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Lifespan is influenced by a large number of conserved proteins and gene-regulatory pathways. Here, we introduce a strategy for systematically finding such longevity factors in Saccharomyces cerevisiae and scoring the genetic interactions (epistasis) among these factors. Specifically, we developed an automated competition-based assay for chronological lifespan, defined as stationary-phase survival of yeast populations, and used it to phenotype over 5,600 single- or double-gene knockouts at unprecedented quantitative resolution. We found that 14% of the viable yeast mutant strains were affected in their stationary-phase survival; the extent of true-positive chronological lifespan factors was estimated by accounting for the effects of culture aeration and adaptive regrowth. We show that lifespan extension by dietary restriction depends on the Swr1 histone-exchange complex and that a functional link between autophagy and the lipid-homeostasis factor Arv1 has an impact on cellular lifespan. Importantly, we describe the first genetic interaction network based on aging phenotypes, which successfully recapitulated the core-autophagy machinery and confirmed a role of the human tumor suppressor PTEN homologue in yeast lifespan and phosphatidylinositol phosphate metabolism. Our quantitative analysis of longevity factors and their genetic interactions provides insights into the gene-network interactions of aging cells.

The All4Children project addresses the urgent need to transition from institutionalization to family-based care for out-of-home children in Portugal. Despite evidence highlighting the detrimental effects of institutionalization, only a small percentage of children (less than 4%) are currently placed in family foster care in the country. In response to European directives for deinstitutionalization, Portuguese legislation now prioritizes non-kinship family foster care as the preferred alternative for young children in need of care. To facilitate this transition, the Integrated Model of Family Foster Care (MIAF) was developed, offering a comprehensive framework covering the entire spectrum of family foster care. This research aims to investigate the initial implementation stage of the MIAF to promote high-quality family foster care in Portugal. The study will conduct a mixed-method and longitudinal research project in family foster care agencies across different regions of Portugal, focusing on evaluating the implementation and outcomes of the MIAF model using a multi-informant and multi-method approach. The participants will include caseworkers, children aged 0-9 years entering foster care, and their respective foster families enrolled in the MIAF program. Process evaluation will assess fidelity, feasibility, appropriateness, and acceptability of MIAF modules, while outcome evaluation will examine child safety, stability, well-being, as well as foster family well-being and quality of relational care. The insights gained from this research initiative will serve as a foundation for the ongoing enhancement of MIAF. Consequently, this project has the capacity to advance evidence-based child welfare practices by refining processes and strategies to better serve vulnerable children and youth. Facilitated by a multidisciplinary team, this project will contribute to advancing research in the field, enhancing practice, and informing policy during a pivotal stage of deinstitutionalization in Portugal.

Mapping habitats is essential to assist strategic decisions regarding the use and protection of coral reefs. Coupled with machine learning (ML) algorithms, remote sensing has allowed detailed mapping of reefs at meaningful scales. Here we integrated WorldView-3 and Landsat-8 imagery and ML techniques to produce a map of suitable habitats for the occurrence of a model species, the hydrocoral Millepora alcicornis, in coral reefs located inside marine protected areas in Northeast Brazil. Conservation and management efforts in the region were also analyzed, integrating human use layers to the ecological seascape. Three ML techniques were applied: two to derive base layers, namely geographically weighted regressions for bathymetry and support vector machine classifier (SVM) for habitat mapping, and one to build the species distribution model (MaxEnt) for Millepora alcicornis, a conspicuous and important reef-building species in the area. Additionally, human use was mapped based on the presence of tourists and fishers. SVM yielded 15 benthic classes (e.g., seagrass, sand, coral), with an overall accuracy of 79%. Bathymetry and its derivative layers depicted the topographical complexity of the area. The Millepora alcicornis distribution model identified distance from the shore and depth as topographical factors limiting the settling and growth of coral colonies. The most important variables were ecological, showing the importance of maintaining high biodiversity in the ecosystem. The comparison of the habitat suitability model with species absence and human use maps indicated the impact of direct human activities as potential inhibitors of coral development. Results reinforce the importance of the establishment of no-take zones and other protective measures for maintaining local biodiversity.

