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BackgroundThe incidence of mental health issues in children is increasing worldwide. In Chile, a recent surge in reports of deteriorating mental health among school populations and an increase in complaints related to poor school climate have been observed. Physical activity, specifically active breaks in the classroom, has shown positive effects on children’s health. However, evidence regarding its impact on mental health and school climate in children is limited.ObjectiveThis work outlines the design, measurements, intervention program, and potential efficacy of the “Active Classes + School Climate and Mental Health” project. This project will assess a 12-week program of active breaks through guided videos with curricular content in the school classroom, and its effects on mental health and school climate as its primary indicators. Additionally, it will measure physical activity, physical fitness, motor competence, and academic performance in students aged 6–10 years in the Biobío province, Chile, as secondary indicators. Methodology: A multicenter randomized controlled trial involving 823 students from 1st to 4th grade (6–10 years old), six schools (three intervention and three control) will be conducted in the Biobío region, Chile. Participants belonging to the intervention group will implement video-guided active breaks through the “Active Classes” web platform, featuring curricular content, lasting 5–10 min and of moderate to vigorous intensity physical activity, twice a day, Monday to Friday, over a span of 12 weeks. Expected Results/Discussion: To our knowledge, this will be the first study in Chile to evaluate the effects of incorporating video-guided active breaks with curricular content on mental health variables and school climate in schoolchildren. Thus, this study contributes to the scarce evidence on the effects of video-guided active breaks on mental health variables and school climate in schoolchildren worldwide. Additionally, it will provide crucial information about active teaching methodologies that have the potential to positively contribute to the wellbeing of students, thus addressing the problems of mental health and climate in Chilean schools. ClinicalTrials.gov ID NCT06423404.

Imaging is increasingly used to capture information on the marine environment thanks to the improvements in imaging equipment, devices for carrying cameras and data storage in recent years. In that context, biologists, geologists, computer specialists and end-users must gather to discuss the methods and procedures for optimising the quality and quantity of data collected from images. The 4th Marine Imaging Workshop was organised from 3-6 October 2022 in Brest (France) in a hybrid mode. More than a hundred participants were welcomed in person and about 80 people attended the online sessions. The workshop was organised in a single plenary session of presentations followed by discussion sessions. These were based on dynamic polls and open questions that allowed recording of the imaging community’s current and future ideas. In addition, a whole day was dedicated to practical sessions on image analysis, data standardisation and communication tools. The format of this edition allowed the participation of a wider community, including lower-income countries, early career scientists, all working on laboratory, benthic and pelagic imaging.This article summarises the topics addressed during the workshop, particularly the outcomes of the discussion sessions for future reference and to make the workshop results available to the open public.

Abstract Metabolic syndrome (MetS) is a common disorder involving a cluster of metabolic abnormalities, such as abdominal obesity, hypertension, dyslipidemia, insulin resistance, and atherogenic profile. MetS is characterized by an increase in oxidative stress and a chronic proinflammatory state, which are directly related to the development and progression of this pathology. It has been seen how a healthy lifestyle and good dietary practices are key to improving the different metabolic parameters and, therefore, play a fundamental role in reducing the risk of developing diabetes. The present review focuses on the research evidence related to the therapeutic properties of Lycium barbarum L. in MetS gathered in the last years. Several preclinical studies suggest that L. barbarum extracts are a good dietary supplement for the prevention of cardiovascular diseases in people with MetS. This compound has been used for years in traditional Chinese medicine for the treatment of atrophic gastritis, problems related to the lungs, kidneys, and liver, and as a supplement for eye health. In addition, different in vitro and in vivo studies have been carried out that support the properties attributed to metabolites derived from L. barbarum, such as polysaccharides that have been shown diverse biological activities. In conclusion, L. barbarum extracts have multiple benefits to increase general well‐being and immune function. However, there are a limited number of studies related to effect of L. barbarum in MetS, but they demonstrated effectiveness in the treatment of obesity, diabetes mellitus type 2, and prevention of diabetes mellitus type 2 complication.

