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Bio-catalytic micro- and nanomotors self-propel by the enzymatic conversion of substrates into products. Despite the advances in the field, the fundamental aspects underlying enzyme-powered self-propulsion have rarely been studied. In this work, we select four enzymes (urease, acetylcholinesterase, glucose oxidase, and aldolase) to be attached on silica microcapsules and study how their turnover number and conformational dynamics affect the self-propulsion, combining both an experimental and molecular dynamics simulations approach. Urease and acetylcholinesterase, the enzymes with higher catalytic rates, are the only enzymes capable of producing active motion. Molecular dynamics simulations reveal that urease and acetylcholinesterase display the highest degree of flexibility near the active site, which could play a role on the catalytic process. We experimentally assess this hypothesis for urease micromotors through competitive inhibition (acetohydroxamic acid) and increasing enzyme rigidity (β-mercaptoethanol). We conclude that the conformational changes are a precondition of urease catalysis, which is essential to generate self-propulsion. Self-propulsion of biocatalytic micro- and nanomotors is facilitated by enzymes converting substrates into products. Here, the authors show that intrinsic enzymatic properties such as conformational changes are crucial for the self-propulsion of silica microcapsules modified with urease.

Glycosidases are phylogenetically widely distributed enzymes that are crucial for the cleavage of glycosidic bonds. Here, we present the exceptional properties of a putative ancestor of bacterial and eukaryotic family-1 glycosidases. The ancestral protein shares the TIM-barrel fold with its modern descendants but displays large regions with greatly enhanced conformational flexibility. Yet, the barrel core remains comparatively rigid and the ancestral glycosidase activity is stable, with an optimum temperature within the experimental range for thermophilic family-1 glycosidases. None of the ∼5500 reported crystallographic structures of ∼1400 modern glycosidases show a bound porphyrin. Remarkably, the ancestral glycosidase binds heme tightly and stoichiometrically at a well-defined buried site. Heme binding rigidifies this TIM-barrel and allosterically enhances catalysis. Our work demonstrates the capability of ancestral protein reconstructions to reveal valuable but unexpected biomolecular features when sampling distant sequence space. The potential of the ancestral glycosidase as a scaffold for custom catalysis and biosensor engineering is discussed. Family 1 glycosidases (GH1) are present in the three domains of life and share classical TIM-barrel fold. Structural and biochemical analyses of a resurrected ancestral GH1 enzyme reveal heme binding, not known in its modern descendants. Heme rigidifies the TIM-barrel and allosterically enhances catalysis.

Objective The objective of this study is to determine whether a sleep and health improvement programme (SHIP) to promote healthy sleep, eating and physical activity would be feasible, acceptable and have a positive impact on lifestyle behaviours for prostate cancer survivors and caregivers. Methods We recruited 50 participants for a single group 3‐month pre‐post pilot study. The SHIP intervention included (1) website about sleep, nutrition and physical activity (≥1 view/week), (2) two email newsletters with goal‐setting exercises and resources and (3) midpoint health coach call. The primary outcome was changes in validated sleep scales; secondary outcomes included changes in diet, physical activity and concentration from baseline to 3 months. Results Of 50 participants enrolled, median age was 65, 30% were Black and 8% were Hispanic. Thirty‐four patients and 7 family caregivers completed the pilot study (82%). Following the intervention, we observed a statistically significant improvement in the Sleep Hygiene Index (pre: 15, post: 13, p < 0.01), and a trend toward lower Insomnia Severity Index (pre: 12, post: 9, p = 0.07). There were no statistically significant improvements in sleep quality or physical activity, but there were improvements in healthy eating (e.g., increase in cruciferous vegetables and reduction in dairy) and in fatigue‐related problems and concentration. Exit interview feedback was positive. Conclusions A web‐based sleep and healthy lifestyle programme for patients with prostate cancer and their caregivers is feasible and acceptable. A randomized controlled trial is planned to test whether a refined SHIP improves sleep and lifestyle in patients with prostate cancer and caregivers.

