[go: up one dir, main page]

 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (112,382)

Search Parameters:
Keywords = physical

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
17 pages, 2327 KiB  
Article
Evaluating Source Complexity in Blended Milk Cheese: Integrated Strontium Isotope and Multi-Elemental Approach to PDO Graviera Naxos
by Majda Nikezić, Paraskevi Chantzi, Johanna Irrgeher and Tea Zuliani
Foods 2024, 13(16), 2540; https://doi.org/10.3390/foods13162540 (registering DOI) - 14 Aug 2024
Abstract
Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well [...] Read more.
Graviera Naxos, a renowned cheese with Protected Designation of Origin status, is crafted from a blend of cow, goat, and sheep milk. This study focused on assessing the Sr isotopic and multi-elemental composition of both the processed cheese and its ingredients, as well as the environmental context of Naxos Island, including samples of milk, water, soil, and feed. The objective was to delineate the geochemical signature of Graviera Naxos cheese and to explore the utility of Sr isotopes as indicators of geographic origin. The 87Sr/86Sr values for Graviera Naxos samples ranged from 0.70891 to 0.70952, indicating a relatively narrow range. However, the Sr isotopic signature of milk, heavily influenced by the feed, which originates from geologically distinct areas, does not always accurately reflect the local breeding environment. Multi-elemental analysis revealed variations in milk composition based on type and season; yet, no notable differences were found between raw and pasteurized milk. A mixing model evaluating the contributions of milk, sea salt, and rennet to the cheese’s Sr isotopic signature suggested a significant average contribution of 73.1% from sea salt. This study highlights the complexities of linking dairy products with their geographical origins and emphasizes the need for sophisticated geochemical authentication methods. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Naxos island and the sampling stations.</p>
Full article ">Figure 2
<p>Variations in Sr and essential elements’ levels across (<b>a</b>) sampling seasons (January 2021 and June 2021), (<b>b</b>) milk types, (<b>c</b>) pasteurized and raw milk, and (<b>d</b>) milk from milk tanks and Graviera Naxos cheese. The relative mass fraction is normalized to 100% for all elements. Elements are displayed based on their total mass fraction, from lowest to highest.</p>
Full article ">Figure 3
<p>Ranges and distribution of <sup>87</sup>Sr/<sup>86</sup>Sr values across matrices (KDE bw = 0.0005641063).</p>
Full article ">Figure 4
<p>Boxplots representing ranges of <sup>87</sup>Sr/<sup>86</sup>Sr values for (<b>a</b>) milk and (<b>b</b>) feed across different sampling campaigns. The median of the data is the line; the points outside the whiskers are points beyond 1.5 times the IQR from the quartiles.</p>
Full article ">Figure 5
<p>Correlation between <sup>87</sup>Sr/<sup>86</sup>Sr values of (<b>a</b>) milk and feed samples from January 2021 and (<b>b</b>) milk (June 2021) and water samples. The line of best fit is represented by a dashed blue line; the shaded area surrounding the line depicts the 95% confidence region.</p>
Full article ">Figure 6
<p>Isospace plot, ⁸⁷Sr/⁸⁶Sr ratios of sources (milk, sea salt, rennet) and Graviera Naxos cheese samples plotted against their respective inverse Sr mass fractions.</p>
Full article ">
20 pages, 16326 KiB  
Article
Multiplatform Computer Vision System to Support Physical Fitness Assessments in Schoolchildren
by José Sulla-Torres, Bruno Santos-Pamo, Fabrizzio Cárdenas-Rodríguez, Javier Angulo-Osorio, Rossana Gómez-Campos and Marco Cossio-Bolaños
Appl. Sci. 2024, 14(16), 7140; https://doi.org/10.3390/app14167140 (registering DOI) - 14 Aug 2024
Abstract
Currently, the lack of physical activity can lead to health problems, with the increase in obesity in children between 8 and 18 years old being of particular interest because it is a formative stage. One of the aspects of trying to solve this [...] Read more.
Currently, the lack of physical activity can lead to health problems, with the increase in obesity in children between 8 and 18 years old being of particular interest because it is a formative stage. One of the aspects of trying to solve this problem is the need for a standardized, less subjective, and more efficient method of evaluating physical condition in these children compared to traditional approaches. Objective: Develop a multiplatform based on computer vision technology that allows the evaluation of the physical fitness of schoolchildren using smartphones. Methodology: A descriptive cross-sectional study was carried out on schoolchildren aged 8 to 18 years of both sexes. The sample was 228 schoolchildren (128 boys and 108 girls). Anthropometric measurements of weight, height, and waist circumference were evaluated. Body mass index (BMI) was calculated. Four physical tests were evaluated: flexibility (sit and reach), horizontal jump (explosive strength), biceps curl (right arm strength resistance), and sit-ups (abdominal muscle resistance). With the information collected traditionally and by filming the physical tests, a computer vision system was developed to evaluate physical fitness in schoolchildren. Results: The implemented system obtained an acceptable level of precision, reaching 94% precision in field evaluations and a percentage greater than 95% in laboratory evaluations for testing. The developed mobile application also obtained a high accuracy percentage, greater than 95% in two tests and close to 85% in the remaining two. Finally, the Systematic Software Quality Model was used to determine user satisfaction with the presented prototype. Regarding usability, a satisfaction level of 97% and a reliability level of 100% was obtained. Conclusion: Compared to traditional evaluation and computer vision, the proposal was satisfactorily validated. These results were obtained using the Expanded Systematic Software Quality Model, which reached an “advanced” quality level, satisfying functionality, usability, and reliability characteristics. This advance demonstrates that the integration of computer vision is feasible, highly effective in the educational context, and applicable in the evaluations of physical education classes. Full article
Show Figures

