Profile Model Research Articles

Beyond the screen: Exploring the dynamics of social media influencers, digital food marketing, and gendered influences on adolescent diets.

Adolescent obesity remains a public health concern, exacerbated by unhealthy food marketing, particularly on digital platforms. Social media influencers are increasingly utilized in digital marketing, yet their impact remains understudied. This research explores the frequency of posts containing food products/brands, the most promoted food categories, the healthfulness of featured products, and the types of marketing techniques used by social media influencers popular with male and female adolescents. By analyzing these factors, the study aims to provide a deeper understanding of how social media influencer marketing might contribute to dietary choices and health outcomes among adolescents, from a gender perspective, shedding light on an important yet underexplored aspect of food marketing. A content analysis was conducted on posts made between June 1, 2021, and May 31, 2022, that were posted by the top three social media influencers popular with males and female adolescents (13-17) on Instagram, TikTok, and YouTube (N = 1373). Descriptive statistics were used to calculate frequencies for posts containing food products/brands, promoted food categories, product healthfulness, and marketing techniques. Health Canada's Nutrient Profile Model was used to classify products as either healthy or less healthy based on their content in sugar, sodium, and saturated fats. Influencers popular with males featured 1 food product/brand for every 2.5 posts, compared to 1 for every 6.1 posts for influencers popular with females. Water (27% of posts) was the primary food category for influencers popular with females, while restaurants (24% of posts) dominated for males. Influencers popular with males more commonly posted less healthy food products (89% vs 54%). Marketing techniques varied: influencers popular with females used songs or music (53% vs 26%), other influencers (26% vs 11%), appeals to fun or coolness (26% vs 13%), viral marketing (29% vs 19%), and appeals to beauty (11% vs 0%) more commonly. Influencers popular with males more commonly used calls-to-action (27% vs 6%) and price promotions (8% vs 1%). Social media influencers play a role in shaping adolescents' dietary preferences and behaviors. Understanding gender-specific dynamics is essential for developing targeted interventions, policies, and educational initiatives aimed at promoting healthier food choices among adolescents. Policy efforts should focus on regulating unhealthy food marketing, addressing gender-specific targeting, and fostering a healthy social media environment for adolescents to support healthier dietary patterns.

Comparative transcriptomics reveal stage-dependent parasitic adaptations in the myxozoan Sphaerospora molnari

BackgroundParasitism as a life strategy has independently evolved multiple times within the eukaryotic tree of life. Each lineage has developed mechanisms to invade hosts, exploit resources, and ensure replication, but our knowledge of survival mechanisms in many parasitic taxa remain extremely limited. One such group is the Myxozoa, which are obligate, dixenous cnidarians. Evidence suggests that myxozoans evolved from free-living ancestors to endoparasites around 600 million years ago and are likely one of the first metazoan parasites on Earth. Some myxozoans pose significant threats to farmed and wild fish populations, negatively impacting aquaculture and fish stocks; one such example is Sphaerospora molnari, which forms spores in the gills of common carp (Cyprinus carpio), disrupting gill epithelia and causing somatic and respiratory failure. Sphaerospora molnari undergoes sequential development in different organs of its host, with large numbers of morphologically distinct stages occurring in the blood, liver, and gills of carp. We hypothesize that these parasite life-stages differ in regards to their host exploitation, pathogenicity, and host immune evasion strategies and mechanisms. We performed stage-specific transcriptomic profiling to identify differentially expressed key functional gene groups that relate to these functions and provide a fundamental understanding of the mechanisms S. molnari uses to optimize its parasitic lifestyle. We aimed to identify genes that are likely related to parasite pathogenicity and host cell exploitation mechanisms, and we hypothesize that genes unique to S. molnari might be indicative of evolutionary innovations and specific adaptations to host environments.ResultsWe used parasite isolation protocols and comparative transcriptomics to study early proliferative and spore-forming stages of S. molnari, unveiling variation in gene expression between each stage. We discovered several apparent innovations in the S. molnari transcriptome, including proteins that are likely to function in the uptake of previously unknown key nutrients, immune evasion factors that may contribute to long-term survival in hosts, and proteins that likely improve adhesion to host cells that may have arisen from horizontal gene transfer. Notably, we identified genes that are similar to known virulence factors in other parasitic organisms, particularly blood and intestinal parasites like Plasmodium, Trypanosoma, and Giardia. Many of these genes are absent in published cnidarian and myxozoan datasets and appear to be specific to S. molnari; they may therefore represent potential innovations enabling Sphaerospora to exploit the host’s blood system.ConclusionsIn order to address the threat posed by myxozoans to both cultured fish species and wild stocks, it is imperative to deepen our understanding of their genetics. Sphaerospora molnari offers an appealing model for stage-specific transcriptomic profiling and for identifying differentially expressed key functional gene groups related to parasite development. We identified genes that are thus far unique to S. molnari, which reveal their evolutionary novelty and likely role as adaptations to specific host niches. In addition, we describe the pathogenicity-associated genetic toolbox of S. molnari and discuss the implications of our discoveries for disease control by shedding light on specific targets for potential intervention strategies.