Biobanks are valuable tools for developing and applying scientific research and international cooperation through the collection of biological materials and their associated data. Systematic research following the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines was conducted in late 2022 in PubMed and Scopus, and generated 17 articles to be reviewed in depth and critically assessed using the Critical Appraisal Skills Programme Checklist due to the limited available data; 12 relevant health organizations and government websites outside of peer-reviewed journals were also included. Our research identified 44 biobanks in Latin America. In general, there is a lack of regulation and legislation guaranteeing the stored materials’ quality and institutional collaboration. We believe a consensus needs to be reached regarding the terminology and definitions used for biobanks. The design for informed consent should also be agreed upon to ensure the privacy of the data shared among institutions. In conclusion, in Latin America, there is a clear need for government support in creating specific procedures for biobanks and providing further support for existing biobanks.

This study measured event-related brain potentials (ERPs) to test competing hypotheses regarding the effects of anger and race on early visual processing (N1, P2, and N2) and error recognition (ERN and Pe) during a sequentially primed weapon identification task. The first hypothesis was that anger would impair weapon identification in a biased manner by increasing attention and vigilance to, and decreasing recognition and inhibition of weapon identification errors following, task-irrelevant Black (compared to White) faces. Our competing hypothesis was that anger would facilitate weapon identification by directing attention toward task-relevant stimuli (i.e., objects) and away from task-irrelevant stimuli (i.e., race), and increasing recognition and inhibition of biased errors. Results partially supported the second hypothesis, in that anger increased early attention to faces but minimized attentional processing of race, and did not affect error recognition. Specifically, angry (vs. neutral) participants showed increased N1 to both Black and White faces, ablated P2 race effects, and topographically restricted N2 race effects. Additionally, ERN amplitude was unaffected by emotion, race, or object type. However, Pe amplitude was affected by object type (but not emotion or race), such that Pe amplitude was larger after the misidentification of harmless objects as weapons. Finally, anger slowed overall task performance, especially the correct identification of harmless objects, but did not impact task accuracy. Task performance speed and accuracy were unaffected by the race of the face prime. Implications are discussed.

Purpose Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited. Design/methodology/approach This systematic review used Web of Science and Scopus as search engines, which are extensive databases in Operations Research and Management Science. Ninety-five articles regarding food bank operations were deeply analyzed to contribute to this literature review. Findings Through a systematic literature review, this paper identifies the challenges faced by food banks from an operations management perspective and positions the scientific contributions proposed to address these challenges. Originality/value This study makes three main contributions to the current literature. First, this study provides new researchers with an overview of the key features of food bank operations. Second, this study identifies and classifies the proposed optimization models to support food bank managers with decision-making. Finally, this study discusses the challenges of food bank operations and proposes promising future research avenues.

Purpose: Pre-positioning is a crucial choice in pre-disaster humanitarian logistics planning that consists of deciding in advance how much aid and where should it be located to enable effective and prompt operations in the case of an emergency. To support managers making such decisions, we propose four mathematical formulations that, considering the uncertainty on the demand to satisfy, seek to optimize aid prepositioning (before the event) and further distribution (after the event) in order to minimize unmet demand (MUD). The purpose of this paper is to evaluate and compare the performance of these formulations on a real case to discuss when and why should each approach be applied.Design/methodology/approach: The two first formulations adopt the cardinality-constrained (CC) approach to handle uncertainty. These formulations differ in their objective functions, the first formulation’s objective seeks to MUD, whilst the second incorporates equity in the way that demand is satisfied. The two remaining formulations are scenario-based (SB) and as in the previous two formulations, seek to MUD with and without equity considerations, respectively.Findings: Applying our formulations to a case study, we compare the differences between the solutions produced by the proposed formulations and the solutions that would have been produced without uncertainty (perfect information) to have a better understanding of their performance and their behavior. A discussion of the strengths and weaknesses of each model is provided to help managers choose the model that best suits their needs.Originality/value: The formulations are applied to a case study where a food bank is faced with the arrival of a hurricane in Mexico. As far as our knowledge, it is the first work in literature to deal with humanitarian logistics under a cardinality-constrained approach.

