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Solar-driven thermochemical hydrogen (STCH) production represents a sustainable approach for converting solar energy into hydrogen (H2) as a clean fuel. This technology serves as a crucial feedstock for synthetic fuel production, aligning with the principles of sustainable energy. The efficiency of the conversion process relies on the meticulous tuning of the properties of active materials, mostly commonly perovskite and fluorite oxides. This Review conducts a comprehensive review encompassing experimental, computational, and thermodynamic and kinetic property studies, primarily assessing the utilization of perovskite oxides in two-step thermochemical reactions and identifying essential attributes for future research endeavors. Furthermore, this Review delves into the application of machine learning (ML) and density functional theory (DFT) for predicting and classifying the thermochemical properties of perovskite materials. Through the integration of experimental investigations, computational modeling, and ML methodologies, this Review aspires to expedite the screening and optimization of perovskite oxides, thus enhancing the efficiency of STCH processes. The overarching objective is to propel the advancement and practical integration of STCH systems, contributing significantly to the realization of a sustainable and carbon-neutral energy landscape.

Cotton (Gossypium hirsutum L.) is the key renewable fibre crop worldwide, yet its yield and fibre quality show high variability due to genotype-specific traits and complex interactions among cultivars, management practices and environmental factors. Modern breeding practices may limit future yield gains due to a narrow founding gene pool. Precision breeding and biotechnological approaches offer potential solutions, contingent on accurate cultivar-specific data. Here we address this need by generating high-quality reference genomes for three modern cotton cultivars (UGA230, UA48 and CSX8308) and updating the TM-1 cotton genetic standard reference. Despite hypothesized genetic uniformity, considerable sequence and structural variation was observed among the four genomes, which overlap with ancient and ongoing genomic introgressions from Pima cotton, gene regulatory mechanisms and phenotypic trait divergence. Differentially expressed genes across fibre development correlate with fibre production, potentially contributing to the distinctive fibre quality traits observed in modern cotton cultivars. These genomes and comparative analyses provide a valuable foundation for future genetic endeavours to enhance global cotton yield and sustainability.

The development of machines explores the development of a manufacturing system. This perspective offers challenges adjacent to the materials, the product that wants to be manufactured, the technical considerations that must be covered during implementation and the manner of delivering the products to the user. This article addresses the design and development of machines, on the one hand the redesign of a multifunctional platform, and on the other hand a 3D food printer. The second is emphasised, since its main objective is to help the food problems that exist today. The design phases, aspects considered for its implementation such as food safety, the choice of the substrate, the functionality of the food, transportation and the pre- and post-processes required. In this manner, the selection and innovation of materials constitutes a primary source of innovation and research in the integral development of a 3D food printer. The main source of value of these both cases lie in customisation. The experiences of a multidisciplinary research team in the development processes are compiled and the design and creation process are shown, in order to contribute to deal with design and development to tackle fundamental problems of society.

The study assessed the possibility of using the revised reinforcement sensitivity theory to predict resource acquisition strategies. It was hypothesized that the Behavior Activation System would differentiate between individuals that favored competitive and those that favored cooperative strategies. A total of 405 undergraduate students from Mexico City answered four questionnaires, namely (1) the Reinforcement Sensitivity Theory Personality Questionnaire, (2) the Sensitivity to Punishment, Sensitivity to Reinforcement Questionnaire, (3) the Assessment of Individual Motives Questionnaire, and (4) the Self-Report Altruism Scale. Results showed that five out of seven competitive motives were predicted by a combination of fight-flight-freeze scores and impulsivity ones. Some cooperative motives may be predicted by specific combinations of behavior inhibition, reward responsiveness, reward intensiveness, and FFFS. Overall, results showed that, only using the BAS to predict individual motives, as previous research has done, may lead to misleading or incomplete conclusions.

The performance of the porous gas bearing depends on the geometric characteristics, material, fluid properties, and the properties of the porous media, which is a restrictor that controls the gas flow. Its application in industrial environments must support higher loads, higher supply pressure, and, consequently, higher pressure in the lubricant fluid film. Because porous media has a relatively low elastic modulus, it is necessary to consider its deformation when designing porous gas bearings. The design of porous gas bearings is a multi-objective problem in engineering because the optimization objectives commonly are to maximize the load capacity or static stiffness coefficient and minimize the airflow; these objectives conflict. This work presents a multi-objective optimization algorithm based on the nature-inspired Flower Pollination Algorithm enhanced with Non-Dominated Sorting Genetic Algorithm II. The algorithm is applied to optimize the design of a porous gas bearing, maximizing the resultant force and the static stiffness coefficient and minimizing the airflow. The results indicate a better performance of the Multi-Objective Flower Pollination Algorithm than the Multi-Objective Cuckoo Search. The results show a relatively short running time of 6 min for iterations and a low number of iterations of 50.