Advances in the Pathogenesis, Diagnosis, Treatment, and Prognosis of Marginal Zone Lymphoma.

The management of marginal zone lymphoma (MZL), an indolent B-cell non-Hodgkin lymphoma, requires a personalized and adaptive approach due to its clinical and prognostic heterogeneity. We believe treatment should emphasize a balanced strategy considering the subtype, disease burden, symptoms, and actionable genetic or environmental factors, such as infections or autoimmune diseases. For asymptomatic patients with low tumor burden or disseminated disease, a watch-and-wait approach remains appropriate, given MZL's indolent nature and the risks of overtreatment. Conversely, for symptomatic or high-burden cases, early intervention with chemoimmunotherapy is recommended for effective disease control. Surgery remains essential for both diagnosis and the treatment of localized disease. Incorporating molecular profiling and prognostic models, such as MZL-IPI and POD24, is crucial for decision-making and risk stratification. Testing for infectious agents like Helicobacter pylori or Hepatitis C virus should be standard practice, as eradication therapy offers a targeted, less toxic, and effective option in select patients. With ongoing advancements in understanding dysregulated signaling pathways and the tumor microenvironment, we anticipate novel targeted therapies and combination regimens will further improve outcomes. We advocate for molecular testing at diagnosis to identify actionable biomarkers, particularly for patients with refractory or relapsed disease. Finally, MZL management requires vigilant follow-up with adjustments based on evolving disease features. Treatment decisions should integrate patient preferences, clinical context, and the latest evidence to maximize survival while preserving quality of life.

Content Analysis of Food and Beverage Advertisements on TV, Radio, and Billboards in Iran.

Food advertising may influence food preferences and consumption. Due to these advertisements' role in health, the present study has updated the data available on television and radio food advertisements in Iran until 2020. Meanwhile, food advertisements on billboards have been analyzed for the first time. All programs broadcast on two TV and radio channels were downloaded and saved 24 h daily for one week (November 10-16th, 2020). Also, on November 15th billboards located on the busy highways of Tehran were photographed. Validated protocols analyzed the advertisements. A total of 6026 advertisements were analyzed, including 4287 on TV, 1545 on radio, and 194 on billboards. The total duration of TV and radio advertising was 48.6 h. Food products were the most displayed advertisements on TV, radio, and billboards (33.6%). Overall, "condiments" and "desserts" were the most frequently advertised food products among all the studied media (20% and 17%, respectively). The most common advertising appeal included "High quality" on TV and billboards and "Stimulation of profiteering and profit-seeking" on the radio. Advertising violation based on Iran's regulations, Nutrient profile model for the marketing of food and non-alcoholic beverages to children in the WHO Eastern Mediterranean Region, and International Network for Food and Obesity / Non-communicable Diseases (NCDs) Research Monitoring and Action Support (INFORMAS) was reported to be 13%, 74%, and 51%, respectively. Food products ranked the highest among all the advertisements. A clear violation of domestic and international advertising regulations was observed which given the high share of food advertising in the studied media, could endanger public health. This study supports the need for stricter regulations and adherence to international standards to improve nutritional status.