Abstract Bioactive compounds in food and beverages, including fruit juices, are susceptible to degradation or oxidation during processing and storage. This vulnerability can lead to a reduction in nutritional value and overall quality of the products. The objective of this research is to explore the potential of encapsulation techniques in preserving and enhancing the nutritional value of fruit juices. The encapsulation of natural compounds, enzymes, and probiotics is seen as a promising approach to fortifying fruit juices, improving their preservation and processing of these encapsulated natural compounds as additives in food packaging. The study involves a comprehensive review of various encapsulation techniques and materials used for encapsulating bioactive compounds. It also investigates the current applications of encapsulated natural products in the preservation of food and beverages. The encapsulation of bioactive compounds has shown the potential in improving the nutritional value and functional properties of fruit juice products and foods. It has also been found to enhance the preservation and processing of these products, contributing to their overall quality and safety. Encapsulation techniques offer a promising avenue for revolutionizing the fruit juice industry by enhancing the nutritional value and shelf life of products. They also present an opportunity for the development of more functional food products. Despite the promising results, more research is needed to fully understand the mechanisms of encapsulation, determine the optimal conditions for encapsulating different ingredients, and assess the effects of encapsulation on the quality and safety of fruit juices. Future studies should focus on these areas to further advance the application of encapsulation techniques in the food and beverage industry. Graphical Abstract

Introducción. Dada la complejidad y costos para determinar el porcentaje de grasa corporal en humanos, se busca establecer nuevas fórmulas para determinar esta variable. La ecuación CUN-BAE usa indicadores simples como sexo e índice de masa corporal (IMC) para estimar el porcentaje de grasa corporal. El objetivo fue determinar el grado de correlación entre el porcentaje de grasa corporal, obtenido mediante ecuación CUN-BAE, IMC y circunferencia de cintura (CC) en adultos chilenos pertenecientes a la muestra de los participantes de la Encuesta Nacional de Salud (ENS) 2016-2017. Metodología. Se incluyeron 5.583 participantes de la ENS 2016-2017, en quienes se obtuvieron medidas antropométricas para determinar IMC, estado nutricional y CC. Se determinó el porcentaje de masa grasa según la ecuación CUN-BAE. La correlación entre IMC, CC y CUN-BAE se analizó mediante el coeficiente de correlación de Pearson (r). Resultados. El IMC y el porcentaje de masa grasa según ecuación CUN-BAE presentaron una fuerte correlación tanto en hombres (r=0,924, p

Automated camera-based sensors are widely used in vessel-based research to monitor plankton and marine particles. However, current methods suffer from the costly and time-consuming requirement of annotating data for fully supervised learning, especially in plankton grouping tasks characterized by long-tailed datasets. In response, we propose a novel self-supervised learning framework that significantly reduces reliance on expensive human annotations by leveraging crucial metadata such as water depth and location. The method comprises three major steps: self-supervised training, innovative sampling, and final classification. It identifies key sample subsets from an unlabeled dataset using a hierarchical clustering approach and incorporates an innovative balancing representative subsampling strategy that addresses the challenge of dataset imbalance and enhances generalizability across diverse plankton classes. Our approach prioritizes discerning representation features observed in images that exhibit correlations with the patterns found in their associated metadata. Furthermore, our method introduces a novel grouping based on the visual perspective selection method, enabling the identification of balanced subset views that depart from traditional class-based categorization. Our experimental results showcase a significant enhancement in image classification accuracy, with a 23% improvement over methods that do not utilize metadata, and attains a macro F1-score of 54% for ten populated species from a severely long-tailed dataset. This is achieved with a mere 0.3% of the entire dataset used for annotation.

Abstract Hypertriglyceridemia (HTG) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD). One of the multiple origins of HTG alteration is impaired lipoprotein lipase (LPL) activity, which is an emerging target for HTG treatment. We hypothesised that early, even mild, alterations in LPL activity might result in an identifiable metabolomic signature. The aim of the present study was to assess whether a metabolic signature of altered LPL activity in a preclinical model can be identified in humans. A preclinical LPL-dependent model of HTG was developed using a single intraperitoneal injection of poloxamer 407 (P407) in male Wistar rats. A rat metabolomics signature was identified, which led to a predictive model developed using machine learning techniques. The predictive model was applied to 140 humans classified according to clinical guidelines as (1) normal, less than 1.7 mmol/L; (2) risk of HTG, above 1.7 mmol/L. Injection of P407 in rats induced HTG by effectively inhibiting plasma LPL activity. Significantly responsive metabolites (i.e. specific triacylglycerols, diacylglycerols, phosphatidylcholines, cholesterol esters and lysophospholipids) were used to generate a predictive model. Healthy human volunteers with the impaired predictive LPL signature had statistically higher levels of TG, TC, LDL and APOB than those without the impaired LPL signature. The application of predictive metabolomic models based on mechanistic preclinical research may be considered as a strategy to stratify subjects with HTG of different origins. This approach may be of interest for precision medicine and nutritional approaches.