Figure 1

Figure 1
<p>View of body tracking on Unity while the bicep curl test is running.</p>
Full article ">Figure 2
<p>Frontal distribution of the test.</p>
Full article ">Figure 3
<p>Joint map provided by LightBuzz.</p>
Full article ">Figure 4
<p>Vector representation of the sit-up test.</p>
Full article ">Figure 5
<p>Vector representation of the biceps curls test.</p>
Full article ">Figure 6
<p>Example of sit-up test evaluation.</p>
Full article ">Figure 7
<p>Example of biceps flexion test evaluation.</p>
Full article ">Figure 8
<p>Example of horizontal jump test evaluation without impulse.</p>
Full article ">Figure 9
<p>Flexibility bending test evaluation example.</p>
Full article ">Figure 10
<p>Mobile application initial interfaces.</p>
Full article ">Figure 11
<p>Mobile application execution test interfaces.</p>
Full article ">Figure 12
<p>Abdominal flexion test by control and software.</p>
Full article ">Figure 13
<p>Biceps curl test by control and software.</p>
Full article ">Figure 14
<p>Flexibility test by control and software.</p>
Full article ">Figure 15
<p>Horizontal jump test without impulse by control and software.</p>
Full article ">
12 pages, 1219 KiB  
Review
Nutritional Management of Patients with Fatty Acid Oxidation Disorders
by Luis Peña-Quintana and Patricia Correcher-Medina
Nutrients 2024, 16(16), 2707; https://doi.org/10.3390/nu16162707 (registering DOI) - 14 Aug 2024
Abstract
Treatment of fatty acid oxidation disorders is based on dietary, pharmacological and metabolic decompensation measures. It is essential to provide the patient with sufficient glucose to prevent lipolysis and to avoid the use of fatty acids as fuel as far as possible. Dietary [...] Read more.
Treatment of fatty acid oxidation disorders is based on dietary, pharmacological and metabolic decompensation measures. It is essential to provide the patient with sufficient glucose to prevent lipolysis and to avoid the use of fatty acids as fuel as far as possible. Dietary management consists of preventing periods of fasting and restricting fat intake by increasing carbohydrate intake, while maintaining an adequate and uninterrupted caloric intake. In long-chain deficits, long-chain triglyceride restriction should be 10% of total energy, with linoleic acid and linolenic acid intake of 3–4% and 0.5–1% (5/1–10/1 ratio), with medium-chain triglyceride supplementation at 10–25% of total energy (total MCT+LCT ratio = 20–35%). Trihepatnoin is a new therapeutic option with a good safety and efficacy profile. Patients at risk of rhabdomyolysis should ingest MCT or carbohydrates or a combination of both 20 min before exercise. In medium- and short-chain deficits, dietary modifications are not advised (except during exacerbations), with MCT contraindicated and slow sugars recommended 20 min before any significant physical exertion. Parents should be alerted to the need to increase the amount and frequency of carbohydrate intake in stressful situations. The main measure in emergency hospital treatment is the administration of IV glucose. The use of carnitine remains controversial and new therapeutic options are under investigation. Full article
(This article belongs to the Special Issue Nutritional Management of Patients with Inborn Errors of Metabolism)
13 pages, 3360 KiB  
Article
An Evaluation of Different Sweet Olive Cultivars with Different Ripening Degrees Grown in the Puglia Region, Southeastern Italy
by Salem Alhajj Ali, Andrea Mazzeo, Antonio Trani, Simona Pitardi, Sara Bisceglie and Giuseppe Ferrara
Horticulturae 2024, 10(8), 861; https://doi.org/10.3390/horticulturae10080861 - 14 Aug 2024
Abstract
Some olive cultivars grown in southeastern Italy are characterized by the production of olives with a reduced level of bitterness. They are known as sweet olive cultivars and fruits are usually consumed directly or cooked without any debittering process, offering either health benefits [...] Read more.
Some olive cultivars grown in southeastern Italy are characterized by the production of olives with a reduced level of bitterness. They are known as sweet olive cultivars and fruits are usually consumed directly or cooked without any debittering process, offering either health benefits to consumers, thanks to the high content of antioxidants, or an economic benefit to farmers for their higher price with respect to both table and oil olives. This study evaluates and compares the organoleptic, pomological, chemical, and physical parameters of seven sweet olive cultivars at different ripening degrees in the Puglia region over 8 weeks of maturity stage for two consecutive seasons (2022 and 2023). The organoleptic evaluation was performed by a restricted panel of usual consumers/experts of sweet olives. The results showed a higher preference for the olive cultivars locally named Triggiano Dolce, Cerasella, and Mele. Significant differences in weight, length, and width of the fruits were observed based on both cultivar and year. The phenolic composition of olive cultivars was significantly affected by both cultivar and harvest year, with Cazzinicchio and Cellina di Nardò having the highest total polyphenols. The analysis of water fraction extracted from olive samples by liquid chromatography coupled with mass spectrometry led to the identification of eleven compounds belonging to the secoiroids, phenylpropanoids, phenylethanolids, and flavonoids classes. The comparison of these compounds among the studied cultivars highlighted significant differences. Full article
(This article belongs to the Section Medicinals, Herbs, and Specialty Crops)
10 pages, 396 KiB  
Article
The Use of ABILHAND-Kids in Children with Unilateral Congenital Below-Elbow Deficiencies and Acquired Amputation: An Italian Cross-Sectional Study
by Gessica Della Bella, Luigino Santecchia, Paola Luttazi, Giordana Mariani, Lorenzo Pochiero, Alessandra Lacopo, Caterina Delia and Marco Tofani
Children 2024, 11(8), 988; https://doi.org/10.3390/children11080988 - 14 Aug 2024
Abstract
Congenital or acquired hand differences, including unilateral below-elbow deficiencies, present complex challenges in pediatric rehabilitation. Surgical management and prosthetic provision represent a big challenge to find a good balance for guaranteeing optimal hand function. There is no specific assessment tool for measuring these [...] Read more.
Congenital or acquired hand differences, including unilateral below-elbow deficiencies, present complex challenges in pediatric rehabilitation. Surgical management and prosthetic provision represent a big challenge to find a good balance for guaranteeing optimal hand function. There is no specific assessment tool for measuring these aspects in the Italian context. The present study investigates the psychometric properties of the ABILHAND-Kids in children with congenital unilateral below-elbow deficiencies and acquired amputation of the upper limb. We measure internal consistency using Cronbach coefficient alpha and the intraclass correlation coefficient (ICC) for measuring test-retest reliability. Differences in hand function in both children with acquired or congenital diseases were also investigated. Participants to the study were 107 (49 F and 58 M) children, with a mean (SD) age of 8.88 (4.25). For test retest reliability, conducted on a sub-sample of 58 children, the ICC was 0.92, while for internal consistency, the Cronbach coefficient alpha was 0.90. We did not find statistically significant differences in scoring (p = 0.33) in the use (mean 29.25 SD 6.58) or non-use of a prosthetic device (mean 30.74 SD 7.43), while statistically significant differences were found in hand function (p < 0.01) for children who had a congenital impairment (mean 31.87 SD 6.49) and children who had an acquired amputation (mean 27.77 SD 6.60). In conclusion, the ABILHAND-Kids showed good internal consistency and reliability and can capture differences in hand function in children with both congenital and acquired hand disorders. Full article
Show Figures

