Evaluation of chloroform/methanol extraction to facilitate the study of membrane proteins of non-model plants

Membrane proteins are of great interest to plant physiologists because of their important function in many physiological processes. However, their study is hampered by their low abundance and poor solubility in aqueous buffers. Proteomics studies of non-model plants are generally restricted to gel-based methods. Unfortunately, all gel-based techniques for membrane proteomics lack resolving power. Therefore, a very stringent enrichment method is needed before protein separation. In this study, protein extraction in a mixture of chloroform and methanol in combination with gel electrophoresis is evaluated as a method to study membrane proteins in non-model plants. Benefits as well as disadvantages of the method are discussed. To demonstrate the pitfalls of working with non-model plants and to give a proof of principle, the method was first applied to whole leaves of the model plant Arabidopsis. Subsequently, a comparison with proteins extracted from leaves of the non-model plant, banana, was made. To estimate the tissue and organelle specificity of the method, it was also applied on banana meristems. Abundant membrane or lipid-associated proteins could be identified in both tissues, with the leaf extract yielding a higher number of membrane proteins

Distinctive mitochondrial genome of Calanoid copepod Calanus sinicus with multiple large non-coding regions and reshuffled gene order: Useful molecular markers for phylogenetic and population studies

<p>Abstract</p> <p>Background</p> <p>Copepods are highly diverse and abundant, resulting in extensive ecological radiation in marine ecosystems. <it>Calanus sinicus </it>dominates continental shelf waters in the northwest Pacific Ocean and plays an important role in the local ecosystem by linking primary production to higher trophic levels. A lack of effective molecular markers has hindered phylogenetic and population genetic studies concerning copepods. As they are genome-level informative, mitochondrial DNA sequences can be used as markers for population genetic studies and phylogenetic studies.</p> <p>Results</p> <p>The mitochondrial genome of <it>C. sinicus </it>is distinct from other arthropods owing to the concurrence of multiple non-coding regions and a reshuffled gene arrangement. Further particularities in the mitogenome of <it>C. sinicus </it>include low A + T-content, symmetrical nucleotide composition between strands, abbreviated stop codons for several PCGs and extended lengths of the genes <it>atp6 </it>and <it>atp8 </it>relative to other copepods. The monophyletic Copepoda should be placed within the Vericrustacea. The close affinity between Cyclopoida and Poecilostomatoida suggests reassigning the latter as subordinate to the former. Monophyly of Maxillopoda is rejected. Within the alignment of 11 <it>C. sinicus </it>mitogenomes, there are 397 variable sites harbouring three 'hotspot' variable sites and three microsatellite loci.</p> <p>Conclusion</p> <p>The occurrence of the <it>circular subgenomic fragment </it>during laboratory assays suggests that special caution should be taken when sequencing mitogenomes using long PCR. Such a phenomenon may provide additional evidence of mitochondrial DNA recombination, which appears to have been a prerequisite for shaping the present mitochondrial profile of <it>C. sinicus </it>during its evolution. The lack of synapomorphic gene arrangements among copepods has cast doubt on the utility of gene order as a useful molecular marker for deep phylogenetic analysis. However, mitochondrial genomic sequences have been valuable markers for resolving phylogenetic issues concerning copepods. The variable site maps of <it>C. sinicus </it>mitogenomes provide a solid foundation for population genetic studies.</p

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Report on consumer acceptability tests of NARITA hybrids in Tanzania and Uganda

Consumer acceptability tests of NARITA hybrids were conducted with a total of 572 randomly selected men and women farmers from 5 sites in different agro-ecological zones in Tanzania and Uganda (Maruku, Mitalula and Moshi in Tanzania; Kawanda and Mbarara in Uganda). Evaluations were done between July and November 2018 under the project ‘Improvement of banana for smallholder farmers in the Great Lakes Region of Africa’. At each site, focus group discussions (FGDs) were first conducted with different age groups: young women, young men (35 years) to ascertain the main products households make using cooking banana cultivars and the preparation method. The most important product was then prepared in each site - steamed matooke in both Uganda sites and boiled fingers in all Tanzania sites. On a given day, about 100 farmers were each provided with coded samples of four NARITA hybrids plus one local check and asked to rate each sample on a 5-point hedonic scale for the following attributes: colour, aroma, texture in hand, taste, mouthfeel and overall acceptability. This report provides results that can help inform the selection of the best NARITAs to take on-farm and subsequent varietal release

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Molecular and cytological characterization of the global Musa germplasm collection provides insights into the treasure of banana diversity

© 2016, The Author(s). Bananas (Musa spp.) are one of the main fruit crops grown worldwide. With the annual production reaching 144 million tons, their production represents an important contribution to the economies of many countries in Asia, Africa, Latin-America and Pacific Islands. Most importantly, bananas are a staple food for millions of people living in the tropics. Unfortunately, sustainable banana production is endangered by various diseases and pests, and the breeding for resistant cultivars relies on a far too small base of genetic variation. Greater diversity needs to be incorporated in breeding, especially of wild species. Such work requires a large and thoroughly characterized germplasm collection, which also is a safe depository of genetic diversity. The largest ex situ Musa germplasm collection is kept at the International Transit Centre (ITC) in Leuven (Belgium) and currently comprises over 1500 accessions. This report summarizes the results of systematic cytological and molecular characterization of the Musa ITC collection. By December 2015, 630 accessions have been genotyped. The SSR markers confirmed the previous morphological based classification for 84% of ITC accessions analyzed. The remaining 16% of the genotyped entries may need field verification by taxonomist to decide if the unexpected classification by SSR genotyping was correct. The ploidy level estimation complements the molecular data. The genotyping continues for the entire ITC collection, including newly introduced accessions, to assure that the genotype of each accession is known in the largest global Musa gene bank. Open Access ispartof: Biodiversity and Conservation vol:26 issue:4 pages:801-824 status: publishe

Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research and practice

As far back as the industrial revolution, great leaps in technical innovation succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision making engendering new opportunities for continued innovation. The impact of AI is significant, with industries ranging from: finance, retail, healthcare, manufacturing, supply chain and logistics all set to be disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: technological, business and management, science and technology, government and public sector. The research offers significant and timely insight to AI technology and its impact on the future of industry and society in general