Profile Model Research Articles

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.

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

ObjectiveMyopia 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).MethodsForm 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.ResultsDifferential 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.ConclusionThis 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.

Lymphatic collection and cell isolation from mouse models for multiomic profiling.

Premetastatic cancer cells often spread from the primary lesion through the lymphatic vasculature and, clinically, the presence or absence of lymph node metastases impacts treatment decisions. However, little is known about cancer progression via the lymphatic system or of the effect that the lymphatic environment has on cancer progression. This is due, in part, to the technical challenge of studying lymphatic vessels and collecting lymph fluid. Here we provide a step-by-step procedure to collect both lymph and tumor-draining lymph in mouse models of cancer metastasis. This protocol has been adapted from established methods of lymph collection and was developed specifically for the collection of lymph from tumors. The approach involves the use of mice bearing melanoma or breast cancer orthotopic tumors. After euthanasia, the cisterna chyli and the tumor are exposed and viewed using a stereo microscope. Then, a glass cannula connected to a 1 mL syringe is inserted directly into the cisterna chyli or the tumor-draining lymphatics for collection of pure lymph. These lymph samples can be used to analyze the lymph-derived cancer cells using highly sensitive multiomics approaches to investigate the impact of the lymph environment during cancer metastasis. The procedure requires 2 h per mouse to complete and is suitable for users with minimal expertise in small animal handling and use of microsurgical tools under a stereo microscope.

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (TF) binding and chromatin accessibility at context-specific cREs remain elusive. We introduce ChromBPNet, a deep learning DNA sequence model of base-resolution accessibility profiles that detects, learns and deconvolves assay-specific enzyme biases from regulatory sequence determinants of accessibility, enabling robust discovery of compact TF motif lexicons, cooperative motif syntax and precision footprints across assays and sequencing depths. Extensive benchmarks show that ChromBPNet, despite its lightweight design, is competitive with much larger contemporary models at predicting variant effects on chromatin accessibility, pioneer TF binding and reporter activity across assays, cell contexts and ancestry, while providing interpretation of disrupted regulatory syntax. ChromBPNet also helps prioritize and interpret regulatory variants that influence complex traits and rare diseases, thereby providing a powerful lens to decode regulatory DNA and genetic variation.

Modeling and advanced Ground Penetrating Radar techniques for evaluating pipe leakage and pavement distress

This study provides a comprehensive analysis of Ground Penetrating Radar (GPR) data processing techniques for detecting water leakage in urban pavements. By examining GPR images characterized by continuous and discontinuous high-amplitude reflections, the research identifies clear indicators of water saturation caused by leaking underground pipes. Computer modeling of GPR profiles enables effective comparisons with real data and helps in calibrating key GPR attributes such as energy, amplitude variance, and RMS amplitude. These attributes allow for the precise delineation of leakage zones in both synthetic and real GPR datasets collected in the Amazon region of Brazil. The Reflector Tilt Method (RTM) significantly enhances GPR data analysis, especially for detecting subsurface water leaks. RTM utilizes changes in the relative dielectric constant and electromagnetic wave velocity induced by water saturation in unconsolidated, horizontally stratified sand layers. By analyzing the tilt of reflections in GPR profiles, RTM differentiates between dry and saturated zones, providing insights into the geometry and extent of water infiltration. Notably, this study demonstrates how increased subsurface water saturation causes underlying reflectors to tilt. Using the tilt angle, we propose a novel technique to quantify the increase in the dielectric constant due to water saturation. This technique could also be adapted to detect leaks involving other fluids, as long as there is a strong contrast between the dielectric constant and the background. The integration of RTM improves the creation of accurate three-dimensional models of affected areas, enhancing leak detection and aiding in understanding water movement dynamics within the subsurface. The study underscores the importance of combining GPR attributes with RTM to generate detailed 3D models of leakage zones, which are essential for estimating the volume of infiltrated areas. Observations indicate significant water saturation near leaking pipes at shallow depths, with saturation levels decreasing rapidly as depth increases. By identifying 2D geophysical signatures of water leaks in public distribution networks, the research highlights the effectiveness of non-invasive GPR methods in mitigating risks such as soil erosion and surface collapse. This approach demonstrates the utility of GPR attributes and RTM as essential tools for assessing urban pavement integrity, enabling early detection of leaks and precise mapping of affected zones. The findings contribute significantly to the efficient and sustainable management of urban infrastructure, enhancing maintenance strategies and reducing potential hazards associated with undetected fluid leaks.

