The use of large artificial reefs to enhance fish populations at different depths in the Florida Keys

This study showed that large prefabricated units and concrete rubble patch reefs, placed as artificial marine habitats on sand bottom, greatly enhance the abundance, diversity, and biomass of fish in an area. Densities of individuals and biomass were found considerably higher at artificial reefs than at nearby, natural, bank reefs, a result consistent with other studies. Location, depth, and vertical profile are important factors determining fish assemblages at artificial habitats in the Keys. Fishes were both produced at artificial reefs and attracted from the surrounding area. Fish assemblages at the Hawk Channel artificial reefs were considerably different from those on the offshore reef tract, particularly in terms of dominant species. Rescue of the original 1992 work in 2005 was funded by the South Florida Ecosystem Restoration Prediction and Modeling Program

A Bio-Catalytic Approach to Aliphatic Ketones

Depleting oil reserves and growing environmental concerns have necessitated the development of sustainable processes to fuels and chemicals. Here we have developed a general metabolic platform in E. coli to biosynthesize carboxylic acids. By engineering selectivity of 2-ketoacid decarboxylases and screening for promiscuous aldehyde dehydrogenases, synthetic pathways were constructed to produce both C5 and C6 acids. In particular, the production of isovaleric acid reached 32 g/L (0.22 g/g glucose yield), which is 58% of the theoretical yield. Furthermore, we have developed solid base catalysts to efficiently ketonize the bio-derived carboxylic acids such as isovaleric acid and isocaproic acid into high volume industrial ketones: methyl isobutyl ketone (MIBK, yield 84%), diisobutyl ketone (DIBK, yield 66%) and methyl isoamyl ketone (MIAK, yield 81%). This hybrid “Bio-Catalytic conversion” approach provides a general strategy to manufacture aliphatic ketones, and represents an alternate route to expanding the repertoire of renewable chemicals

Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles

Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering

Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South America. Since 1990, the attributable burden has increased by 50%, driven largely by population growth and ageing. Globally, the impact of reductions in household air pollution was largely offset by increased ambient PM2·5. Interpretation: Air pollution is a major risk factor for diabetes. We estimated that about a fifth of the global burden of type 2 diabetes is attributable PM2·5 pollution. Air pollution mitigation therefore might have an essential role in reducing the global disease burden resulting from type 2 diabetes. Funding: Bill & Melinda Gates Foundation

Aspects of the encephalitis epidemic caused by arbovirus in the Ribeira Valley, S. Paulo, Brazil, during 1975-1978

In 1975, 1976, and 1977, arbovirus caused an encephalitis epidemic in the Ribeira Valley in the state of S. Paulo. The epidemic would peak when the temperature and pluvial levels were higher. From 1978 on the disease maintained low levels within a presumed endemic zone. The epidemic had swept from east to west and from east to southwest in a wave toward the neighboring coastal region. The mountain chain to the north and northwest acted as a barrier. It was only logical natural that the hypothesis that the etiological agent, arbovirus Rocio may have recently infected the human population be considered. Mosquitos must have transmitted the infection from birds and small mammals in nearby forests. Probable forms of transmission of arboviroses in the home are also discussed in this article as well as the fact that population groups that presented the worst forms of the disease were the very young, and the very old and those living in the worst conditions. The epidemiological perspective of the arboviroses is that it persists in this area because the area presents excellent conditions for the development of the etiological agent - reservoirs and biological vectors, with a continuous supply of susceptible people, migrants or tourists.Foi realizado estudo epidemiológico da encefalite por arbovirus na região do Vale do Ribeira, S. Paulo, Brasil. Uma epidemia da moléstia ocorreu em 1975, 1976 e 1977, com picos nas épocas de maior temperatura e pluviosidade. A partir de 1978 a moléstia manteve-se em níveis baixos numa presumível ende-micidade. A epidemia se deslocou em onda em direção leste-oeste e leste-sudoeste para a região litorânea vizinha. A cadeia montanhosa situada ao norte e noroeste atuou como barreira à propagação da moléstia. Considerou-se a hipótese que o agente etiológico, arbovirus Rocio, deva ter começado a infectar a população humana recentemente, tendo sido veiculado ao homem de reservatórios silvestres, aves e pequenos mamíferos, por culicídeos silvestres. Discutiu-se também prováveis formas de transmissão domiciliar. Verificou-se que os grupos populacionais que apresentaram as formas mais graves foram os de idades extremas e os que apresentavam piores condições de vida. Considerou-se que a perspectiva epidemiológica desta arbovirose é que ela persista na região, uma vez que a mesma apresenta condições ótimas para o desenvolvimento do agente etiológico, dos reservatórios e dos vetores biológicos, além de receber um contínuo afluxo de população suscetível, migrantes ou turistas

Climate Change, Coral Reef Ecosystems, and Management Options for Marine Protected Areas

Marine protected areas (MPAs) provide place-based management of marine ecosystems through various degrees and types of protective actions. Habitats such as coral reefs are especially susceptible to degradation resulting from climate change, as evidenced by mass bleaching events over the past two decades. Marine ecosystems are being altered by direct effects of climate change including ocean warming, ocean acidification, rising sea level, changing circulation patterns, increasing severity of storms, and changing freshwater influxes. As impacts of climate change strengthen they may exacerbate effects of existing stressors and require new or modified management approaches; MPA networks are generally accepted as an improvement over individual MPAs to address multiple threats to the marine environment. While MPA networks are considered a potentially effective management approach for conserving marine biodiversity, they should be established in conjunction with other management strategies, such as fisheries regulations and reductions of nutrients and other forms of land-based pollution. Information about interactions between climate change and more “traditional” stressors is limited. MPA managers are faced with high levels of uncertainty about likely outcomes of management actions because climate change impacts have strong interactions with existing stressors, such as land-based sources of pollution, overfishing and destructive fishing practices, invasive species, and diseases. Management options include ameliorating existing stressors, protecting potentially resilient areas, developing networks of MPAs, and integrating climate change into MPA planning, management, and evaluation

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe