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Employability has been a priority for higher education institutions, and some skills, such as auditing skills, are necessary for students to enter into work life. In this regard, auditing skills can be applied in the food industry in different areas such as production, food safety, quality assurance, and environmental responsibility. Developing these skills could take years of practice and the completion of several courses, certifications, and diplomas, increasing the existing gap regarding auditing skills. In this work, 25 students from a Design of Process and Food Safety Management Systems class implemented a Food Safety and Food Quality checklist based on the Mexican regulation NOM-251-SSA1-2009 Hygiene Practices for Food, Beverage, and Supplement Processing, to analyze a real-life challenge in a beverage company that participated as a Training Partner. Two soft skills were evaluated: decision-making and problem-solving. The problem-solving effectiveness of these instruments was assessed by collecting the feedback of an experienced auditor and a representative from the audited company. The average result obtained for students after auditing the beverage company was 67.04, which was close to the assigned grade from the senior auditor (72.94), which indicates a good estimation from the instruments generated by the students. This exercise demonstrated the effectiveness of the students in performing accordingly and delivering the expected outcomes in front of a real industrial challenge. The combination of continuous education with a work-based challenge functioned as a laboratory for the students, where they can obtain technical skills in combination with fundamental, personal, and cooperative skills, which are necessary for facing industry challenges in an environment of globalization.

Background: Agroecosystems (AE) have different technological, productive, economic and socio-cultural characteristics; therefore, it is important to characterize them to offer information for the design of focused agricultural policies, and that promote the development of the region. Objective: Characterize the AE with native maize in Tezonapa, Veracruz, based on socio-cultural, technical-productive, economic and environmental factors. Methodology: The study was carried out using the survey technique by the non-probabilistic snowball method. Ninety-seven native maize producers were surveyed in 29 Tezonapa communities in 2020. The questionnaire was semi-structured with 53 questions in five sections, including producer data, as well as socio-cultural, technical-productive, environmental and economic. The data obtained from the questionnaires were captured in the Microsoft Office Excel® 2019 computer program, subsequently, a principal component analysis (PCA), descriptive statistics and a dendrogram were executed. For the typology of maize production systems, the 53 original variables were used, generating a dendrogram. Results: Two types of AE were found: Traditional (T) and Traditional Subsistence (TS) with differentiated technical-productive, socio-cultural, economic and environmental characteristics. According to the PCA, the most significant variables in the description of the AEs were two sociodemographic, five technical-productive, and three economic. Implications: The producers of Tezonapa must organize themselves for the management of innovations that impact the yield of native maize crop, but taking into account their knowledge about sustainable agricultural practices. Conclusions: The AEs studied, although they present technical-productive and economic limitations, also have benefits when providing food, generating and exchanging empirical knowledge, which has allowed the conservation of native maize, and said valuable knowledge must be rescued and promoted.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closteriums metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closteriums metabolic flexibility and its potential role in global carbon cycling.

El objetivo del informe consiste en explicar los diferentes procedimientos y métodos contemplados en la realización del proyecto final de la asignatura “Ciencia de los Materiales”. Este proyecto consistió en la utilización del tanino extraído de la savia de la bananera como materia prima para el curtido de pieles marinas. Para dar inicio al proyecto, el grupo de estudiantes utilizó las habilidades investigativas e iniciativa para la obtención de información documentada en diferentes bases de datos on-line. Luego, el grupo realizó los diferentes procesos para la producción de taninos de la savia del pseudotallo y los diferentes procedimientos para el tratamiento que deben recibir las pieles de pescado. En el presente proyecto se trabajó con pieles de pargo rojo (Pagrus pagrus) las cuales, una vez curtidas, sirvieron para la confección de pequeñas muestras artesanales. Con lo anterior, se demostró la viabilidad técnica de implementar un sub-producto de la industria pesquera y obtener un mayor aprovechamiento.

UNLABELLED: Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

BackgroundEpigenetic changes link medical, social, and environmental factors with cardiovascular and kidney disease and, more recently, with cancer. The mechanistic link between metabolic health and epigenetic changes is only starting to be investigated. In our in vitro and in vivo studies, we performed a broad analysis of the link between hyperinsulinemia and chromatin acetylation; our top "hit" was chromatin opening at H3K9ac.MethodsBuilding on our published preclinical studies, here, we performed a detailed analysis of the link between insulin resistance, chromatin acetylation, and inflammation using an initial test set of 28 women and validation sets of 245, 22, and 53 women.ResultsChIP-seq identified chromatin acetylation and opening at the genes coding for TNFα and IL6 in insulin-resistant women. Pathway analysis identified inflammatory response genes, NFκB/TNFα-signaling, reactome cytokine signaling, innate immunity, and senescence. Consistent with this finding, flow cytometry identified increased senescent circulating peripheral T-cells. DNA methylation analysis identified evidence of accelerated aging in insulin-resistant vs. metabolically healthy women.ConclusionsThis study shows that insulin-resistant women have increased chromatin acetylation/opening, inflammation, and, perhaps, accelerated aging. Given the role that inflammation plays in cancer initiation and progression, these studies provide a potential mechanistic link between insulin resistance and cancer.

Polycystic ovarian syndrome is the most common endocrine disorder that affects women. The focus on inflammatory PCOS will be the basis of understanding how hormonal imbalances are due to inflammatory diets, stress, and environmental factors that impact symptom severity. The objective of this paper was to provide a systematic review that would determine if symptom severity was associated with psychological stress on polycystic ovarian syndrome (PCOS). It is important to note that PCOS does not have a cure but symptom severity may only be treated, this is due to the cause being unknown and PCOS being used as an umbrella term for issues related to the ovaries and reproductive cycle. As a result, the most commonly suggested findings were to improve diet, improve exercise habits, and seek psychological help if feeling distressed by the symptoms of PCOS.

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).

We previously showed that femur fracture in mice caused a reduction in bone volume at distant skeletal sites within 2 weeks post-fracture. Osteocytes also have the ability to remodel their surrounding bone matrix through perilacunar/canalicular remodeling (PLR). If PLR is altered systemically following fracture, this could affect bone mechanical properties and increase fracture risk at all skeletal sites. In this study, we investigated whether lacunar-canalicular microstructure and the rate of PLR are altered in the contralateral limb following femoral fracture in mice. We hypothesized that femoral fracture would accelerate PLR by 2 weeks postfracture, followed by partial recovery by 4 weeks. We used histological evaluation and high-resolution microcomputed tomography to quantify the morphology of the lacunar-canalicular network at the contralateral tibia, and we used quantitative real-time polymerase chain reaction (RT-PCR) and RNA-seq to measure the expression of PLR-associated genes in the contralateral femur. We found that at both 2 and 4 weeks postfracture, canalicular width was significantly increased by 18.6% and 16.6%, respectively, in fractured mice relative to unfractured controls. At 3 days and 4 weeks post-fracture, we observed downregulation of PLR-associated genes; RNA-seq analysis at 3 days post-fracture showed a deceleration of bone formation and mineralization in the contralateral limb. These data demonstrate notable canalicular changes following fracture that could affect bone mechanical properties. These findings expand our understanding of systemic effects of fracture and how biological and structural changes at distant skeletal sites may contribute to increased fracture risk following an acute injury.

The complete genomes of 63 unique microviruses were determined from fecal samples collected from three yellow-bellied marmots living in Colorado, USA. These microviruses are diverse, with genome sizes ranging from 4,265 to 7,225 nucleotides, and have varied presence across the three samples.

The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.