Quantitative profiling and mapping of small molecules by laser desorption/ionization mass spectrometry: combinations of carbon-based nano-matrices and sample preparation protocols.

The choices of matrices and protocols for sample deposition are critical factors, which impact each other in the matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Previous reports on MALDI MS matrices have only compared their performances in terms of their MS signal intensities and provided optical microphotos or MALDI MS images of sample spots but typically lacked quantitative evaluation. Therefore, there is an urgent need to develop a multivariate model to evaluate the performance of different combinations of matrices and sample protocols. Herein, we report a weighted multivariate model for the systematic quantitative profiling of combinations of matrices/protocols. Our work covers three types of carbon-based nano-matrices, namely, two-dimensional graphite oxide (GO) nanosheets, one-dimensional carbon nanotubes (CNTs), and zero-dimensional graphite dots (GDs). Four representative sample protocols for MALDI MSI, namely, rapid evaporation, seed layer, sandwich, and dry droplet, were examined in our model. We selected five important parameters, namely, (1) planar dispersity grading by SEM, (2) planar fluorescence homogeneity, (3) coefficient variations in MS signals, (4) MS signal-to-noise ratio, and (5) planar MS signal heatmap grading, and individually assigned weights according to their significance in MSI performance. Calculation of the radar chart areas based on our model provided quantitative profiles for individual combinations. Among these, the dry droplet protocol using a matrix of GDs offered the largest radar chart area, indicating that it was a favorable combination choice for MSI. Thus, this work proposes a multivariate model to systematically compare different combinations of matrices and sample protocols, thereby promising a useful tool for mapping small molecules through MALDI MS.

Computational profiling and pharmacokinetic modelling of Febuxostat: Evaluating its potential as a therapeutic agent for diabetic wound healing.

Diabetic wounds, a significant complication of Type 2 Diabetes Mellitus (T2DM), face delayed healing due to impaired inflammation, angiogenesis, and collagen synthesis. This study explores Febuxostat, a xanthine oxidase inhibitor for its therapeutic potential in wound healing. Combining computational approaches and in-vitro assays, the study evaluates its effects on key wound healing pathways, cell viability, migration. The potential of Febuxostat in diabetic wound healing was studied using in-silico tools for Molecular docking and ADMET profiling, alongside Molecular dynamics (MD) simulations. Toxicity was assessed with OSIRIS Explorer, and biological activity was predicted using the PASS tool. In-vitro MTT and scratch assays on L929 cells further validated cytotoxicity and wound healing efficacy. Docking analysis revealed strong binding affinities to key wound healing targets, including VEGF (-9.11kcal/mol) and NFKβ (-8.62kcal/mol). Pharmacokinetic studies highlighted favorable skin permeability, supporting topical applications. Toxicity predictions indicated a safe profile. Molecular dynamics simulations demonstrated stable protein-ligand complexes, particularly with VEGF. Cytotoxicity studies on L929 cells revealed an IC50 of 6.08μM and the scratch assay demonstrated significant wound healing activity, highlighting its effectiveness in promoting cell migration and closure. Febuxostat shows remarkable potential in enhancing diabetic wound healing by promoting cell migration, targeting wound-healing proteins, as demonstrated through in-silico and in-vitro studies. This drug is poised to effectively treat diabetic wounds, accelerating healing and reducing complications. Rigorous pre-clinical and clinical evaluations are essential to validate its safety, efficacy, and therapeutic potential.