Figure 1

Figure 1
<p>Mean difference (<span class="html-italic">p</span> &lt; 0.01) in ABILHAND-kids total score according to acquired or congenital impairment.</p>
Full article ">
16 pages, 614 KiB  
Review
The Transition from Childhood to Adolescence: Between Health and Vulnerability
by Francesca Mastorci, Maria Francesca Lodovica Lazzeri, Cristina Vassalle and Alessandro Pingitore
Children 2024, 11(8), 989; https://doi.org/10.3390/children11080989 - 14 Aug 2024
Abstract
Transitioning from childhood into adolescence is an extraordinary time of life, associated with major physical, emotional, cognitive, and social changes and characterized by dynamic development in which interaction with the environment modulates the individual resources responsible for well-being and health. This sensitive period [...] Read more.
Transitioning from childhood into adolescence is an extraordinary time of life, associated with major physical, emotional, cognitive, and social changes and characterized by dynamic development in which interaction with the environment modulates the individual resources responsible for well-being and health. This sensitive period is the time when, in addition to hormonal, metabolic, and neural changes, certain behavioral strategies begin to take shape that will shortly go on to define the emotional, social, and cultural identity of the individual. This narrative review aimed to uncover the crucial processes underlying the transition by identifying processes that are responsible for cognitive, psychosocial, and emotional development, in the absence of disease. For this aim, we highlight (1) the physical, psychological, and social determinants during the transition from childhood to adolescence; (2) the role of health-related variables in resilience or vulnerability mechanisms; and (3) recent school-based strategies to promote health and well-being. Recognizing that health and well-being are the result of the interaction of many biological, psychological, social, cultural, and physical factors will lead to comprehensive health promotion involving all actors joining the growth process, from health professionals and the educational community to parents and community. Furthermore, it is important that psychosocial dimensions are strengthened already during childhood to prevent the onset of frailty and illness in adolescence. Full article
(This article belongs to the Section Global Pediatric Health)
15 pages, 13689 KiB  
Article
Impact of Changing Inlet Modes in Ski Face Masks on Adolescent Skiing: A Finite Element Analysis Based on Head Models
by Minxin Huang, Ruiqiu Zhang and Xiaocheng Zhang
Modelling 2024, 5(3), 936-950; https://doi.org/10.3390/modelling5030049 - 14 Aug 2024
Abstract
Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This [...] Read more.
Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO2 concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future. Full article
Show Figures

Figure 1

Figure 1
<p>RUROC RG1-DX adult ski helmet (airflow mechanism).</p>
Full article ">Figure 2
<p>Ski face protection design, ventilation mode, and size.</p>
Full article ">Figure 3
<p>(<b>a</b>) Fine modeling size of underage man’s nose (nasal length/depth/height); (<b>b</b>) nasal breadth; (<b>c</b>) anterior nostril opening angle/diffusion angle; (<b>d</b>) nose opening angle/diffusion angle.</p>
Full article ">Figure 4
<p>CO<sub>2</sub> distribution in different intake modes.</p>
Full article ">Figure 5
<p>(<b>a</b>) Vorticity research slice position; (<b>b</b>) velocity and temperature research section position.</p>
Full article ">Figure 6
<p>Total vorticity of 5 cross-slices.</p>
Full article ">Figure 7
<p>(<b>a</b>) The point of velocity and temperature section; (<b>b</b>) the analysis area division of ski face protection.</p>
Full article ">Figure 8
<p>(<b>a</b>) Temperatures of different air intake modes in the cavity; (<b>b</b>) velocities of different air intake modes in the cavity; (<b>c</b>) average velocity and temperature at the outlet.</p>
Full article ">
18 pages, 962 KiB  
Article
Nebivolol Polymeric Nanoparticles-Loaded In Situ Gel for Effective Treatment of Glaucoma: Optimization, Physicochemical Characterization, and Pharmacokinetic and Pharmacodynamic Evaluation
by Pradeep Singh Rawat, Punna Rao Ravi, Mohammed Shareef Khan, Radhika Rajiv Mahajan and Łukasz Szeleszczuk
Nanomaterials 2024, 14(16), 1347; https://doi.org/10.3390/nano14161347 - 14 Aug 2024
Abstract
Nebivolol hydrochloride (NEB), a 3rd-generation beta-blocker, was recently explored in managing open-angle glaucoma due to its mechanism of action involving nitric oxide release for the vasodilation. To overcome the issue of low ocular bioavailability and the systemic side effects associated with [...] Read more.
Nebivolol hydrochloride (NEB), a 3rd-generation beta-blocker, was recently explored in managing open-angle glaucoma due to its mechanism of action involving nitric oxide release for the vasodilation. To overcome the issue of low ocular bioavailability and the systemic side effects associated with conventional ocular formulation (aqueous suspension), we designed and optimized polycaprolactone polymeric nanoparticles (NEB-PNPs) by applying design of experiments (DoE). The particle size and drug loading of the optimized NEB-PNPs were 270.9 ± 6.3 nm and 28.8 ± 2.4%, respectively. The optimized NEB-PNPs were suspended in a dual-sensitive in situ gel prepared using a mixture of P407 + P188 (as a thermo-sensitive polymer) and κCRG (as an ion-sensitive polymer), reported previously by our group. The NEB-PNPs-loaded in situ gel (NEB-PNPs-ISG) formulation was characterized for its rheological behavior, physical and chemical stability, in vitro drug release, and in vivo efficacy. The NEB-PNPs-loaded in situ gel, in ocular pharmacokinetic studies, achieved higher aqueous humor exposure (AUC0–t = 329.2 ng × h/mL) and for longer duration (mean residence time = 9.7 h) than compared to the aqueous suspension of plain NEB (AUC0–t = 189 ng × h/mL and mean residence time = 6.1 h) reported from our previous work. The pharmacokinetic performance of NEB-PNPs-loaded in situ gel translated into a pharmacodynamic response with 5-fold increase in the overall percent reduction in intraocular pressure by the formulation compared to the aqueous suspension of plain NEB reported from our previous work. Further, the mean response time of NEB-PNPs-loaded in situ gel (12.4 ± 0.6 h) was three times higher than aqueous suspension of plain NEB (4.06 ± 0.3 h). Full article
(This article belongs to the Topic Advances in Controlled Release and Targeting of Drugs)
26 pages, 31159 KiB  
Article
A Method for Predicting High-Resolution 3D Variations in Temperature and Salinity Fields Using Multi-Source Ocean Data
by Xiaohu Cao, Chang Liu, Shaoqing Zhang and Feng Gao
J. Mar. Sci. Eng. 2024, 12(8), 1396; https://doi.org/10.3390/jmse12081396 - 14 Aug 2024
Abstract
High-resolution three-dimensional (3D) variations in ocean temperature and salinity fields are of great significance for ocean environment monitoring. Currently, AI-based 3D temperature and salinity field predictions rely on expensive 3D data, and as the prediction period increases, the stacking of high-resolution 3D data [...] Read more.
High-resolution three-dimensional (3D) variations in ocean temperature and salinity fields are of great significance for ocean environment monitoring. Currently, AI-based 3D temperature and salinity field predictions rely on expensive 3D data, and as the prediction period increases, the stacking of high-resolution 3D data greatly increases the difficulty of model training. This paper transforms the prediction of 3D temperature and salinity into the prediction of sea surface elements and the inversion of subsurface temperature and salinity using sea surface elements, by leveraging the relationship between sea surface factors and subsurface temperature and salinity. This method comprehensively utilizes multi-source ocean data to avoid the issue of data volume caused by stacking high-resolution historical data. Specifically, the model first utilizes 1/4° low-resolution satellite remote sensing data to construct prediction models for sea surface temperature (SST) and sea level anomaly (SLA), and then uses 1/12° high-resolution temperature and salinity data as labels to build an inversion model of subsurface temperature and salinity based on SST and SLA. The prediction model and inversion model are integrated to obtain the final high-resolution 3D temperature and salinity prediction model. Experimental results show that the 20-day prediction results in the two sea areas of the coastal waters of China and the Northwest Pacific show good performance, accurately predicting ocean temperature and salinity in the vast majority of layers, and demonstrate higher resource utilization efficiency. Full article
(This article belongs to the Section Physical Oceanography)
Show Figures