Predictive Models of Deep Shear Wave Velocity Profiles at Excavated Rock Sites in Korea

Predictive Models of Deep Shear Wave Velocity Profiles at Excavated Rock Sites in Korea

Design and simulation of hybrid renewable energy system integration of micro grids using Simulink

This research explores the novel design and simulation of a hybrid renewable energy system integrating photovoltaic (PV) panels, wind turbines, and a diesel generator backup to address the energy demands of an independent hostel in Nottingham, United Kingdom. The system prioritizes minimizing long-term costs and emissions while maintaining energy reliability. A detailed load profile was developed to optimize battery performance, reduce reliance on diesel, and extend battery lifespan, thereby lowering operational costs. Voltage source control and load profile modeling were implemented and evaluated using MATLAB R2023b. Simulation outcomes demonstrated a significant reduction in the diesel generator's runtime, leading to lower emissions. Additionally, the integrated energy management system ensures safe battery operations, enhancing system efficiency, sustainability, and overall performance. This research underscores the potential of optimizing renewable energy integration within microgrids using Simulink.

Integration of gene expression profiling and mathematical modelling to unravel significant biomarkers in population exposed to arsenic

Integration of gene expression profiling and mathematical modelling to unravel significant biomarkers in population exposed to arsenic

AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist

G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening. To address these issues, we introduce AiGPro, a novel multitask model designed to predict small molecule agonists (EC50) and antagonists (IC50) across the 231 human GPCRs, making it a first-in-class solution for large-scale GPCR profiling.Leveraging multi-scale context aggregation and bidirectional multi-head cross-attention mechanisms, our approach demonstrates that ensemble models may not be necessary for predicting complex GPCR states and small molecule interactions. Through extensive validation using stratified tenfold cross-validation, AiGPro achieves robust performance with Pearson's correlation coefficient of 0.91, indicating broad generalizability. This breakthrough sets a new standard in the GPCR studies, outperforming previous studies. Moreover, our first-in-class multi-tasking model can predict agonist and antagonist activities across a wide range of GPCRs, offering a comprehensive perspective on ligand bioactivity within this diverse superfamily. To facilitate easy accessibility, we have deployed a web-based platform for model access at https://aicadd.ssu.ac.kr/AiGPro.Scientific Contribution We introduce a deep learning-based multi-task model to generalize the agonist and antagonist bioactivity prediction for GPCRs accurately. The model is implemented on a user-friendly web server to facilitate rapid screening of small-molecule libraries, expediting GPCR-targeted drug discovery. Covering a diverse set of 231 GPCR targets, the platform delivers a robust, scalable solution for advancing GPCR-focused therapeutic development.The proposed framework incorporates an innovative dual-label prediction strategy, enabling the simultaneous classification of molecules as agonists, antagonists, or both. Each prediction is further accompanied by a confidence score, offering a quantitative measure of activity likelihood. This advancement moves beyond conventional models focusing solely on binding affinity, providing a more comprehensive understanding of ligand-receptor interactions.At the core of our model lies the Bi-Directional Multi-Head Cross-Attention (BMCA) module, a novel architecture that captures forward and backward contextual embeddings of protein and ligand features. By leveraging BMCA, the model effectively integrates structural and sequence-level information, ensuring a precise representation of molecular interactions. Results show that this approach is highly accurate in binding affinity predictions and consistent across diverse GPCR families.By unifying agonist and antagonist bioactivity prediction into a single model architecture, we bridge a critical gap in GPCR modeling. This enhances prediction accuracy and accelerates virtual screening workflows, offering a valuable and innovative solution for advancing GPCR-targeted drug discovery.Graphical

Assessment of Commercial Baby Food Quality and Promotion in Malaysia for Policy Development.