Research on software task profile modeling technology

Research on software task profile modeling technology

Children’s exposure to unhealthy food advertising on Philippine television: content analysis of marketing strategies and temporal patterns

ABSTRACT Background This study conducted an exploratory content analysis of TV food advertisements on the top three most popular channels for Filipino children aged two to 17 during school and non-school days. Methods Data were collected by manually recording of aired advertisements from 16 non-school days (July to September 2020) and 16 school days (January to April 2021). Descriptive and inferential statistical analyses were used to assess children’s rates of exposure to food advertisements (mean ± SD of advertisements aired per channel per hour), the healthiness of promoted foods (as permitted (healthier) or not permitted (unhealthy) according to nutrient profiling models from the World Health Organization), and persuasive techniques used in food advertisements, including promotional characters and premium offers. Results The results show that the rates of exposure to food advertisements were higher during school days (14.6 ± 14.8) than on non-school days (11.9 ± 12.0) (p < 0.01). Both periods yield a similarly higher proportion of non-permitted food advertisements (e.g. 9.3 ± 9.7 ads/channel/hour for school days and 8.3 ± 8.5 ads/channel/hour for non-school days) than permitted ones. More non-permitted food advertisements during children’s peak viewing times were observed than non-peak viewing times (e.g. 11.8 ± 10. vs. 8.3 ± 9.2 ads/channel/hour for school days). Non-permitted food advertisements employed persuasive techniques more frequently, accounting for 64–91% of all food ads during peak viewing times. Conclusion Children are exposed to a large volume of television advertisements for foods that should not be permitted to be marketed to children based on authoritative nutrient criteria.

Mineralogical indicators of Holocene climate in sediments of the high-mountain Lake Sagan-Nur (East Sayan Mountains)

We present the first results of study of bottom sediments taken from the high-mountain freshwater Lake Sagan-Nur of glacial origin which is located on the Oka plateau (East Sayan Mountains). Comprehensive investigations of the mineral composition of Lake Sagan-Nur sediments accumulated during the last ~8600 cal. years were carried out. The research methods include X-ray diffractometry (XRD), infrared spectroscopy, laser grain size analysis, SiO2bio determination, AMS and 210Pb dating. The mineral composition of the bottom sediments is dominated by layered silicates, quartz and plagioclase; and also, the X-ray amorphous component consisting of biogenic silica and organic matter is constantly present. The method of mathematical modeling of XRD profiles was used for correct identification of the layered silicates. It was found that the clay mineral assemblage was stable comprising muscovite, chlorite, illite, mixed-layer illite-smectite and chlorite-smectite, kaolinite. At the same time, the quantitative ratios of these minerals and their structural characteristics were modified significantly in response to paleoenvironmental changes in the region. This feature reveals the potential of a number of phyllosilicates (illite, mixed-layer minerals) as indicators of paleoclimate in sedimentary sections of freshwater basins. The study of sediment composition and, especially, the precision mineralogical-crystallochemical analysis of layered silicates allowed us to reconstruct the paleolandscapes and climatic conditions of sediment accumulation for different Holocene sub-stages.

Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data

Abstract. Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset provides widely used data to estimate TC climatology. However, it has low data coverage, lacking intensity and outer-size data for more than half of all recorded storms, and is therefore insufficient as a reference for researchers and decision makers. To fill this data gap, we reconstruct a long-term TC dataset by integrating IBTrACS and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data. This reconstructed dataset covers the period 1959–2022, with 3 h temporal resolution. Compared to the IBTrACS dataset, it contains approximately 3–4 times more data points per characteristic. We establish machine learning models to estimate the maximum sustained wind speed (Vmax) and radius of maximum wind (Rmax) in six basins for which TCs are generated, using ERA5-derived 10 m azimuthal mean azimuthal wind profiles as input, with Vmax and Rmax data from the IBTrACS dataset used as learning target data. Furthermore, we employ an empirical wind–pressure relationship and six wind profile models to estimate the minimum central pressure (Pmin) and outer size of the TCs, respectively. Overall, this high-resolution TC reconstruction dataset demonstrates global consistency with observations, exhibiting mean biases of &lt;1 % for Vmax and 3 % for Rmax and Pmin in almost all basins. The dataset is publicly available from https://doi.org/10.5281/zenodo.13919874 (Xu et al., 2024) and substantially advances our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.