Figure 1

Figure 1
<p>Data partitioning for prediction and inversion. Figure (<b>a</b>) shows the division of forecast data, and figure (<b>b</b>) shows the division of inversion data.</p>
Full article ">Figure 2
<p>SimVP-gsta model. Figure (<b>a</b>) shows the SimVP–gsta model, and figure (<b>b</b>) shows the gsta module.</p>
Full article ">Figure 3
<p>M-ViT model. Figure (<b>a</b>) shows the M–ViT model, and figure (<b>b</b>) shows the Mobile–ViT module.</p>
Full article ">Figure 4
<p>Three-dimensional prediction model.</p>
Full article ">Figure 5
<p>Bilinear interpolation model.</p>
Full article ">Figure 6
<p>Loss function settings in the inversion experiment.</p>
Full article ">Figure 7
<p>Visualization of the average SST prediction error over 20 days in the Coastal Waters of China. The MAE chart is on the left, and the RMSE chart is on the right.</p>
Full article ">Figure 8
<p>Visualization of the average SST prediction error over 20 days in the Northwest Pacific. The MAE chart is on the left, and the RMSE chart is on the right.</p>
Full article ">Figure 9
<p>Visualization of the average SLA prediction error over 20 days in the Coastal Waters of China. The MAE chart is on the left, and the RMSE chart is on the right.</p>
Full article ">Figure 10
<p>Visualization of the average SLA prediction error over 20 days in the Northwest Pacific. The MAE chart is on the left, and the RMSE chart is on the right.</p>
Full article ">Figure 11
<p>Visualization of errors in 48-layer temperature and salinity inversion in the Coastal Waters of China. Figure (<b>a</b>) represents the temperature error, with the MAE chart on the left and the RMSE chart on the right. Figure (<b>b</b>) represents the salinity error, with the MAE chart on the left and the RMSE chart on the right.</p>
Full article ">Figure 12
<p>Visualization of errors in 48-layer temperature and salinity inversion in the Northwest Pacific. Figure (<b>a</b>) represents the temperature error, with the MAE chart on the left and the RMSE chart on the right. Figures (<b>b</b>) represents the salinity error, with the MAE chart on the left and the RMSE chart on the right.</p>
Full article ">Figure 13
<p>Three-dimensional sea temperature prediction error curves in the Coastal Waters of China. Figures (<b>a</b>–<b>d</b>) show the average MAE for temperature every 5 days from 1 to 20 days, and figures (<b>e</b>–<b>h</b>) show the average RMSE for temperature.</p>
Full article ">Figure 14
<p>Three-dimensional salinity prediction error curves in the Coastal Waters of China. Figures (<b>a</b>–<b>d</b>) show the average MAE for salinity, and figures (<b>e</b>–<b>h</b>) show the average RMSE for salinity.</p>
Full article ">Figure 15
<p>Visualization of the 3D temperature and salinity predictions for day 1 in the Coastal Waters of China. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
Full article ">Figure 16
<p>Visualization of the 3D temperature and salinity predictions for day 20 in the Coastal Waters of China. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
Full article ">Figure 17
<p>Three-dimensional sea temperature prediction error curves in the Northwest Pacific. Figures (<b>a</b>–<b>d</b>) show the average MAE for temperature every 5 days from 1 to 20 days, and figures (<b>e</b>–<b>h</b>) show the average RMSE for temperature.</p>
Full article ">Figure 18
<p>Three-dimensional sea salinity prediction error curves in the Northwest Pacific. Figures (<b>a</b>–<b>d</b>) show the average MAE for salinity, and figures (<b>e</b>–<b>h</b>) show the average RMSE for salinity.</p>
Full article ">Figure 19
<p>Visualization of the 3D temperature and salinity predictions for day 1 in the Northwest Pacific. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
Full article ">Figure 20
<p>Visualization of the 3D temperature and salinity predictions for day 20 in the Northwest Pacific. (<b>a</b>) Temperature, (<b>b</b>) salinity.</p>
Full article ">Figure 21
<p>Visualization of temperature and salinity prediction errors. The areas highlighted in the figure are the regions with larger forecast errors in this instance.</p>
Full article ">Figure 22
<p>The RMSE curves for temperature with different methods. Figures (<b>a</b>–<b>d</b>) show the errors averaged every 5 days for the 20-day temperature and salinity forecasts. Orange and red box indicate the positions where error changes are noticeably variable over time.</p>
Full article ">Figure 23
<p>The RMSE curves for salinity with different methods. Figures (<b>a</b>–<b>d</b>) show the errors averaged every 5 days for the 20-day temperature and salinity forecasts. Orange and red box indicate the positions where error changes are noticeably variable over time.</p>
Full article ">
21 pages, 2357 KiB  
Article
Trustworthiness for an Ultra-Wideband Localization Service
by Philipp Peterseil, Bernhard Etzlinger, Jan Horáček, Roya Khanzadeh and Andreas Springer
Sensors 2024, 24(16), 5268; https://doi.org/10.3390/s24165268 - 14 Aug 2024
Abstract
Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including the attributes of reliability, security, privacy, and resilience. Our [...] Read more.
Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including the attributes of reliability, security, privacy, and resilience. Our goal is to provide guidance for evaluating a system’s trustworthiness based on objective evidence, i.e., so-called trustworthiness indicators. These indicators are carefully selected through the threat analysis of the particular system under evaluation. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats. Moreover, experimental evaluations are conducted to demonstrate the effectiveness of the proposed method. While the framework is tailored for this specific use case, the process itself serves as a versatile template, which can be used in other applications in the domains of the Internet of Things or cyber–physical systems. Full article
(This article belongs to the Special Issue Microwave Sensing Systems)
26 pages, 29445 KiB  
Article
Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia
by Andualem Shiferaw, Tsegaye Tadesse, Clinton Rowe and Robert Oglesby
Atmosphere 2024, 15(8), 974; https://doi.org/10.3390/atmos15080974 - 14 Aug 2024
Abstract
Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate [...] Read more.
Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast. Full article
(This article belongs to the Special Issue Climate Change and Regional Sustainability in Arid Lands)
Show Figures