Abstract Background The architecture and dynamics of T cell populations are critical in orchestrating the immune response to SARS-CoV-2. In our study, we used T Cell Receptor sequencing (TCRseq) to investigate TCR repertoires in 173 post-infection COVID-19 patients. Methods The cohort included 98 mild and 75 severe cases with a median age of 53. We amplified and sequenced the TCR β chain Complementary Determining Region 3 (CDR3b) and performed bioinformatic analyses to assess repertoire diversity, clonality, and V/J allelic usage between age, sex and severity groups. CDR3b amino acid sequence inference was performed by clustering structural motifs and filtering validated reactive CDR3b to COVID-19. Results Our results revealed a pronounced decrease in diversity and an increase in clonal expansion in the TCR repertoires of severe COVID-19 patients younger than 55 years old. These results reflect the observed trends in patients older than 55 years old (both mild and severe). In addition, we identified a significant reduction in the usage of key V alleles (TRBV14, TRBV19, TRBV15 and TRBV6-4) associated with disease severity. Notably, severe patients under 55 years old had allelic patterns that resemble those over 55 years old, accompanied by a skewed frequency of COVID-19-related motifs. Conclusions Present results suggest that severe patients younger than 55 may have a compromised TCR repertoire contributing to a worse disease outcome. Graphical Abstract

IntroductionThe 2024 Voice AI Symposium, hosted by the Bridge2AI-Voice Consortium in Tampa, FL, featured two keynote speeches that addressed the intersection of voice AI, healthcare, ethics, and law. Dr. Rupal Patel and Dr. Nicholson Price provided insights into the advancements, applications, and challenges of AI-driven voice tools in healthcare. The symposium aimed to advance cross-disciplinary collaboration and establish frameworks for the ethical use of AI technologies in healthcare.MethodsThe keynote speeches, delivered on May 1st and 2nd, were 30 minutes each, followed by 10-minutes Q&A sessions. The audio was recorded and transcribed using Whisper (v7.13.1). Content summaries were generated with the aid of ChatGPT (v4.0), and the authors reviewed and edited the final transcripts to ensure accuracy and clarity.ResultsDr. Rupal Patel’s keynote, “Reflections and New Frontiers in Voice AI”, explored the potential of voice AI for early detection of health conditions, monitoring disease progression, and promoting non-invasive global health management. She highlighted innovative uses beyond traditional applications, such as examining menopause-related symptoms. Dr. Nicholson Price’s keynote, “Governance for Clinical Voice AI”, addressed the regulatory and ethical challenges posed by AI in healthcare. He stressed the need for context-aware systems and dynamic legal frameworks to address liability and accountability.ConclusionsThe 2024 Voice AI Symposium highlighted the transformative potential of voice AI for early detection, health monitoring, and reducing healthcare disparities. It also underscored the importance of dynamic governance to address the ethical and regulatory challenges of deploying AI in clinical settings.

IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

BackgroundTreatment of multiple sclerosis (MS) with delayed-release dimethyl fumarate (DMF) has shown efficacy and safety in clinical trials. However, the occurrence of infectious complications, particularly in elderly patients, remains a concern.MethodsWe present the case of a 63-year-old woman with late-onset MS treated with DMF, who developed a severe primary cytomegalovirus (CMV) infection. DMF was discontinued, and antiviral treatment was initiated, resulting in complete resolution of symptoms.ResultsWhile DMF is generally considered safe in terms of opportunistic infections, this case highlights the possibility of serious infectious complications in eldery DMF-treated patients, even without documented lymphopenia.ConclusionAdvanced age and the associated immunological changes may contribute to an inadequate immune response to MS treatments, highlighting the importance of a careful treatment choice in elderly patients with MS. Further research and specific data on the safety and efficacy of DMF in this population are required to guide clinical decision-making.

Abstract Scientists and researchers have been searching for drugs targeting the main protease (Mpro) of SARS-CoV-2, which is crucial for virus replication. This study employed a virtual screening based on molecular docking to identify benzoylguanidines from an in-house chemical library that can inhibit Mpro on the active site and three allosteric sites. Molecular docking was performed on the LaSMMed Chemical Library using 88 benzoylguanidine compounds. Based on their RMSD values and conserved pose, three potential inhibitors (BZG1, BZG2, and BZG3) were selected. These results indicate that BZG1 and BZG3 may bind to the active site, while BZG2 may bind to allosteric sites. Molecular dynamics data suggest that BZG2 selectively targets allosteric site 3. In vitro tests were performed to measure the proteolytic activity of rMpro. The tests showed that BZG2 has uncompetitive inhibitory activity, with an IC50 value of 77 µM. These findings suggest that benzoylguanidines possess potential as Mpro inhibitors and pave the way towards combating SARS-Cov-2 effectively.

Abstract Background Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers. Methods We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer. Results We find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Conclusions Our results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

Abstract Structures in nature are often multi‐material, and their structures have a fine balance between segregation and aggregation (mixed, but not scrambled) that provides functionality. Chaotic fabrication, a technology that exploits the ability of chaotic advection to create predictable and reproducible multilayered structures, excels at producing materials where this balance can be achieved and finely tuned. This method is based on the use of chaotic mixing systems, which can produce constructs with highly organized internal micro‐architecture in a simple and cost‐effective way. This manuscript provides a perspective on how chaotic printing can be a great enabler in the manufacture of advanced materials, including living tissues. Chaotic printing may overcome many of the critical hurdles that are currently faced in manufacturing and biofabrication (e.g., creating a wide array of interfaces, reaching high resolutions rapidly and at low cost, and producing densely vascularized tissues). The manuscript introduces the technology, explains how the idea originated, presents a timeline that provides a recapitulation of the milestones achieved so far, describes the main characteristics, advantages, limitations, and challenges of the technology, and concludes with future perspectives on the evolution and use of this versatile method.