Latent class profile model with time-dependent covariates: a study on symptom patterning of patients for head and neck cancer

The latent class profile model (LCPM) is a widely used technique for identifying distinct subgroups within a sample based on observations' longitudinal responses to categorical items. This paper proposes an expanded version of LCPM by embedding time-specific structures. Such development allows analysts to investigate associations between latent class memberships and time-dependent predictors at specific time points. We suggest a simultaneous estimation of latent class measurement parameters via the expectation-maximization (EM) algorithm, which yields valid point and interval estimators of associations between latent class memberships and covariates. We illustrate the validity of our estimation strategy via numerical studies. In addition, we demonstrate the novelty of the proposed model by analyzing the head and neck cancer data set.

Defining "High-In" Saturated Fat, Sugar, and Sodium to Help Inform Front-of-Pack Labeling Efforts for Packaged Foods and Beverages in the United States.

Background: To help consumers make healthier choices, the US Food and Drug Administration (FDA) has been charged with developing a front-of-package label (FOPL) to appear on US packaged foods and beverages. One option being explored is the use of "high-in" FOPLs for added sugar, sodium, and saturated fat using a threshold of ≥20% of the recommended daily value (%DV) per portion/serving size to define "high-in". While research has addressed what FOPL designs are most effective at visually communicating "high-in", less attention has been paid to the nutrient profile model (NPM) used to decide which products should receive these labels. In addition, several established regional NPMs already exist that identify products that are high in nutrients of concern, but it is unclear how these compare to the FDA's %DV approach. Methods: We used a dataset of 51,809 US products from Mintel's Global New Products Database to examine how the FDA's current definition of "high-in" compares to three established regional NPMs: the Canadian NPM, the Pan American Health Organization (PAHO) NPM, and Chile's NPM. Results: Overall agreement between the four NPMs was 51% for foods and 72% for beverages, with highest agreement in categories such as sweetened sodas (87%), and lowest agreement in categories such as bread (14%) and salty snacks (29%). The Canadian NPM showed the highest agreement to the FDA "high-in" criteria while the Chilean and PAHO models had lower agreement. For many food categories, the FDA's definition of "high-in" would require the fewest products to carry a "high-in" label. This issue was particularly pronounced in categories that tend to be served in small portions (e.g., salty snacks, bars), but disappeared or reversed for categories that are served in larger portions (e.g., frozen and non-frozen main dishes). Conclusions: The NPM chosen has important policy implications for an FOPL system's ability to identify unhealthy foods and incentivize companies to reformulate products. Based on these results, the FDA should consider using a stronger NPM similar to those used elsewhere in the Americas region when deciding the final thresholds for "high-in" for US packaged foods and beverages.

Impact of Urbanization on Mesoscale Convective Systems: Insights From a Radar Wind Profiler Mesonet, Theoretical Analyses, and Model Simulations

AbstractThe role of planetary boundary layer (PBL) in the urban effect on mesoscale convective systems (MCSs) remains highly uncertain. The high‐density Mesonet of radar wind profilers (RWPs) in Beijing, along with geostationary satellite data from Fengyun‐2, were used to investigate the MCS evolution and potential impact of urbanization. An MCS as observed by satellite tends to weaken over urban and downstream areas under strong precipitating conditions as opposed to an invigorated MCS over urban and downstream areas under weak precipitating conditions. The pattern of low‐level convergence and high‐level divergence from RWP Mesonet dominates the PBL of all MCS events under strong precipitating conditions, as compared with fluctuating vertical structures of convergence under weak precipitating conditions. Under strong precipitating conditions, turbulent friction weakens MCS activity over the Beijing urban area. Under weak precipitating conditions, thermal effect dominated over urban areas, favoring the formation of surface‐level convergence and invigorating MCSs. Using the large‐eddy simulation, we further investigated the influence of turbulent structural characteristics on the development of MCS. For the strong (weak) precipitating case, turbulence dissipation (buoyancy) dominated the turbulent kinetic energy over urban areas, explaining well the characteristics of MCSs propagating over these areas. Overall, the theoretical analysis and model simulation results confirm the observed impact of urbanization on MCSs. Besides the above scientific findings, our study highlights the importance of the RWP Mesonet in studying the urban effect on MCSs.