Unforeseen plant phenotypic diversity in a dry and grazed world.

Earth harbours an extraordinary plant phenotypic diversity1 that is at risk from ongoing global changes2,3. However, it remains unknown how increasing aridity and livestock grazing pressure-two major drivers of global change4-6-shape the trait covariation that underlies plant phenotypic diversity1,7. Here we assessed how covariation among 20 chemical and morphological traits responds to aridity and grazing pressure within global drylands. Our analysis involved 133,769 trait measurements spanning 1,347 observations of 301 perennial plant species surveyed across 326 plots from 6 continents. Crossing an aridity threshold of approximately 0.7 (close to the transition between semi-arid and arid zones) led to an unexpected 88% increase in trait diversity. This threshold appeared in the presence of grazers, and moved toward lower aridity levels with increasing grazing pressure. Moreover, 57% of observed trait diversity occurred only in the most arid and grazed drylands, highlighting the phenotypic uniqueness of these extreme environments. Our work indicates that drylands act as a global reservoir of plant phenotypic diversity and challenge the pervasive view that harsh environmental conditions reduce plant trait diversity8-10. They also highlight that many alternative strategies may enable plants to cope with increases in environmental stress induced by climate change and land-use intensification.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling.

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Development of a robotic cluster for automated and scalable cell therapy manufacturing.

BACKGROUND AIMS: The production of commercial autologous cell therapies such as chimeric antigen receptor T cells requires complex manual manufacturing processes. Skilled labor costs and challenges in manufacturing scale-out have contributed to high prices for these products. METHODS: We present a robotic system that uses industry-standard cell therapy manufacturing equipment to automate the steps involved in cell therapy manufacturing. The robotic cluster consists of a robotic arm and customized modules, allowing the robot to manipulate a variety of standard cell therapy instruments and materials such as incubators, bioreactors, and reagent bags. This system enables existing manual manufacturing processes to be rapidly adapted to robotic manufacturing, without having to adopt a completely new technology platform. Proof-of-concept for the robotic cluster's expansion module was demonstrated by expanding human CD8+ T cells. RESULTS: The robotic cultures showed comparable cell yields, viability, and identity to those manually performed. In addition, the robotic system was able to maintain culture sterility. CONCLUSIONS: Such modular robotic solutions may support scale-up and scale-out of cell therapies that are developed using classical manual methods in academic laboratories and biotechnology companies. This approach offers a pathway for overcoming manufacturing challenges associated with manual processes, ultimately contributing to the broader accessibility and affordability for personalized immunotherapies.

Early response to Tezepelumab in type-2 severe asthma patients non-responders to other biological treatments: a real-life study.

BACKGROUND: Biologic therapies play a crucial role in the treatment of severe asthma. Tezepelumab, a human monoclonal antibody (mAb), inhibits thymic stromal lymphopoietin, a pivotal factor in the pathophysiology of asthma. Although randomized clinical trials have demonstrated the efficacy of Tezepelumab, evidence gaps remain in real-world scenarios. OBJECTIVE: We sought investigate Tezepelumab's response in a clinical setting, focusing on patients who previously failed to other asthma mAbs. METHODS: Real-life study with severe uncontrolled asthma patients despite mAb treatment, requiring a switch to Tezepelumab. Follow-up was done four to six months after initiation of Tezepelumab. The primary endpoint was to evaluate the response in patients with poor response or intolerance to other mAbs. RESULTS: Nine patients were followed up during 7 months. Patients were predominantly middle-aged females with eosinophilic or eosinophilic-allergic phenotypes. Patients had a median failure rate of 2 mAbs (IQR 2-3), with an uncontrolled asthma (median of 2 severe exacerbations the previous year, airflow obstruction and 78% corticosteroid dependence). Tezepelumab demonstrated after 4 to 6 months of treatment reduce corticosteroid dependence (complete withdrawal in 2/7 patients), no exacerbations in 6/9, symptoms control improvement (Asthma Control Test score improved in 5/9) and modulate lung function (improving in 3/9 patients). These findings align with clinical trial results, suggesting Tezepelumab's potential in real-world settings. CONCLUSION: In real-world scenarios, despite the study's limitations, our results underscore Tezepelumab's promise as a therapeutic option for uncontrolled severe asthma, and may be useful for non-responders to other mAbs. Further studies are needed to corroborate these findings.

