| 1. |
- Naghavi, Mohsen, et al.
(author)
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Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013
- 2015
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In: The Lancet. - 1474-547X .- 0140-6736. ; 385:9963, s. 117-171
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Journal article (peer-reviewed)abstract
- Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65.3 years (UI 65.0-65.6) in 1990, to 71.5 years (UI 71.0-71.9) in 2013, while the number of deaths increased from 47.5 million (UI 46.8-48.2) to 54.9 million (UI 53.6-56.3) over the same interval. Global progress masked variation by age and sex: for children, average absolute diff erences between countries decreased but relative diff erences increased. For women aged 25-39 years and older than 75 years and for men aged 20-49 years and 65 years and older, both absolute and relative diff erences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10.7%, from 4.3 million deaths in 1990 to 4.8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.
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| 2. |
- Sanmartín, Ricardo, et al.
(author)
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Affective Latent Profiles and Personality Dimensions in Spanish Children
- 2023
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In: The Affective Profiles Model - 20 Years of Research and Beyond. - Cham : Springer. ; , s. 145-158
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Book chapter (other academic/artistic)abstract
- Background: During the last years, the affective profiles model has been applied to study individual differences among Spanish children in psychological characteristics, such as optimism and pessimism. This research has replicated past studies from adult and adolescent Swedish populations by identifying four profiles using clustering methods on self-reports of children’s experience of positive (PA) and negative affect (NA): self-fulfilling (high PA and low NA), high affective (high PA and high NA), low affective (low PA and NA), and self-destructive (low PA and high NA). Nevertheless, only a few studies have investigated individual differences in personality using the affective profiles model as the framework, none of them among children. Personality is, for instance, a topic with an important impact on child development. Moreover, more advanced person-centred techniques for the clustering of profiles need to be tested to confirm these observed patterns of affectivity or profiles. Aims: Our aims were the identification of affective profiles through latent class analysis (LCA) and to test individual differences in personality between children with these distinct affective profiles. Methods: The Positive Affect and Negative Affect Schedule for Children-Short Form (PANAS-C-SF) was used to assess positive and negative affect and the Big-Five Questionnaire for Children (BFQ-C) to measures the traits of Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness. These instruments were administered to a sample of 533 Spanish children aged between 8 and 11 (M = 9.77; SD = 1.09). LCA was used to identify the affective profiles and multivariate analyses of variance (MANOVA) to study differences in personality traits between children with distinct affective profiles. Results: Three, rather than four, affective latent profiles were identified: self-fulfilling, self-destructive, and neutral affective profile (characterised by neutral scores both in PA and in NA, that is, neither high nor low). The children with a self-fulfilling profile reported the highest scores in Extraversion, Openness, Agreeableness, and Conscientiousness in comparison with the rest of the profiles, whereas the children with a self-destructive profile reported higher scores in Neuroticism in comparison with children with a self-fulfilling profile. Conclusions: We verified a three affective profile distribution through LCA, including a new group characterised by neutral affect. Moreover, adaptive personality traits (high Extraversion, high Openness, high Agreeableness, high Conscientiousness, and low Neuroticism) were found to be related to children with a self-fulfilling profile, whereas maladaptive personality traits (e.g. high Neuroticism) were related to children with a self-destructive profile. We suggest that these findings need to be considered in the creation of person-centred programmes for children that aim to promote well-being, welfare, and positive affect and to reduce stress and negative affect.
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| 3. |
- El-Semman, Ibrahim, 1977, et al.
(author)
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Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies
- 2022
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here, we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation-known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimisation, providing a framework to understand metabolic adaptation in eukaryal cells.
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| 4. |
- Garcia, Manuel, 1973, et al.
(author)
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Application of genome-scale metabolic models in metabolic engineering
- 2013
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In: Industrial Biotechnology. - : Mary Ann Liebert Inc. - 1550-9087 .- 1931-8421. ; 9:4, s. 203-214
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Journal article (peer-reviewed)abstract
- Genome-scale metabolic models (GEMs) are growing in accuracy and complexity with the development of advanced computational tools for using these models in simulation and for integrative data analysis. The reconstruction of metabolic networks for various microorganisms is allowing the analysis of phenotypic changes under various environmental and genetic conditions. Thanks to growing datasets on the specific properties of various microorganisms and to the development of computational tools and mathematical modeling, it is becoming possible to evaluate metabolic network modifications in microorganisms in silico, and thereby use GEMs to an increasing extent in metabolic engineering. Here we review the reconstruction of GEMs and their use in developing novel bioprocesses for producing biofuels, biomaterials, food ingredients, and pharmaceuticals.
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| 5. |
- Garcia, Manuel, 1973, et al.
(author)
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BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data
- 2014
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In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 42:W1, s. W175-W181
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Journal article (peer-reviewed)abstract
- Analysis of large data sets using computational and mathematical tools have become a central part of biological sciences. Large amounts of data are being generated each year from different biological research fields leading to a constant development of software and algorithms aimed to deal with the increasing creation of information. The BioMet Toolbox 2.0 integrates a number of functionalities in a user-friendly environment enabling the user to work with biological data in a web interface. The unique and distinguishing feature of the BioMet Toolbox 2.0 is to provide a web user interface to tools for metabolic pathways and omics analysis developed under different platform-dependent environments enabling easy access to these computational tools.
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| 6. |
- Garcia, Manuel, 1973, et al.
(author)
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Finding directionality and gene-disease predictions in disease associations
- 2015
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In: BMC Systems Biology. - : Springer Science and Business Media LLC. - 1752-0509. ; 9:1
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Journal article (peer-reviewed)abstract
- Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders. Methods: The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases. Results: 880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology. Conclusions: The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications.
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| 7. |
- Laurie, Steven, et al.
(author)
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The RD-Connect Genome-Phenome Analysis Platform : Accelerating diagnosis, research, and gene discovery for rare diseases
- 2022
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In: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 43:6, s. 717-733
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Journal article (peer-reviewed)abstract
- Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.
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| 8. |
- Vicedo, Toni, et al.
(author)
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Temporal activity patterns of bears, wolves and humans in the Cantabrian Mountains, northern Spain
- 2023
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In: European Journal of Wildlife Research. - 1612-4642 .- 1439-0574. ; 69:5
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Journal article (peer-reviewed)abstract
- Human-wildlife coexistence is important for a sustainable relationship between humans and the natural environment. However, human activities often act as a disturbance to wild animals, which may show behavioural shifts indicating human avoidance. For large carnivores, which are prone to conflict with many human interests, coexistence with humans can be particularly challenging. We used long-term camera trap data to evaluate seasonal and diel variations in activity of two large carnivores, the brown bear (Ursus arctos) and the grey wolf (Canis lupus), as well as humans in the Cantabrian Mountains, northern Spain. Brown bears were less active in winter than in summer; the opposite was observed for wolves, whereas there was limited seasonal variation in human activity. On a diel scale, both bears and wolves were mostly crepuscular during summer and had less distinct, but generally more nocturnal activity during winter. Humans were strictly diurnal during both seasons. We suggest that the diel activity of bears and wolves was partially caused by human avoidance, but that seasonal variations in both overall and diel activity were mainly caused by ecological and physiological factors. While we suggest that the observed similarity in diel activity of bears and wolves did not have caused strong competition between these two species, it may have influenced interactions with other predators and prey. Since such interactions are likely to be context dependent, we urge for further studies evaluating how humans influence the behaviour of large carnivores across different spatio-temporal scales.
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