(1) Background: Chayote [Sechium edule Jacq. (Sw.)] is a non-traditional export product; recently, demand has increased due to its nutritional and functional properties. There is a wide diversity of varietal groups (VGs) within this species. Despite this, only virens levis and nigrum spinosum varieties are commercialized on a large scale, while the rest are underutilized and poorly studied, so the genetic pool of this species is at risk. (2) Methods: The following variables were evaluated in the fruits of 10 chayote groups of varieties: shape, size, weight, stomatal frequency (SF), stoma size, stomatal index (SI), color index (CO*), pigments, titratable acidity (TA), total soluble solids (TSS), total sugars and moisture content. In addition, the postharvest behavior of the ten VGs stored at room temperature and the effect of 1-MCP on fruit quality during cold storage were evaluated. (3) Results: The groups a. minor and n. minor showed rapid weight loss, the albus varieties showed high epidermis oxidation, while v. levis, n. maximum, n. spinosum and n. xalapensis were susceptible to viviparity, blisters and fungal incidence. 1-MCP prevented chilling injury (CI) and weight loss. (4) Conclusions: The diversity of postharvest characteristics allows the use of VGs for different uses such as a fresh fruit, agroindustrial transformation or mixing with other vegetables.

In this thesis we study sequential decision making through the lens of Online Learn- ing. Online Learning is a very powerful and general framework for multi-period decision making. Due to its simple farmulation and effectiveness it has become a tool of daily use in multibillion companies. Moreover, duc to its beautiful theory and its tight connections with other fields, Online Learning has caught the attention of academics all over the world and driven first-class research.In the first chapter of this thesis. joint work with Huan Xu, we study a problem called: Risk-Averse Convex Bandit. Risk-aversion mukes reference to the fact that humans prefer consistent sequences of good rewards instead of highly variable sequences with slightly better rewards. The Risk-Averse Convex Bandit addresses the fact that, while hisman deci- sion makers are risk-averse. most algorithms for Online Learning are not. In this thesis we provide the first efficient algorithms with strong theoretical guarantees for the Risk-Averse Convex Bandit problem.In the second chapter. joint work with Rachel Cummings, we study the problem of pre- serving privacy in the setting of online submodular minimization. Submodular functions have multiple applications in machine leaming and economics. which usually involve sen- sitive data from individuals. Using tools from Online Convex Optimization, we provide the first «-differentially private algorithms for this problem which are almost as good as the non-private versions for this problem.In the third chapter, joint work with Jacob Abernethy, He Wang. and Huan Xu. we study a dynamic version of two player zero-sum games. Zeto-sum games are ubiquitous in economics, and central to understanding Linear Programming Duality, Convex and Robust Optimization. and Statistics. For many decades it was thought that one could solve this kind of games using sublinear regret algorithms for Online Convex Optimization. We show that while the previous is tue when the game docs not change with time, a naive application of these algorithms can be fatal if the game changes and the players are trying to compete with the Nash Equilibrium of the sum of the games in hindsight.In the fourth chapter. joint work with He Wang and Huan Xu. we revisit the decade old problem of Markov Decision Processes (MDPs) with Adversurial Rewards. MDPs provide a genera) mathematical framework for sequemial decision making under uncertainty when there is a notion of ‘state’, moreover they are the backbone of all Reinforcemem Leaming. We provide an elegant algorithm for this problem using tools fram Online Convex Opti- mization. The algorithm's performance is comparable with cusrent state of the art. We also consider the problem under the large state-space reginw:. and provide the first algorithm with strong theoretical guarantees.