Grapevine Trunk Diseases are a set of fungal diseases that mainly affect wood tissues of grapevine, reducing plant fitness and yield. These diseases limit grape production, so producers need to employ several management strategies to avoid great losses. Nevertheless, due to complex etiology and irregular symptom onset that can be influenced by environmental conditions, producers find it difficult to predict outbreaks and thus to implement management practices in a timely manner. Additionally, fungal infections can remain quiescent for several years, colonizing the plant tissues without symptoms, with symptoms emergence usually occurring several years later. Therefore, the identification of infected grapevines is essential for the definition of effective management strategies. The objective of this work was to design a set of assays based on High Resolution Melting that can detect and identify the most common fungal species responsible for four Grapevine Trunk Diseases. The beta-tubulin gene (Tub2) was selected to design an HRM assay, considering a 130 bp amplicon. The assays distinguish ten of the most common fungal species responsible for Botryosphaeria dieback, Esca, Phomopsis dieback and Eutypa dieback, based on difference curve profiles against a known reference, Neofusicoccum parvum. When applied to mixtures of two fungal species, this strategy allowed both the identification and relative quantification of the pathogens. The application of such an analysis to grapevines will allow the assessment of the health status of the vines so informed management practices can be applied. This will also contribute to the reduction of the potentially pathogenic fungal load by the removal of part or the whole infected plant, ultimately reducing viticulture production costs.

Grapevine health is influenced by microbiome composition, which is affected by region and several plant features such as cultivar, age and rootstock. Grapevine Trunk Diseases (GTDs) are caused by several wood-colonizing fungi, leading to imbalances in microbiome composition. Here, we performed next-generation sequencing of fungal and bacterial microbiomes present in trunk samples of ninety-seven symptomatic grapevines, from two cultivars (cv. Touriga Nacional and cv. Aragonez), collected in eight Portuguese wine-producing regions. The influence of wine-producing regions, grapevine genotype, rootstock, and age was analyzed. Results indicate that microbiome composition is largely influenced by region and cultivar, with more pronounced alterations in cv. Touriga Nacional. Furthermore, relationships between microbes were characterized, revealing that several genera could engage in competitive interactions with the pathogens. We postulate that environmental conditions associated to the wine-producing regions modulate trunk endosphere and microbiome composition. Plant cultivar, age and rootstock also influence the trunk microbiome assembly, leading to distinct taxa composition in the trunk, and also altered microbe relationships.

Epidemiological studies found that hepatitis E virus genotype 3 (HEV-3) infection was associated with chronic hepatitis and cirrhosis in immunocompromised patients. Our study aimed to investigate the relationship between the host immunosuppressive status and the occurrence of HEV-related chronic hepatitis. Here we describe a successful experimental study, using cynomolgus monkeys previously treated with tacrolimus, a potent calcineurin inhibitor immunosuppressant, and infected with a Brazilian HEV-3 strain isolated from naturally infected pigs. HEV infected monkeys were followed up during 160 days post infection (dpi) by clinical signs; virological, biochemical and haematological parameters; and liver histopathology. The tacrolimus blood levels were monitored throughout the experiment. Immunosuppression was confirmed by clinical and laboratorial findings, such as: moderate weight loss, alopecia, and herpes virus opportunistic infection. In this study, chronic HEV infection was characterized by the mild increase of liver enzymes serum levels; persistent RNA viremia and viral faecal shedding; and liver histopathology. Three out of four immunosuppressed monkeys showed recurrent HEV RNA detection in liver samples, evident hepatocellular ballooning degeneration, mild to severe macro and microvesicular steatosis (zone 1), scattered hepatocellular apoptosis, and lobular focal inflammation. At 69 dpi, liver biopsies of all infected monkeys revealed evident ballooning degeneration (zone 3), discrete hepatocellular apoptosis, and at most mild portal and intra-acinar focal inflammation. At 160 dpi, the three chronically HEV infected monkeys showed microscopic features (piecemeal necrosis) corresponding to chronic hepatitis in absence of fibrosis and cirrhosis in liver parenchyma. Within 4-months follow up, the tacrolimus-immunosuppressed cynomolgus monkeys infected with a Brazilian swine HEV-3 strain exhibited more severe hepatic lesions progressing to chronic hepatitis without liver fibrosis, similarly as shown in tacrolimus-immunosuppressed solid organ transplant (SOT) recipients. The cause-effect relationship between HEV infection and tacrolimus treatment was confirmed in this experiment.

