ecological significance and the mechanisms related to the creation,growth,and progression of BSCs.It discusses the techniques used for cultivating BSCs in laboratories and using them in the field,focusing on the Qinghai-Tibet Plateau circumstance.We thoroughly discussed the potential and the required paths for further studies.This study may be used as a basis for selecting suitable microbial strains and accompanying supplemental actions for implementing IBSCs in the Qinghai-Tibet Plateau.

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.

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.

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.

Chronic Hepatitis B Virus Infection: Biological Parameters in Patients Treated with Tenofovir Disoproxil Fumarate

Chronic hepatitis B causes a liver disease characterized by inflammation of the liver parenchyma. The aim of this study was to investigate the evolution of biological parameters in patients treated with Tenofovir for chronic B infection at the Commune V referral health center in Bamako. We obtained a prevalence of 14.15%. The most represented age group was 31 - 40 years, with 36.8%. The sex ratio was 1.44 in favour of men. Viral load was undetectable after 18 months of treatment in 25 patients (42.37%). Tenofovir, the 1st-line drug in Mali, is effective on the biological parameters monitored in patients.

Secondary Metabolites of Entomopathogenic Fungi, Biological Alternative for the Control of Agricultural Pests and Disease: Present and Perspectives

The use of entomopathogenic fungi (EF) in recent years has been highly effective against the different orders of insects considered pests of agricultural importance and their conidia have been commonly applied, but it has been reported that these are sensitive to the environmental conditions. For this reason, biopesticides products have been formulated based on secondary metabolites, recently. These biomolecules participate as biological control agent, such as: cyclic depsipeptides, amino acids, polyketides, polyphenols and terpenoids, affecting their morphology, life cycle and insect behavior. The use of secondary metabolites of entomopathogenic fungi opens the possibility of application in a more efficient way for the control of agricultural pests in a compatible with the environment and human health;therefore, it is important to know, analyzing the type of molecules, their effects, and their different methods of application.

The biological functions and metabolic pathways of valine in swine

Valine is an essential amino acid and a type of branched-chain amino acid. Due to the involvement of branchedchain amino acids in various metabolic pathways, there has been a surge of interests in valine nutrition and its role in animal physiology. In pigs, the interactions between valine and other branched-chain amino acids or aromatic amino acids are complex. In this review, we delve into the interaction mechanism, metabolic pathways, and biological functions of valine. Appropriate valine supplementation not only enhances growth and reproductive performances, but also modulates gut microbiota and immune functions. Based on past observations and interpretations, we provide recommended feed levels of valine for weaned piglets, growing pigs, gilts, lactating sows, barrows and entire males. The summarized valine nutrient requirements for pigs at different stages offer valuable insights for future research and practical applications in animal husbandry.

Evaluation of the Efficacy of an Aloe barbadensis Based Biological Insecticide against Pests of Abelmochus esculentus for Promoting Ecological Agriculture (Far-North, Cameroon)

Chemical insecticides have been considered as a means to combat crop pests. Although their effectiveness is evident, their impact on the environment is increasingly being discussed. The aim of this study is to determine the agro-ecological potential of a biological insecticide (C25H32O12) based on Aloe barbadensis in a Sahelian context. For this purpose, a completely randomized block experimental design with 3 replications and 4 treatments was set up to experiment with Aloe barbadensis as a bioinsecticide against pests of Abelmoschus esculentus. However, data were collected using an observation and parameter monitoring grid. This includes the cultivation of Abelmoschus esculentus, soil preparation, seeding and watering, plot labeling, preparation of the bioinsecticide (selection and preparation of raw materials, grinding of Aloe barbadensis miller and extraction of the crude bioinsecticide, quantification of treatment doses and dilution, and obtaining the formulated bioinsecticide), plant watering, plant treatment, and finally parameter monitoring. The results obtained reveal that the level of damage is significantly high in the control treatment T0 (63%) compared to the other treatments, with 29% for treatment T1, 7% for T2, and 1% for T3, implying a strong action capability of this insecticide against pests of Abelmoschus esculentus. Therefore, it can be concluded that for a normal growing season of Abelmoschus esculentus, this biological insecticide should be sprayed 12 times. Furthermore, this biological insecticide is unique in that it does not inflict any gastric toxicity on the pests, which gives it the characteristic of being a repellent. It is a biological insecticide whose efficacy period has been tested, with a minimum duration of 21 days. In conclusion, this formulated bioinsecticide based on Aloe barbadensis demonstrates significant efficacy against pests of Abelmoschus esculentus. In the future, we will consider experimenting with its effectiveness against pests of other plants.

Comparative proteomics reveals the response and adaptation mechanisms of white Hypsizygus marmoreus against the biological stress caused by Penicillium

White Hypsizygus marmoreus is a popular edible mushroom.Its mycelium is easy to be contaminated by Penicillium,which leads to a decrease in its quality and yield.Penicillium could compete for limited space and nutrients through rapid growth and produce a variety of harmful gases,such as benzene,aldehydes,phenols,etc.,to inhibit the growth of H.marmoreus mycelium.A series of changes occurred in H.marmoreus proteome after contamination when detected by the label-free tandem mass spectrometry(MS/MS)technique.Some proteins with up-regulated expression worked together to participate in some processes,such as the non-toxic transformation of harmful gases,glutathione metabolism,histone modification,nucleotide excision repair,clearing misfolded proteins,and synthesizing glutamine,which were mainly used in response to biological stress.The proteins with down-regulated expression are mainly related to the processes of ribosome function,protein processing,spliceosome,carbon metabolism,glycolysis,and gluconeogenesis.The reduction in the function of these proteins affected the production of the cell components,which might be an adjustment to adapt to growth retardation.This study further enhanced the understanding of the biological stress response and the growth restriction adaptation mechanisms in edible fungi.It also provided a theoretical basis for protein function exploration and edible mushroom food safety research.

Study of the Constraints of Millet Production (Pennisetum glaucum (L.) R. Br.) and the Peasant Perception of Biological Control in the Tahoua Region

Millet (Pennisetum glaucum (L.) R. Br.) is the Sahelian crop par excellence due to its adaptation to the particular production conditions in this region. Unfortunately, in recent years this crop has been threatened by very strong parasitic pressure and drought during the production period. The objective of this study is to analyze the main constraints of millet production and the solutions known to producers. A survey was carried out in November 2022 with a sample of 298 producers in five municipalities in the Tahoua region. The main constraints are drought and pressure from crop pests (locust, millet ear miner, floricultural insects) according to 57.9% of respondents. The millet ear miner is the most formidable pest according to 55% of respondents. Thus, the average yield obtained in a year of good production without the leafminer is 194 kg/ha and that obtained in a year of millet ear leafminer is around 27 kg to 43 kg/ha depending on the municipality. The yield obtained this last campaign after the attack of this leafminer varies from 64 to 77 kg/ha depending on the municipalities compared to a potential yield of over 1000 kg/ha. More than half of producers (58.1%) are unaware of the existence of biological control compared to only 12.5% who are aware of this alternative method. Work to popularize this technology is necessary in the five municipalities and the entire region in general.