Adherence to the Mediterranean Diet and its association with sustainable dietary behaviors, sociodemographic factors, and lifestyle: a cross-sectional study in US University students.

BACKGROUND: Promoting healthy and sustainable diets is increasingly important and the Mediterranean Diet (MD) has been recognized as an appropriate example that can be adapted to different countries. Considering that the college years are the time when US young adults are most likely to adopt unhealthy eating habits, the present study assessed adherence to the MD and the sustainability of dietary behaviors in a nationally representative sample of US university students, aiming to identify crucial levers for improving their eating behaviors. METHODS: MD adherence and the adoption of healthy and sustainable dietary patterns were assessed through the KIDMED and the Sustainable-HEalthy-Diet (SHED Index questionnaires, respectively, administered through an online survey that also included sociodemographic and behavioral questions. Non-parametric and logistic regression analyses were performed. RESULTS: A sample of 1485 participants (median (IQR) age 21.0 (19.0-22.0); 59% women) correctly completed the survey. A medium adherence to the MD was the most prevalent (47%). According to multivariate logistic regression analysis, the likelihood of being more compliant with the MD increased when meeting physical activity recommendations, having a high SHED Index score, having the willingness to purchase and eat healthy and sustainable dishes, eating ultra-processed plant-based meat alternatives foods daily, and regularly attending the university canteen. CONCLUSIONS: Encouraging dietary patterns rich in plant-based foods and with a moderate intake of animal products is crucial to increasing the adoption of healthy and sustainable diets, and university dining services represent a suitable setting to build a supportive environment that educates students on human and planetary health.

The Second Life Metaverse and Its Usefulness in Medical Education After a Quarter of a Century.

The immersive virtual world platform Second Life (SL) was conceived 25 years ago, when Philip Rosedale founded Linden Lab in 1999 with the intention of developing computing hardware that would allow people to immerse themselves in a virtual world. This initial effort was transformed 4 years later into SL, a universally accessible virtual world centered on the user, with commercial transactions and even its own virtual currency, which fully connects with the concept of the metaverse, recently repopularized after the statements of the chief executive officer of Meta (formerly Facebook) in October 2021. SL is considered the best known virtual environment among higher education professionals. This paper aimed to review medical education in the SL metaverse; its evolution; and its possibilities, limitations, and future perspectives, focusing especially on medical education experiences during undergraduate, residency, and continuing medical education. The concept of the metaverse and virtual worlds was described, making special reference to SL and its conceptual philosophy, historical evolution, and technical aspects and capabilities for higher education. A narrative review of the existing literature was performed, including at the same time a point of view from our teaching team after an uninterrupted practical experience of undergraduate and postgraduate medical education in the last 13 years with >4000 users and >10 publications on the subject. From an educational point of view, SL has the advantages of being available 24/7 and creating in the student the important feeling of "being there" and of copresence. This, together with the reproduction of the 3D world, real-time interaction, and the quality of voice communication, makes the immersive experiences unique, generating engagement and a fluid interrelation of students with each other and with their teachers. Various groups of researchers in medical education have developed experiences during these years, which have shown that courses, seminars, workshops and conferences, problem-based learning experiences, evaluations, teamwork, gamification, medical simulation, and virtual objective structured clinical examinations can be successfully carried out. Acceptance from students and faculty is generally positive, recognizing its usefulness for undergraduate medical education and continuing medical education. In the 25 years since its conception, SL has proven to be a virtual platform that connects with the concept of the metaverse, an interconnected, open, and globally accessible system that all humans can access to socialize or share products for free or using a virtual currency. SL remains active and technologically improved since its creation. It is necessary to continue carrying out educational experiences, outlining the organization, objectives, and content and measuring the actual educational impact to make SL a tool of more universal use.