Grape cluster compactness is a key trait that influences fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years, with marker effects ranging from 7.6% to 22.1%. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

Changing the perception of defects as imperfections in crystalline frameworks into correlated domains amenable to chemical control and targeted design might offer opportunities for the design of porous materials with superior performance or distinctive behavior in catalysis, separation, storage, or guest recognition. From a chemical standpoint, the establishment of synthetic protocols adapted to control the generation and growth of correlated disorder is crucial to consider defect engineering a practicable route towards adjusting framework function. By using UiO-66 as experimental platform, we systematically explored the framework chemical space of the corresponding defective materials. Periodic disorder arising from controlled generation and growth of missing cluster vacancies can be chemically controlled by the relative concentration of linker and modulator, which has been used to isolate a crystallographically pure “disordered” reo phase. Cs-corrected scanning transmission electron microscopy is used to proof the coexistence of correlated domains of missing linker and cluster vacancies, whose relative sizes are fixed by the linker concentration. The relative distribution of correlated disorder in the porosity and catalytic activity of the material reveals that, contrarily to the common belief, surpassing a certain defect concentration threshold can have a detrimental effect. The ability to control defects offers the opportunity to design porous materials with desired properties. Here the authors present a strategy to control the generation and growth of periodic missing cluster vacancies in UiO-66 frameworks and analyze their impact on the physical properties and catalytic activity.

Background Hepatitis A virus (HAV) and hepatitis E virus (HEV) are both transmitted by the faecal-oral route, and represent common causes of acute hepatitis in developing countries. The endemicity of HAV infection has shifted from high to moderate in Brazil. Human cases of HEV infection seem to be rare, although the virus has been detected in swine livestock and effluents of slaughterhouses. This study was to determine the epidemiology of hepatitis A and E in one of the largest agricultural settlements in the Amazon Basin of Brazil. Methods Serum samples collected from 397 individuals aged between 5 and 90 years during a population-based cross-sectional survey were tested for anti-HAV and anti-HEV antibodies. Associated risk factors and spatial clustering of HAV and HEV seropositivity were also analyzed. Results The overall rate of HAV seropositivity was 82.9% (95% confidence interval (CI), 79.2-86.6%). Multilevel logistic regression analysis identified increasing age (in years; odds ratio (OR), 1.097; 95% CI, 1.050-1.147; P < 0.001) and crowding (OR, 1.603; 95% CI, 1.054-2.440; P = 0.028) as significant risk factors for HAV seropositivity. Anti-HEV IgG was detected in 50/388 settlers (12.9%, 95% CI, 9.5-16.2%). Anti-HEV IgM was detected in 7/43 (16.3%) anti-IgG positive samples, and 4 of them had a confirmed result by immunoblot. Increasing age was the only significant determinant of HEV seropositivity (OR, 1.033; 95% CI, 1.016-1.050; P < 0.001). No significant spatial clustering of HAV and HEV seropositivity was detected in the area. Conclusions Both HAV and HEV are endemic, with differing rates of infection in children and adults in this rural setting of the Brazilian Amazon. Anti-HEV prevalence was considerably higher than those previously reported in Brazil. The detection of HEV- specific IgM antibodies in four asymptomatic individuals is highly suggestive of the circulation of HEV in this rural population.

Reservoir management decisions are often based on simulation models and probabilistic approaches. Thus, the response of the model must be sufficiently accurate to base sound decisions on and fast enough to be practical for methodologies requiring many simulation runs. However, simulation models often forecast production rates different to real production rates for various reasons. Two possible causes of these deviations are (1) upscaling (a technique to reduce the computational time of simulation models by reducing the number of grid blocks) and (2) uncertainties (the values established to attributes are different from real values caused by lack of knowledge of real reservoir). Morosov and Schiozer [(2016) applied a closed-loop technique in a benchmark case where decisions taken using the simulation models are applied to a reference case. The optimized production strategy, using simulations models, increased the expected monetary value of the project by about 29%, but the Net Present Value (NPV), calculated using a reference case, decreased by 2%. The real NPV was outside the expected range and revealed that the set of models did not fully represent the real field, even for high-quality history-matched models. The objective of this study is to identify the causes of these discrepancies. To reach this goal, we investigate and analyze both the impact of the upscaling and the uncertainty on production and economic indicators. We use a set of representative models of benchmark UNISIM-I (Avansi and Schiozer, 2015) to consider the effects of uncertainty and upscaling. Our main concern was the uncertainties in the distribution of petrophysical properties that strongly influence the productivity and injectivity of wells, noted by Morosov and Schiozer (2016) as being the main cause for differences among models. Furthermore, to verify the isolated effects of the possible causes of deviation, we use a single model to show only the effects of upscaling, and another set of models showing only the uncertainty. The results showed that the impact of the uncertainties was higher than the upscaling for the studied case. The upscaling generated an optimistic bias for production and economic indicators, but well-correlated with the reference case. The uncertainties significantly affected the production forecasts for this study. This happened because the response of the wells is highly dependent on the petrophysical properties of the model, which varies widely between the different models representing uncertainties and was not adequately depicted by the representative models.