Nutritional quality of food for infants and young children (FIYC) is key to optimum development and health. To evaluate commercial FIYC products, the World Health Organization Regional Office for Europe (WHO-EURO) has defined a Nutrient and Promotion Profile Model (NPPM). Although FIYC is commonly used in Malaysia, its quality is largely unknown. We evaluated the nutrient profiles and on-pack promotions of FIYC available in Kuala Lumpur, Malaysia, using the NPPM. Among the 285 products sampled from a range of retail outlets, none met all NPPM promotional recommendations; only 24% satisfied all relevant nutrition criteria. Several products failed on sugar (29%) and sodium (26%) criteria, with around half having added sugar. In addition, 3%, 6%, and 9% of FIYC failed relevant fat, protein, and energy requirements, respectively. Although 98% had inappropriate health claims, 36% missed appropriate breastfeeding statements. There is an urgent need to improve the quality of FIYC in Malaysia.

Integrated Multivariate Predictive Model of Body Composition and Lipid Profile for Cardiovascular Risk Assessment

(1) Background/Objectives: Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality globally, necessitating effective risk prediction strategies. This study was aimed at developing and validating a multivariate predictive model integrating body composition and lipid profile to assess cardiovascular risk in an adult population. (2) Methods: A cross-sectional analysis of 90 participants from the general Spanish population was conducted. Participants were classified into cardiovascular risk groups (low, medium, high) based on systolic blood pressure. (3) Results: Descriptive and multinomial logistic regression analyses revealed significant associations between cardiovascular risk and specific parameters, such as visceral fat, glucose levels, and waist-to-hip ratio. Visceral adiposity emerged as a strong predictor of high cardiovascular risk, highlighting its critical role in cardiovascular health. Glucose levels were also significantly associated with increased risk, underscoring the importance of metabolic health in cardiovascular outcomes. Contrary to expectations, lipid markers like cholesterol and triglycerides did not show significant variations across risk categories, suggesting that traditional lipid profiles may not fully capture cardiovascular risk in the study group. Waist-to-hip ratio showed significant associations with cardiovascular risk transitions, particularly between low and medium risk, emphasizing the importance of fat distribution patterns. (4) Conclusions: These findings suggest that body composition, particularly visceral fat, is a crucial determinant of cardiovascular risk, necessitating more personalized risk assessment approaches that move beyond traditional lipid markers.

User Profile Construction Based on High-Dimensional Features Extracted by Stacking Ensemble Learning

Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating unpredictable social behaviors, such as the malicious manipulation of public opinion, the doxing of ordinary users, and cyberbullying. However, the sparsity of data and the concealed nature of user behavior pose significant challenges to existing network reconnaissance technologies. In this study, we focus on constructing user profiles on online social network platforms by extracting features to build deep user profiles based on behavioral patterns. Drawing inspiration from the 5Cs principle of credit evaluation, we refine it into a 3Cs principle tailored for user profiling on social network platforms and associate it with user behavioral patterns. To further analyze user behavior, a high-dimensional feature extraction method is proposed using an improved stacking ensemble learning model. Based on experimental data analysis, the most suitable base algorithms for high-dimensional feature extraction are identified. Experimental results demonstrate that the integration of high-dimensional features improved the behavior prediction accuracy of the profiling model by 9.26% on balanced datasets and enhanced the AUC (area under the curve) metric by 3.69% on imbalanced datasets. The proposed method effectively increases the depth and generalization performance of user profiling.