Figure 1

Figure 1
<p>Model domain and topography (<b>left</b>) and location of weather stations used for verification (<b>right</b>).</p>
Full article ">Figure 2
<p>Experimental set up. Each row contains experiments with KF, BMJ, and Grell CU schemes. First 3 columns: YSU PBL; columns 4–6: experiments with MYJ PBL scheme; and columns 7–9: ACM2 PBL scheme. Columns 1, 4, 7: WSM6 MP scheme; columns 2, 5, 8: LIN MP scheme; and columns 3, 6, 9: Morrison MP scheme. All 27 experiments utilize RRTM LW and Dudhia SW radiation schemes.</p>
Full article ">Figure 3
<p>Mean summer (JJA) rainfall (mm/day) in 2002 for (<b>a</b>) CHIRPS, (<b>b</b>) ENACTS, and WRF simulation. Simulations (<b>c</b>–<b>k</b>), (<b>l</b>–<b>t</b>), and (<b>u</b>–<b>ad</b>) used KF, BMJ, and Grell CU schemes, respectively. Rows 1–3: YSU; rows 4–6: MYJ; and rows 7–9: ACM2 PBL schemes. Rows 1, 4, and 7: WSM6; rows 2, 5, and 8: LIN; and rows 3, 6, and 9: Morrison MP scheme (<a href="#atmosphere-15-00974-f002" class="html-fig">Figure 2</a>).</p>
Full article ">Figure 4
<p>Pattern correlation coefficient (PCC) for JJA mean rainfall between 27 WRF simulations and (<b>a</b>) CHIRPS and (<b>b</b>) ENACTS.</p>
Full article ">Figure 5
<p>Observed number of rainy days over weather stations (<b>top</b>) and bias in WRF-simulated number of rainy days (<b>bottom</b>) during JJA season of 2002 compared to weather stations. For rainy day bias, simulations E1–E9, E10–E18, and E19–E27 used KF, BMJ, and Grell CU schemes, respectively. Columns 1–3: YSU; columns 4–6: MYJ; and columns 7–9: ACM2 PBL schemes. Columns 1, 4, and 7: WSM6; columns 2, 5, and 8: LIN; and columns 3, 6, and 9: Morrison MP scheme.</p>
Full article ">Figure 6
<p>Observed mean rainfall intensity (total rainfall/number of rainy days) (top) and bias in WRF-simulated mean rainfall intensity (E1–E27) compared to weather stations during JJA season of 2002 for 27 experiments. For intensity bias (E1–E27) day bias, simulations E1–E9, E10–E18, and E19–E27 used KF, BMJ, and Grell CU schemes, respectively. Columns 1–3: YSU; columns 4–6: MYJ; and columns 7–9: ACM2 PBL schemes. Columns 1, 4, and 7: WSM6; columns 2, 5, and 8: LIN; and columns 3, 6, and 9: Morrison MP scheme.</p>
Full article ">Figure 7
<p>Comparison of observed and simulated daily precipitation (mm) during the summer of 2002 at selected weather stations: (<b>a</b>) Addis Ababa (Bole) (central Ethiopia: 8.985<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 38.786<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E), (<b>b</b>) Dangila (northwestern Ethiopia: 11.434<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 36.846<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E), (<b>c</b>) Mekele (northern Ethiopia: 13.467<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 39.533<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E), (<b>d</b>) Dire Dawa (eastern Ethiopia: 9.625<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 41.854<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E), (<b>e</b>) Asaita (northeastern Ethiopia: 11.53<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 41.53<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E), and (<b>f</b>) Bure (western Ethiopia: 8.233<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> N, 35.1<math display="inline"><semantics> <msup> <mo> </mo> <mo>∘</mo> </msup> </semantics></math> E). Observed precipitation is shown in black, with simulation using KF CU (E1–E9) in blue, BMJ CU (E10–E18) in green, and Grell CU in red).</p>
Full article ">Figure 8
<p>Pearson correlation coefficient between observed and simulated daily precipitation during the summer of 2002 for 27 WRF simulations. Y-axis labels show cumulus, PBL, and microphysics schemes for each of the 27 experiments. Red dots indicate the location of weather stations and yellow boxes indicate the weather stations in each geographic group shown as G1 to G7 on the x-axis.</p>
Full article ">Figure 9
<p>Mean JJA rainfall (mm/day): (<b>a</b>) observed (weather station), (<b>b</b>) E1 (LW/SW: RRTM/Dudhai), (<b>c</b>) E28 (LW/SW: RRTMG/Dudhia); (<b>d</b>) E29 (LW/SW: RRTMG/RRTM); and (<b>e</b>) E30 (LW/SW: RRTMG/RRTMG).</p>
Full article ">Figure 10