Frequency and intensity of maximal and submaximal demanding scenarios in U19 professional soccer players.

This study examined the frequency of the most demanding scenarios (MDS) during official soccer matches using rolling averages over 1, 5, and 10 min. Forty-two Under-19 players from different positions (central defenders, full-backs, central midfielders, wide midfielders, offensive midfielders, and forwards) were monitored across 27 matches using GPS to track distance covered, high-speed running, sprint distance, accelerations, and decelerations. Intensity thresholds were established based on percentiles (0-25, 25-50, 50-75, 75-100, and ≥100). The main findings suggest that: (i) Peak Demands occur in less than 1% of all time windows and variables for all positions; (ii) Most efforts occur below peak demands, with around 95% for high-speed running and sprint distance, and 85% for accelerations, decelerations, and total distance; (iii) Significant differences in intensity distributions were found between positions, particularly at medium-low and high intensities. Regarding training prescription, relying solely on MDS may be limited, highlighting the need to supplement MDS with other metrics for a comprehensive understanding of match demands. This approach ensures better-informed training programs for soccer players.

The occurrence of peak demand events during soccer matches is infrequent. Therefore, understanding the frequency of efforts below the intensity threshold of the most demanding passages for each playing position and across various analysed variables and periods is of utmost importance.MDS might not be enough for effective soccer training planning. Complementing with additional metrics like GPS, tactical analysis, physiological data and psychological factors is essential for a comprehensive understanding of the game's demands and tailored training programmes.

RNA Expression of MMP12 Is Strongly Associated with Inflammatory Bowel Disease and Is Regulated by Metabolic Pathways in RAW 264.7 Macrophages.

Macrophage metalloelastase or matrix metalloproteinase-12 (MMP12) is a macrophage-specific proteolytic enzyme involved in the physiopathology of many inflammatory diseases, including inflammatory bowel disease. Although previously published data suggested that the modulation of MMP12 in macrophages could be a determinant for the development of intestinal inflammation, scarce information is available on the mechanisms underlying the regulation of MMP12 expression in those phagocytes. Therefore, in this study, we aimed to delineate the association of MMP12 with inflammatory bowel disease and the molecular events leading to the transcriptional control of this metalloproteinase. For that, we used publicly available transcriptional data. Also, we worked with the RAW 264.7 macrophage cell line for functional experiments. Our results showed a strong association of MMP12 expression with the severity of inflammatory bowel disease and the response to relevant biological therapies. In vitro assays revealed that the inhibition of mechanistic target of rapamycin complex 1 (mTORC1) and the stimulation of the AMP-activated protein kinase (AMPK) signaling pathway potentiated the expression of Mmp12. Additionally, AMPK and mTOR required a functional downstream glycolytic pathway to fully engage with Mmp12 expression. Finally, the pharmacological inhibition of MMP12 abolished the expression of the proinflammatory cytokine Interleukin-6 (Il6) in macrophages. Overall, our findings provide a better understanding of the mechanistic regulation of MMP12 in macrophages and its relationship with inflammation.

Analysis of Madrid Primary Health-Care staff for the implementation of exercise prescription.