Variability of transport emissions and policy implications for the transition towards low-carbon transport

ABSTRACT This paper investigates intra-week variability of transport emissions using 2019 English National Travel Survey data. The unique lens of emissions variability sheds new light on the conceptualization, measurement and challenges of sustainable travel. Specifically, our analysis shows significant heterogeneity along three dimensions, (1) intra-week travel emissions; (2) the level and the variability of weekly emissions within each generic socio-economic group; and (3) latent emissions profiles across the population, which are identified using a latent profile model. Our empirical analysis demonstrates that the socio-economic status alone cannot capture the nuanced emission characteristics across traveller groups. The positive correlation between socio-economic status and travel emissions is subject to significant compositional effects. It is argued that transport sustainability is a relative term, and its definition and usefulness in a policy context are dependent on the benchmark selected. The paper calls for a more purposeful and relatable approach for segmenting travellers in designing sustainable travel policies. A dual focus on the level and the variability of travel emissions offers a new perspective for understanding travel behaviour heterogeneity and adaptability. Highlights Significant intra-week variability of transport emissions in England Latent travel emissions profiles empirically identified Socio-economic status is a weak predictor of generic emissions profile Calling for a more purposeful and relatable approach for segmenting travellers Emissions variability is a new perspective for understanding behaviour adaptability

Online profiling of volunteers in public health emergencies: insights from COVID-19 in China

BackgroundDuring public health emergencies, the diverse backgrounds of volunteers pose numerous management challenges. This study aims to develop an online profiling model of volunteers using social media data to achieve a more comprehensive and objective understanding of them.MethodsIn the proposed model, the study designed five profiling tags: basic information, sentiment, topic features, interest preferences, and online social engagement. K-Modes clustering was employed to implement the profiling. To validate the feasibility of the model, an empirical study was conducted using Weibo data from 1,070 volunteers during the COVID-19 pandemic in China, resulting in the online profiling of these volunteers.ResultsFour categories of volunteers could be identified: Public Affairs Pioneers (32.4%), Diary Record Lurkers (32.8%), Social Topic Sharers (20.9%), and Fashion and Entertainment Influencers (13.9%). Overall, volunteers were predominantly female, generally interested in entertainment, relatively satisfied with their volunteer work, and possessed a sense of social responsibility. The four categories of volunteers exhibited distinct characteristics in terms of interests, online social behavior, and influence.ConclusionsThe proposed online profiling model objectively captures the characteristics of volunteers during public health emergencies. The four volunteer categories identified through the empirical results provide a multidimensional and comprehensive understanding of volunteers. For different volunteer categories, official agencies can tailor their recruitment, management, and training strategies to better suit the specific needs and strengths of the volunteers, thereby enhancing the effectiveness and efficiency of volunteer engagement and ensuring volunteers are well-prepared and supported in their roles.

Utilizing Patient-Derived Xenografts to Model Precision Oncology for Biliary Tract Cancer.

Biliary tract cancers, which are rare and aggressive malignancies, are rich in clinically actionable molecular alterations. A major challenge in the field is the paucity of clinically relevant biliary tract cancer models that recapitulate the diverse molecular profiles of these tumors. The purpose of this study was to curate a collection of patient-derived xenograft (PDX) models that reflect the spectrum of genomic alterations present in biliary tract cancers to create a resource for modeling precision oncology. PDXs were derived from biliary tract cancer samples collected from surgical resections or metastatic biopsies. Alterations present in the PDXs were identified by whole-exome sequencing and RNA sequencing. PDXs were treated with approved and investigational agents. Efficacy was assessed by change in tumor volume from baseline. Event-free survival was defined as the time to tumor doubling from baseline. Responses were categorized at day 21: >30% decrease in tumor volume = partial response, >20% increase in tumor volume = progressive disease, and any non-partial response/progressive disease was considered stable disease. Genomic sequencing demonstrated key actionable alterations across this cohort, including alterations in FGFR2, isocitrate dehydrogenase I, ERRB2, PIK3CA, PTEN, and KRAS. RNA sequencing demonstrated fusions and expression of antibody-drug conjugate targets, including TROP2, HER2, and Nectin4. Therapeutic matching revealed objective responses to approved and investigational agents that have been shown to have antitumor activity clinically. In this study, we developed a catalog of biliary tract cancer PDXs that underwent comprehensive molecular profiling and therapeutic modeling. To date, this is one of the largest collections of biliary tract cancer PDX models and will facilitate the development of personalized treatments for patients with these aggressive malignancies.