The identification of new compounds with potential activity against CXC chemokine receptor type 4 (CXCR4) has been broadly studied, implying several chemical families, particularly AMD3100 derivatives. Molecular modeling has played a pivotal role in the identification of new active compounds. But, has its golden age ended? A virtual library of 450,000 tetraamines of general structure 8 was constructed by using five spacers and 300 diamines, which were obtained from the corresponding commercially available cyclic amines. Diversity selection was performed to guide the virtual screening of the former database and to select the most representative set of compounds. Molecular docking on the CXCR4 crystal structure allowed us to rank the selection and identify those candidate molecules with potential antitumor activity against diffuse large B-cell lymphoma (DLBCL). Among them, compound A{17,18} stood out for being a non-symmetrical structure, synthetically feasible, and with promising activity against DLBCL in in vitro experiments. The focused study of symmetrical-related compounds allowed us to identify potential pre-hits (IC50~20 µM), evidencing that molecular design is still relevant in the development of new CXCR4 inhibitor candidates.

When profit-driven renewal is difficult to implement, many historic districts in China become “frozen” under protection. In the recent social context of “building cities for the people”, public value-oriented renewal of urban historic areas has become crucial in city transformation. This study investigates strategies for historic district regeneration in China by integrating spatial form, urban governance, and public value. We propose a novel framework to analyze the regeneration of historic districts, using Taiyuan’s Bell Tower Street as a case study. The framework distinguishes between two dimensions: spatial and economic–social. In the spatial dimension, we focus on the transformation of spatial form to regenerate public value, emphasizing the attribute of “publicness” as central to urban regeneration efforts. This involves a tiered approach to excavating and upgrading historic districts at macro, meso, and micro levels. The economic–social dimension explores urban governance to enhance public value through collaboration between government, market, and societal actors. This study employs a mixed-methods approach, including document reviews, interviews, field observations, and statistical data analysis. The analysis highlights that a public value-oriented approach to heritage regeneration can balance historical preservation with contemporary urban needs, offering a sustainable model for other cities.

Background: The vegan diet (VEGD) has gained popularity in recent years for ecological and ethical reasons, as well as for its health benefits. In addition to the type of diet, the resistance training program (RTP) plays a fundamental role as one of the main natural anabolic stimuli to increase musculoskeletal mass and reduce fat mass. Methods: The study was a 16-week non-randomized controlled clinical trial consisting of three RTP sessions per week. The sample included 70 Chilean individuals, aged between 18 and 59 years, who had been following a VEGD or omnivorous diet (OMND) for the past 6 months. Four groups were established: Vegan Diet Resistance Training Program (VEGD-RTP), Vegan Diet Control (VEGD-C), Omnivorous Diet Resistance Training Program (OMND-RTP), and Omnivorous Diet Control (OMND-C). Results: The sample consisted of 47 women and 23 men, with a mean age of 30.1 (±8.6) years. A reduction of 1.20% in the percentage of fat mass (%FM) was observed in the VEGD-RTP group (r = 0.554, p = 0.016), as well as a reduction of 0.70 kg in kilograms of fat mass (KFM) (r = 0.480, p = 0.036). The OMND-RTP group decreased %FM by 0.90% (r = 0.210, p = 0.432) and KFM by 0.50 kg (r = 0.109, p = 0.683). Conclusions: RTP combined with VEGD or OMND significantly reduced the percentage of fat mass, although its effect was more significant in the VEGD-RTP participants.

Abstract Here we present a method for using the spatial x–y coordinate of an image cropped from the cylindrical surface of digital 3D drill core images and demonstrate how this spatial metadata can be used to improve unsupervised machine learning performance. This approach is applicable to any data set with known spatial context, however, here it is used to classify 400 m of drillcore imagery into 12 distinct classes reflecting the dominant rock types and alteration features in the core. We modified two unsupervised learning models to incorporate spatial metadata and an average improvement of 25% was achieved over equivalent models that did not utilize metadata. Our semi‐supervised workflow involves unsupervised network training followed by semi‐supervised clustering where a support vector machine uses a subset of M expert labeled images to assign a pseudolabel to the entire data set. Fine‐tuning of the best performing model showed an f1 (macro average) of 90%, and its classifications were used to estimate bulk fresh and altered rock abundance downhole. Validation against the same information gathered manually by experts when the core was recovered during the Oman Drilling Project revealed that our automatically generated data sets have a significant positive correlation (Pearson's r of 0.65–0.72) to the expert generated equivalent, demonstrating that valuable geological information can be generated automatically for 400 m of core with only ∼24 hr of domain expert effort.