OBJECTIVE: To assess the self-perception of nurses and general practitioners (GPs) toward Physical Activity on Prescription (PAP) in Madrid Primary Health-Care (PHC). DESIGN: A survey-cohort study. SITE: Nurses and GPs of Madrid PHC System. PARTICIPANTS: A total of 319 GPs and 285 nurses' responders. MEASUREMENTS: Data were analyzed under a classification tree analysis by four predictor variables: (i) Health professional (Nurses/GPs); (ii) Exercise prescription collaboration with all health professionals: physicians, nurses, psychologists, physical therapists, sports medicine physicians, sports scientists, nutritionists, and teachers (Yes/No); (iii) PA promotion collaboration with Sports Scientists (Yes/No); and (iv) The stage of change of PHC staff to PA promotion (0-4 Likert scale). RESULTS: Regarding the predictor variable (i), responders without PA guidelines knowledge and positive attitude to collaborate with nurses in PA promotion are more GPs of female sex (nurses n=33 and GPs n=175) than male sex (nurses n=3 and GPs n=59) (p<.001). For the predictor variable (ii) only 9.30% of PHC staff with a positive attitude to collaborate with all health professionals in PA promotion and exercise prescription. For the predictor variable (iii) was shown low collaboration with sports physicians and sports scientists under a multidisciplinary PAP approach (26.50% responders). Finally, in the predictor variable (iv) Staff maintaining PAP for at least 6 months, self-considered active, and with PAP knowledge want to collaborate with Sports scientists (Yes=233; No=133). CONCLUSIONS: Nurses and GPs are conscious of health-related PA benefits despite the lack of PAP knowledge and lack of willingness to collaborate with other health personnel, exercise professionals, and community resources available.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Pulsed magnetic field increases the effect of ultraviolet C radiation and thermal shock in aged yeasts.

The study of the effects of the magnetic field (MF) on living matter continues to be a dilemma. Until now, the interaction mechanisms of MF with living matter that explain the observed phenomena are unknown. Despite the existing literature and the multiple effects described to date, there are few published articles that study the combined effect of MF with other physical agents during the cellular aging process. In this sense, the aim of this work is to study whether low frequency and intensity pulsed and sinusoidal MF exposure produce alterations in the cell killing effect of ultraviolet C (UVC) radiation and thermal shock during the chronological aging of S. cerevisiae. Yeast cells were exposed to 2.45 mT (50 Hz) sinusoidal MF and 1.5 mT (25 Hz) pulsed MF, during 40 days of aging, in combination with UVC radiation (50 J/m2) and/or thermal shock (52°C). Cell survival was evaluated by clonogenic assay. The exposure of yeast to pulsed MF produces an acceleration of aging, which is not observed in cells exposed to sinusoidal MF. The pulsed MF modifies the cellular response to damaging agents only in aged S. cerevisiae cells. In this sense, the pulsed MF applied increases the damage induced by UVC radiation and by thermal shock. In contrast, the sinusoidal MF used has no effect.

Subcuticular suture and incisional surgical-site infection in elective hepatobiliary and pancreatic surgery: an open-label, pragmatic randomized clinical trial (CLOSKIN trial).

BACKGROUND: Subcuticular suture has proven to reduce superficial incisional SSI (si-SSI) in clean surgery. However, question remains regarding clean-contaminated procedures. The aim of this study is to assess if subcuticular suture is superior to staples in reducing si-SSI incidence in elective HBP surgery. METHODS: Single-centre, open-label, parallel, pragmatic randomized clinical trial conducted at a referral tertiary Hospital between January 2020 and April 2022. Patients eligible for elective HBP surgery were randomly assigned (1:1) to subcuticular suture or surgical staples wound closure using a minimisation method based on previously confirmed risk factors. The primary endpoint was the incidence of si-SSI. Considered secondary endpoints were major postoperative morbidity in both groups, additional wound complications, median hospital length of stay and need for re-hospitalisation. RESULTS: Of the 379 patients, 346 patients were randomly assigned to receive skin closure with staples (n = 173) or subcuticular suture (n = 173). After further exclusion of 11 participants, 167 and 168 patients, respectively in the control and the experimental group received their allocated intervention. For the primary endpoint, no significant differences in si-SSI rate were found: 17 (9.82%) staples group vs. 8 (4.62%) in subcuticular suture group (p = 0.062). Subset analysis confirmed absence of significant differences. As for secondary endpoints, overall wound complications did not differ significantly between two procedures: 19 (10.98%) vs. 10 (6.35%) (p = 0.127). There were no treatment related adverse events. However, occurrence of si-SSI contributed to major postoperative morbidity in both groups (p < 0.001 and p = 0.018) and to a substantially prolonged postoperative hospitalization (p = 0.015). CONCLUSIONS: Subcuticular suture might offer a relative benefit for skin closure reducing incidence of si-SSI after elective HBP surgery, although this was found not to be clinically relevant. Yet, this should not be interpreted as equivalence among both treatments. Therefore, wound closure strategy should not be based only on these grounds. TRIAL REGISTRATION NUMBER: ISRCTN Registry number ISRCTN37315612 (registration date: 14/01/2020).