Construction of AI scroll profiles for scroll compressors utilizing algebraic spiral and involute

A new type of scroll profile consisting of an algebraic spiral and an involute (AI) is proposed in this paper. To solve the problem of the small built-in volume ratio in the traditional scroll profile and the need to modify the wrap head before use, mathematical equations and geometric models of AI scroll profiles are generated based on the boundary constraints of combined scroll profiles. In addition, the geometrical relationship between scroll profile parameters and the influence of profile parameters on the midline, scroll wrap, and geometrical properties of AI scroll profiles are quantitatively studied. Finally, the stress and deformation of the new AI scroll wrap are compared to those of the involute scroll wrap. The results indicate that the AI scroll profile improves the performance of the scroll compressor and can be used directly without head modification. Under the same scroll wrap profile parameters, the maximum deformation and maximum equivalent stress of AI scroll wrap are 44.61% and 80.12% lower than those of the involute scroll wrap, respectively.

MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon

This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R2 values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m3ha−1 and the sawn lumber volume 333.6 m3ha−1. The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.

Design of a nutrient profiling model for life cycle assessment of “superfoods” to address nutritional deficiencies and enhance environmental protection in Spain

Abstract Purpose The overriding connection between climate interactions and nutritional outcomes of food systems is at the forefront of research, especially when it comes to assessing alternative food products. Accordingly, the objective of this paper is to design a nutrient profiling (NP) model adapted to the Spanish context for use in nutritionally-factored environmental life cycle assessments (LCA) of “superfoods.” Methods The variability in nutritional needs between countries and their associated environmental impact were the key points that motivated the creation of the model and guided its development. Based on the “nutrient rich” family of models, the characterization of the NP system was guided by the definition of the specific purpose and the selection of qualifying and disqualifying nutrients according to the Spanish recommendations. The introduction of weighting factors was motivated by the capacity of “superfoods” to cover main nutritional shortfalls of the population and they were estimated with the actual and the recommended intake levels. Results and discussion The Spanish Nutrient Rich (“super”)Food 9.2 (sNRF9.2) model validation and testing across various foods successfully fulfills its purpose by aligning with the Spanish Public Health Strategy and providing an adequate prioritization of products. The application of the index to “superfoods” identified chia seeds, turmeric, kale, or moringa, among others as the most beneficial, thus demonstrating their nutritional potential. Even though the application as functional unit in the LCA of “superfoods” is ongoing, preliminary results in conventional products showed its usefulness in conveying integrated information efficiently. Conclusions The model represents an initial step toward advancing research, adapting a contextualized NP model for future objective environmental analysis of “superfoods.” It will contribute to ensuring sustainable food security and provide new insights and perspective for decision-making by consumers, stakeholders, and policy makers.

Meta-analysis of retinal transcriptome profiling studies in animal models of myopia.