<p>Mean JJA rainfall (mm/day): (<b>a</b>) CHIRPS, (<b>b</b>) ENACT, (<b>c</b>) E1 (LW/SW: RRTM/Dudhai), (<b>d</b>) E28 (LW/SW: RRTMG/Dudhia); (<b>e</b>) E29 (LW/SW: RRTMG/RRTM); and (<b>f</b>) E30 (LW/SW: RRTMG/RRTMG).</p>
Full article ">Figure 11
<p>Rank category based on aggregate score (AS).</p>
Full article ">Figure A1
<p>(<b>a</b>) Annual rainfall climatology: percent contribution of (<b>b</b>) March–May; (<b>c</b>) June–August; (<b>d</b>) September–November; and (<b>e</b>) December–February seasons to annual rainfall. Climatology based on 35 years of CHIRPS data.</p>
Full article ">Figure A2
<p>Bias in WRF-simulated mean summer (JJA) rainfall (mm/day) in 2002. Simulations in rows 1–3 used KF, BMJ, and Grell CU schemes, respectively. Columns 1–3, 4–6, and 7–9 used YSU, MYJ, and ACM2 PBL schemes, respectively. Columns 1, 4, and 7 used the WSM6 scheme; 2, 5, and 8 the LIN scheme; and 3, 6, and 9 the Morrison MP scheme (<a href="#atmosphere-15-00974-f002" class="html-fig">Figure 2</a>).</p>
Full article ">
30 pages, 394 KiB  
Review
CPT Symmetry Searches in the Neutral Meson System
by Ágnes Roberts
Particles 2024, 7(3), 717-746; https://doi.org/10.3390/particles7030042 - 14 Aug 2024
Abstract
A review of the landscape of CPT symmetry tests is presented, centered around the Standard-Model Extension and focusing on tests in the neutral meson system. A discussion of the relevant theories summarizes original ideas. It is followed by a short transition into phenomenology. [...] Read more.
A review of the landscape of CPT symmetry tests is presented, centered around the Standard-Model Extension and focusing on tests in the neutral meson system. A discussion of the relevant theories summarizes original ideas. It is followed by a short transition into phenomenology. A more detailed parameterization is presented. Various experiments are used to deliver an overview of testing CPT from every angle that the theory suggested and that the neutral meson (NM) system could accommodate. Full article
(This article belongs to the Special Issue Feature Papers for Particles 2023)
23 pages, 3307 KiB  
Article
Role of Extracellular Vesicles in Crohn’s Patients on Adalimumab Who Received COVID-19 Vaccination
by Maria De Luca, Biagia Musio, Francesco Balestra, Valentina Arrè, Roberto Negro, Nicoletta Depalo, Federica Rizzi, Rita Mastrogiacomo, Giorgia Panzetta, Rossella Donghia, Pasqua Letizia Pesole, Sergio Coletta, Emanuele Piccinno, Viviana Scalavino, Grazia Serino, Fatima Maqoud, Francesco Russo, Antonella Orlando, Stefano Todisco, Pietro Mastrorilli, Maria Lucia Curri, Vito Gallo, Gianluigi Giannelli and Maria Principia Scavoadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2024, 25(16), 8853; https://doi.org/10.3390/ijms25168853 - 14 Aug 2024
Abstract
Crohn’s disease (CD) is a type of inflammatory bowel disease (IBD) affecting the gastrointestinal tract that can also cause extra-intestinal complications. Following exposure to the mRNA vaccine BNT162b2 (Pfizer-BioNTech) encoding the SARS-CoV-2 Spike (S) protein, some patients experienced a lack of response to [...] Read more.
Crohn’s disease (CD) is a type of inflammatory bowel disease (IBD) affecting the gastrointestinal tract that can also cause extra-intestinal complications. Following exposure to the mRNA vaccine BNT162b2 (Pfizer-BioNTech) encoding the SARS-CoV-2 Spike (S) protein, some patients experienced a lack of response to the biological drug Adalimumab and a recrudescence of the disease. In CD patients in progression, resistant to considered biological therapy, an abnormal increase in intestinal permeability was observed, more often with a modulated expression of different proteins such as Aquaporin 8 (AQP8) and in tight junctions (e.g., ZO-1, Claudin1, Claudin2, Occludin), especially during disease flares. The aim of this study is to investigate how the SARS-CoV-2 vaccine could interfere with IBD therapy and contribute to disease exacerbation. We investigated the role of the SARS-CoV-2 Spike protein, transported by extracellular vesicles (EVs), and the impact of various EVs components, namely, exosomes (EXOs) and microvesicles (MVs), in modulating the expression of molecules involved in the exacerbation of CD, which remains unknown. Full article
(This article belongs to the Section Molecular Immunology)
Show Figures