CO2 emissions and global value chains indicators:new evidence for 1995-2018.

Globalization and the configuration of production processes around Global Value Chains (GVCs) have become key factors for explaining the recent evolution of environmental and economic indicators. Indeed, previous research found evidence on the significant impact of GVCs indicators (participation and position) on CO2 emissions. Additionally, results obtained in previous literature vary depending on the time period and geographical areas considered. In this context, the main aims of this paper are to analyze the role the GVCs in explaining the evolution of CO2 emissions, and to identify possible structural breaks. This study uses the Multiregional Input-Output framework to calculate a position indicator and two different measures of participation in GVCs (interpreted either as trade openness or international competitiveness). The analysis useS Inter-Country Input-Output tables (ICIO) as main database, which includes 66 countries and 45 industries and covers the period 1995-2018. It is first concluded that upstream positions in GVCs are associated to lower global emissions. Additionally, the effect of participation depends on the measure used: trade openness is linked to lower emissions, while a higher competitiveness in international trade leads to higher emissions. Finally, two structural breaks are identified in 2002 and 2008, revealing that position is significant in the two first subperiods, while participation becomes significant from 2002 onwards. Thus, policies to mitigate CO2 emissions might to be different before and after 2008: currently, reductions in emissions can be achieved by increasing value-added embodied in trade while decreasing the volume of transactions.

Influence of game and quarter results on external peak demands during games in under-18 years, male basketball players.

To quantify and compare the external peak demands (PD) encountered according to game result (win vs. loss), quarter result (win vs. tie vs. loss), and quarter point difference (± difference in score) in under-18 years (U18), male basketball players. Thirteen basketball players had external load variables monitored across 9 games using local positioning system technology, including distance covered, distance covered in different intensity zones, accelerations, decelerations, and PlayerLoad™. PD were calculated across 30-s, 1-min, and 5-min time windows for each variable. Linear mixed models were used to compare PD for each variable according to game result (win vs. loss), quarter result (win vs tie vs loss), and quarter point difference (high vs. low). External PD were comparable between games that were won and lost for all variables and between quarters that were won and lost for most variables (p > 0.05, trivial-small effects). In contrast, players produced higher (p < 0.05, small effects) 1-min high-speed running distance and 5-min PlayerLoadTM in quarters that were won compared to quarters that were lost. Additionally, high quarter point differences (7.51 ± 3.75 points) elicited greater (p < 0.05, small effects) external PD (30-s PlayerLoadTM, 30-s and 5-min decelerations, and 1-min and 5-min high-speed running distance) than low quarter point differences (-2.47 ± 2.67 points). External PD remain consistent (trivial-small effects) regardless of game result, quarter result, and quarter point difference in U18, male basketball players. Accordingly, external PD attained during games may not be a key indicator of team success.

Aminoacyl-tRNA synthetases.

The aminoacyl-tRNA synthetases are an essential and universally distributed family of enzymes that plays a critical role in protein synthesis, pairing tRNAs with their cognate amino acids for decoding mRNAs according to the genetic code. Synthetases help to ensure accurate translation of the genetic code by using both highly accurate cognate substrate recognition and stringent proofreading of noncognate products. While alterations in the quality control mechanisms of synthetases are generally detrimental to cellular viability, recent studies suggest that in some instances such changes facilitate adaption to stress conditions. Beyond their central role in translation, synthetases are also emerging as key players in an increasing number of other cellular processes, with far-reaching consequences in health and disease. The biochemical versatility of the synthetases has also proven pivotal in efforts to expand the genetic code, further emphasizing the wide-ranging roles of the aminoacyl-tRNA synthetase family in synthetic and natural biology.

Phenotype-centric modeling for rational metabolic engineering.

Phenotype-centric modeling enables a paradigm shift in the analysis of mechanistic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive simulation-centric modeling. Here, we explore applications of this new modeling strategy in the field of rational metabolic engineering using the amorphadiene biosynthetic network as a case study. This network has previously been studied using a mechanistic model and the simulation-centric strategy, and thus provides an excellent means to compare and contrast results obtained from these two very different strategies. We show that the phenotype-centric strategy, without values for the parameters, not only identifies beneficial intervention strategies obtained with the simulation-centric strategy, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and to enhance the mechanistic understanding of the amorphadiene biosynthetic network. Further, we identify the landscape of possible intervention strategies for the given model. We believe that phenotype-centric modeling can advance the field of rational metabolic engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of system design in the parameter space.

LptD-antigen system on gold nanoparticles: an innovative strategy in the nanovaccine development.

Nanovaccine development is a growing research field in which the development of new carriers and bioconjugation approaches is a priority. In this sense, this report describes for the first time, the development of a novel conjugate that consists of gold nanoparticles (AuNPs) obtained by a one-step synthesis using an immunogenic peptide of the Lipopolysaccharide-assembly protein LptD fromVibrio parahaemolyticusbacteria as a reducing and capping agent. The resultingLptD@AuNPscompounds were fully characterized and the results showed the high capacity of the peptide to form complexes and reduce gold ions. The reaction yield estimated was higher than 83% and the chemical integrity of the peptide on the NP surface revealed a tyrosine amino acid bonding on the AuNP surface. Furthermore, theLptD@AuNPsystem showed high colloidal stability in a wide pH range (3-11 pH values), where the hydrodynamic diameter and Zeta potential behavior were strongly influenced by the functional groups of the antigenic peptide. The cytotoxicity assays showed that the obtained system is safe for mouse leukocytes, while immunized mice withLptD@AuNPsproduced specific IgG antibodies. These encouraging results revealed the efficacy of some antigenic peptides as reducers and capping agents, in addition, opening the path to determine immunogenicity and immunoprotective efficacy of theLptD@AuNPsystem against the disease induced byVibrio parahaemolyticus.

Characterizing player's playing styles based on player vectors for each playing position in the Chinese Football Super League.

Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.

Individualization of Pinus radiata Canopy from 3D UAV Dense Point Clouds Using Color Vegetation Indices.

The location of trees and the individualization of their canopies are important parameters to estimate diameter, height, and biomass, among other variables. The very high spatial resolution of UAV imagery supports these processes. A dense 3D point cloud is generated from RGB UAV images, which is used to obtain a digital elevation model (DEM). From this DEM, a canopy height model (CHM) is derived for individual tree identification. Although the results are satisfactory, the quality of this detection is reduced if the working area has a high density of vegetation. The objective of this study was to evaluate the use of color vegetation indices (CVI) in canopy individualization processes of Pinus radiata. UAV flights were carried out, and a 3D dense point cloud and an orthomosaic were obtained. Then, a CVI was applied to 3D point cloud to differentiate between vegetation and nonvegetation classes to obtain a DEM and a CHM. Subsequently, an automatic crown identification procedure was applied to the CHM. The results were evaluated by contrasting them with results of manual individual tree identification on the UAV orthomosaic and those obtained by applying a progressive triangulated irregular network to the 3D point cloud. The results obtained indicate that the color information of 3D point clouds is an alternative to support individualizing trees under conditions of high-density vegetation.