Myopia prevalence is increasing at alarming rates, yet the underlying mechanistic causes are not understood. Several studies have employed experimental animal models of myopia and transcriptome profiling to identify genes and pathways contributing to myopia. In this study, we determined the retinal transcriptome changes in response to form deprivation in mouse retinas. We then conducted a transcriptome meta-analysis incorporating all publicly available datasets and analyzed how the results related to the genes associated with refractive errors in human genome-wide association studies (GWAS). Form deprivation was induced in three male C57BL6/J mice from postnatal day 28 (P28) to P42. Retinal gene expression was analyzed with RNA sequencing, followed by differential gene expression analysis with DESeq2 and identification of associated pathways with the Kyoto Encyclopedia of Genes and Genomes (KEGG). A systematic search identified four similar retinal transcriptomics datasets in response to experimental myopia using chicks or mice. The five studies underwent transcriptome meta-analyses to determine retinal gene expression changes and associated pathways. The results were compared with genes associated with human myopia. Differential gene expression analysis of form-deprived mouse retinas revealed 235 significantly altered transcripts, implicating the BMP2 signaling pathway and circadian rhythms, among others. Transcriptome-wide meta-analyses of experimental myopia datasets found 427 differentially expressed genes in the mouse model and 1,110 in the chick model, with limited gene overlap between species. Pathway analysis of these two gene sets implicated TGF-beta signaling and circadian rhythm pathways in both mouse and chick retinas. Some pathways associated only with mouse retinal changes included dopamine signaling and HIF-1 signaling pathway, whereas glucagon signaling was only associated with gene changes in chick retinas. The follistatin gene changed in both mouse and chick retinas and has also been implicated in human myopia. TGF-beta signaling pathway and circadian entrainment processes were associated with myopia in mice, chicks, and humans. This study highlights the power of combining datasets to enhance statistical power and identify robust gene expression changes across different experimental animal models and conditions. The data supports other experimental evidence that TGF-beta signaling pathway and circadian rhythms are involved in myopic eye growth.

Modeling of Shoulder–Elbow Movement with Exponential Parameter Identification During Walking Gaits for Healthy Subjects and Patients with Parkinson’s Disease

Background: This paper aims to complement the latest contribution in the literature that provides estimates of physiological parameters of a dynamic model for the elbow time profile during walking while linking them to a neurodegenerative disorder (Parkinsons’s disease) characterized by motor symptoms. An upper limb model is here proposed in which an active contractile element is included within a model, viewing the arm as a double pendulum system and muscles as represented by a Kelvin–Voight system. All model parameters characterizing both the shoulder and the elbow of each subject are estimated via a gradient-like identifier whose exponential convergence properties are determined by a non-anticipative Lyapunov function, ensuring robustness features. Methods: Joint angle data from different walking subjects (healthy subjects and patients with Parkinson’s disease) have been recorded using an IMU sensor system and compared with the joint angles obtained by means of the proposed model, which was adapted to each subject using available anthropometric knowledge and relying on the estimated parameters. Results: Experiments show that the reconstruction of shoulder and elbow time profiles can be definitely achieved through the proposed procedure with the estimated stiffness parameters turning out to constitute objective and quantitative indices of muscle stiffness (as a pivotal symptom of the pathology), which are able to track changes due to the therapy. Conclusions: The same dynamic model is actually able to capture the main features of the upper limb movement of both (healthy and pathological) walking subjects, with its parameters, in turn, characterizing the nature and progress of the pathology.

Evaluating alignment of UK commercial baby food products with the WHO nutrient and promotion profile model

PurposeThe first 1000 days of life are critical for long-term health outcomes, and there is increasing concern about the suitability of commercial food products for infants, toddlers, and children. This study evaluates the compliance of UK commercial baby food products with WHO Nutrient and Promotion Profile Model (NPPM) guidelines.MethodsBetween February and April 2023, data on 469 baby food products marketed for infants and children under 36 months were collected from the online platforms of four major UK supermarkets. Nutritional composition and labelling information were assessed using the NPPM criteria. Quantitative analyses were performed using IBM SPSS, presenting data as means with 95% confidence intervals.ResultsWhile 75% of products met the minimum energy content criteria, compliance with total sugar content and protein requirements was 59% and 94%, respectively. Overall, only 45% of products adhered to NPPM nutritional standards. Promotional assessments revealed that no products met the requirements for appropriate nutrient, health, or marketing claims. Furthermore, only 5% of products included adequate statements in support of breastfeeding.ConclusionThis study highlights the need for stricter nutritional and promotional standards in the UK baby food industry to foster healthier early dietary habits. Regulatory measures are essential to align commercial baby food products with WHO recommendations, reducing inappropriate claims and improving nutritional quality.