Figure 1

Figure 1
<p>The 1D 1H CPMG spectrum of serum (Bruker Avance 400 MHz, D2O). The assignment of NMR signals was performed by comparison with standard compounds. The residual water signal (4.78 ppm) is hidden. The signals assigned to the metabolites are indicated with increasing numbers according to the same criterion adopted in <a href="#app1-ijms-25-08853" class="html-app">Table S1</a> (<b>A</b>). OPLS-DA was applied to the 72 spectra by using UV-scaled 0.04 ppm-sized bucketing. Scores plot for the selected components, where the observations are indicated according to the vaccine time as follows: “<span style="color:red">▲</span>” before the first dose and “<span style="color:#538135">■</span>” before the third dose. The ellipse shows the 95% confidence interval using statistics from the Hotelling T-square test (T2). The ellipses are colored according to the class of samples they include, i.e. pink for the samples before the first dose of vaccine and green for the samples before the third dose of vaccine (<b>B</b>). Analysis of the Variable Importance in Projection (VIP) identified by OPLS-DA. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group under study. The samples class is indicated as blue and red squares for serum samples collected before the first dose of vaccine (T0) and before the third (T2), respectively (<b>C</b>). OPLS-DA was applied to the 72 spectra using UV-scaled 0.04 ppm-sized bucketing. Scores plot for the selected components, where the observations are indicated according to the responsiveness to the therapy administered as follows: “<span style="color:red">∆</span>” non-responder and “+” responder. The ellipse shows the 95% confidence interval using statistics from the Hotelling T-square test (T2). The ellipses are colored according to the class of samples they include, i.e. pink for the non-responder and green for the responder (<b>D</b>). Analysis of the Variable Importance in Projection (VIP) identified by OPLS-DA. The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group under study. The samples class is indicated as blue and red squares for serum samples collected from patients who responded to the therapy and those who did not respond, respectively (<b>E</b>).</p>
Full article ">Figure 2
<p>EV chemical–physical characterization. EXOs and MVs extracted from the sera of patients with CD, including responders and non-responders to Adalimumab, treated with the BNT162b2 mRNA-Pfizer COVID-19 vaccine at baseline, before the first dose (T0), and before the third dose (T2). Representative intensity size distribution by DLS analysis (<b>A</b>) and TEM micrographs. Scale bar: 200 nm (<b>B</b>). The recorded average hydrodynamic diameter and polydispersity index (PDI) obtained by DLS analysis and the ζ-potential value (mean ± SD) are reported in the provided table (<b>C</b>). Evaluation of Adalimumab in the two EV subpopulations (EXOs and MVs) derived from R and NR patients, at T0 and T2. NR T2 vs. R T2 (<b>D</b>). Evaluation of MV and EXO proteins isolated from serum specimens derived from patients with CD and treated with the BNT162b2 mRNA-Pfizer COVID-19 vaccine at baseline (T0, before the first dose) and before the third dose (T2), responders or non-responders to Adalimumab before the third dose. Representative Western blotting of different proteins (Spike, ACE, AQP8) and housekeeping protein (Annexin 1 for MV and CD81 for EXO) (<b>E</b>,<b>F</b>). Semiquantitative evaluation of the considered protein expression levels in MVs and EXOs obtained from patients with CD after the BNT162b2 mRNA-Pfizer COVID-19 vaccine, both Rs and NRs to Adalimumab by video-densitometry analysis of Spike, ACE2, and AQP8 bands on Western blotting. The Annexin 1 and CD81 protein bands were used to normalize the protein band for each subject. (**) <span class="html-italic">p</span> &lt; 0.001 T2 vs. T0 for R and NR conditions, respectively (<b>G</b>,<b>H</b>).</p>
Full article ">Figure 3
<p>Cell permeability assay of HCEC-1CT cell layers as a function of time upon challenging with EVs derived from the serum of R and NR patients (<b>A</b>–<b>C</b>). The control was untreated cells. For each diagram (<b>B</b>–<b>D</b>), the error bars represent the standard deviation of the mean over five experiments with different EVs. (<b>B</b>) TEER of HCEC-1CT cell layers during challenges with EXO T0 and T2 and MV T0 and T2 derived from R patients’ sera. (<b>D</b>) TEER of HCEC-1CT cell layers during challenges with EXO T0 and T2 and MV T0 and T2 derived from NR patients’ sera. ** <span class="html-italic">p</span> &lt; 0.001. Scale bar: 100 μm.</p>
Full article ">Figure 4
<p>Droplet digital PCR analysis of OCLN, CLDN2, ACE, ABCC8 (SUR), ABCC9 (SUR2), KCNJ8, KCNJ11, SCN5A, and SCN2A. HCEC-1CT cells treated with serum-derived EXOs and MVs from patients with CD, R and NR patients at T0 (before the first dose of the BNT162b2 mRNA-Pfizer COVID-19 vaccine at baseline) and before the third dose (T2). The value of copies/μL for OCLN is reported in (<b>A</b>). Average values are reported in (<b>B</b>). The value of copies/μL for CLDN2 is reported in (<b>C</b>). Average values are reported in (<b>D</b>). The value of copies/μL ACE is reported in (<b>E</b>). Average values are reported in (<b>F</b>). The value of copies/μL for ABCC8 (SUR), ABCC9 (SUR2), KCNJ8, and KCNJ11 are reported in (<b>G</b>). Average values are reported in (<b>H</b>). The value of copies/μL for SCN2A is reported in (<b>I</b>). Average values are reported in (<b>J</b>). The value of copies/μL for SCN5A are reported in (<b>K</b>). Average values are reported in (<b>L</b>). The <span class="html-italic">p</span>-value was determined by one-way ANOVA, * <span class="html-italic">p</span> &lt; 0.005 and ** <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 5
<p>Evaluation of several proteins involved in tight junction formation, adherent junctions, and water channels in HCEC-1CT cells treated with different concentrations of urea or with MVs and EXOs isolated from serum specimens derived from patients with CD and treated with the BNT162b2 mRNA-Pfizer COVID-19 vaccine at baseline (T0, before the first dose) and before the third dose (T2), responders or non-responders to Adalimumab before the third dose. Representative Western blotting of different proteins (ZO-1, OCLN, CLDN1 CLDN2, E-CAD, and AQP8) and the housekeeping protein (GAPDH) (<b>A</b>) after treatment with urea. Semiquantitative evaluation of considered protein expression levels in HCEC-1CT (<b>B</b>). Representative Western blotting of cells treated with EXOs and MVs for the same proteins (<b>C</b>) and semiquantitative evaluation of the considered proteins. The GAPDH protein band was used to normalize the protein band in each subject. (*) <span class="html-italic">p</span> &lt; 0.005 and (**) <span class="html-italic">p</span> &lt; 0.001 T2 vs. T0 (<b>D</b>). Representative confocal microscopy images of HCEC-1CT cells for the detection of ZO-1, OCLN, CLDN-2, and E-CAD by immunofluorescence. Blue channel: nuclei; green channel: labeled ZO-1, OCLN, CLDN-2, and E-CAD in overlay images for treatment with EVs derived from R patients (<b>E</b>) and NR patients (<b>F</b>). Scale bar: 50 μm. Magnification: ×20. Semiquantitative evaluation of ZO-1, OCLN, CLDN-2, and E-CAD expression levels in HCEC-1CT treated with EVs extracted from R patients (<b>G</b>) and NR patients (<b>H</b>) by fluorescence expression levels, quantitatively evaluated as the mean green intensity index in cells by immunofluorescence. (*) <span class="html-italic">p</span> &lt; 0.005 and (**) <span class="html-italic">p</span> &lt; 0.001 T2 vs. T0.</p>
Full article ">
13 pages, 411 KiB  
Article
Nutritional Health Risk (Food Security) in Thai Older Adults and Related Factors
by Teeranut Harnirattisai, Sararud Vuthiarpa, Lisa Renee Pawloski, Kevin Michael Curtin, Eden Blackwell, Jenny Nguyen and Sophia Madeleine Bourgeois
Nutrients 2024, 16(16), 2703; https://doi.org/10.3390/nu16162703 - 14 Aug 2024
Abstract
The older adult population in Thailand has been steadily increasing in recent years, and urbanization has resulted in many older adults living independently, leaving many at nutritional risk. The purpose of this research is to explore food security among Thai older adults using [...] Read more.
The older adult population in Thailand has been steadily increasing in recent years, and urbanization has resulted in many older adults living independently, leaving many at nutritional risk. The purpose of this research is to explore food security among Thai older adults using a simple screening tool, the DETERMINE tool, as well as from three surveys which reflect seniors’ health and ultimately food security including the mini-mental state examination (MMSE), the self-efficacy for physical activity scale (SEPAS), and the health literacy questionnaire. The DETERMINE tool was used in Thailand for the first time in this study. The findings revealed a moderate risk of food insecurity amongst participants, as most of them claimed to have underlying diseases, eat alone, eat a few nutrient-rich foods, and take medication. The MMSE, SEPAS, and health literacy questionnaire results suggested that food security was found to be negatively correlated with higher cognitive ability, higher physical activity, self-efficacy, and higher health literacy. In conclusion, there appears to be a high risk for malnutrition among older adults in Thailand, particularly in those with low income and underlying diseases. Full article
(This article belongs to the Special Issue Nutrition and Food Security for All: A Step towards the Future)
17 pages, 8202 KiB  
Article
Using Dynamic Laser Speckle Imaging for Plant Breeding: A Case Study of Water Stress in Sunflowers
by Sherif Bouzaouia, Maxime Ryckewaert, Daphné Héran, Arnaud Ducanchez and Ryad Bendoula
Sensors 2024, 24(16), 5260; https://doi.org/10.3390/s24165260 - 14 Aug 2024
Abstract
This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress [...] Read more.
This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool. Full article
(This article belongs to the Section Smart Agriculture)
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Pictures of plants on the measurement bench (<b>a</b>) variety T, and (<b>b</b>) variety S.</p>
Full article ">Figure 2
<p>Diagram of the biospeckle acquisition system.</p>
Full article ">Figure 3
<p>For each realisation and acquisition (<b>a</b>), 40 biospeckle images are captured (<b>b</b>). A Fujii activity map is generated from these images (<b>c</b>), and a mask is created (<b>d</b>) based on a threshold on this map. This mask is then applied to the 40 biospeckle images to obtain the midrib segmentation (<b>e</b>). A THSP matrix is computed (<b>f</b>) for each pixel of the segmented midrib, and a COM matrix is computed (<b>g</b>). Finally, AVD index is calculated from the COM matrix (<b>h</b>).</p>
Full article ">Figure 4
<p>Raw biospeckle images at the beginning of the experiment for two control plants of the S variety, (<b>a</b>) realisation 5 and (<b>b</b>) realisation 6 and on an inert object (<b>c</b>) realisation 7. The colour bar represents greyscale intensity.</p>
Full article ">Figure 5
<p>Biospeckle activity maps calculated by Fujii method at the beginning of experiment for realisations 4 (<b>a</b>), 5 (<b>c</b>), and 7 (<b>e</b>) (inert object). At the end of experiment, these are, respectively, (<b>b</b>,<b>d</b>,<b>f</b>). The colour bar represents the intensity of biospeckle activity across 40 biospeckle images.</p>
Full article ">Figure 6
<p>Masks obtained for isolating the midrib for (<b>a</b>) realisation 4 and (<b>b</b>) realisation 5 of the S variety. The masks are calculated using a threshold of the Fujii values.</p>
Full article ">Figure 7
<p>COM matrices of midrib pixels: (<b>a</b>,<b>b</b>) inert object at the beginning and end of the experiment. (<b>c</b>,<b>d</b>) realisation 5 at the beginning and at the end of the experiment, respectively, and (<b>e</b>,<b>f</b>) realisation 4 at the beginning and end of the experiment, respectively. The colour bar represents the identical occurrences of an intensity level “i” at level “j” in two consecutive intensity samples within a THSP matrix. Intensity values are displayed in log-scale.</p>
Full article ">Figure 8
<p>AVD index calculated on an inert object with the normalised COM matrix <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (AVD1) (<b>a</b>) and <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </semantics></math> (AVD4) (<b>b</b>).</p>
Full article ">Figure 9
<p>AVD index calculated on a control sunflower plant (realisation 5 in green and 6 in blue) of the S variety with the normalized COM matrix <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (AVD1) (<b>a</b>), and <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </semantics></math> (AVD4) (<b>b</b>).</p>
Full article ">Figure 10
<p>Biospeckle activity calculated by AVD. Colours yellow and orange refer, respectively, to realisations 3 and 4, while dotted lines correspond to PEG introduction with the normalized COM matrix <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </semantics></math> (<b>b</b>).</p>
Full article ">Figure 11
<p>Biospeckle activity calculated by AVD with realisation 1 in purple and 2 in blue. Dotted lines correspond to PEG introduction with the normalized COM matrix <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <msub> <mi>M</mi> <mrow> <mi>i</mi> <mi>j</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </semantics></math> (<b>b</b>).</p>
Full article ">Figure 12
<p>Value of AVD1 (<b>a</b>) and AVD4 (<b>b</b>) for realisations 1, 2, 3 and 4 (respectively purple, blue, yellow and orange) with their respective regression lines, after introduction of the PEG. Each curve is normalised by its highest value.</p>
Full article ">
Back to TopTop