Loss of Developmental Diapause as Prerequisite for Social Evolution in Bees

Diapause is a physiological arrest of development ahead of adverse environmental conditions and is a critical phase of the life cycle of many insects. In bees, diapause has been reported in species from all seven taxonomic families. However, they exhibit a variety of diapause strategies. These different strategies are of particular interest since shifts in the phase of the insect life cycle in which diapause occurs have been hypothesized to promote the evolution of sociality. Here we provide a comprehensive evaluation of this hypothesis with phylogenetic analysis and ancestral state reconstruction (ASR) of the ecological and evolutionary factors associated with diapause phase. We find that social lifestyle, latitude and voltinism are significant predictors of the life stage in which diapause occurs. ASR revealed that the most recent common ancestor of all bees likely exhibited developmental diapause and shifts to adult, reproductive, or no diapause have occurred in the ancestors of lineages in which social behaviour has evolved. These results provide fresh insight regarding the role of diapause as a prerequisite for the evolution of sociality in bees

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Background: Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD–risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable. Methods: Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I 2 statistics. Results: BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I 2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD–risk association (1.51 (1.41, 1.61); I 2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I 2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV–risk association (1.44 (1.34, 1.54); I 2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I 2 = 0%, P = 0.36, respectively). Conclusions: When volumetric MD–breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable

Number of risky lifestyle behaviors and breast cancer risk

Background Lifestyle factors are associated with overall breast cancer risk, but less is known about their associations, alone or jointly, with risk of specific breast cancer subtypes. Methods We conducted a case–control subjects study nested within a cohort of women who participated in the Norwegian Breast Cancer Screening Program during 2006–2014 to examine associations between risky lifestyle factors and breast cancer risk. In all, 4402 breast cancer cases subjects with information on risk factors and hormone receptor status were identified. Conditional logistic regression was used to estimate odds ratios (ORs), with 95% confidence intervals (CIs), in relation to five risky lifestyle factors: body mass index (BMI) of 25 kg/m² or greater, three or more glasses of alcoholic beverages per week, ever smoking, fewer than four hours of physical activity per week, and ever use of menopausal hormone therapy. Analyses were adjusted for education, age at menarche, number of pregnancies, and menopausal status. All statistical tests were two-sided. Results Compared with women with no risky lifestyle behaviors, those with five had 85% (OR = 1.85, 95% CI = 1.42 to 2.42, Ptrend  .18 for all). Conclusions Number of risky lifestyle factors was positively associated with increased risk for luminal A–like and luminal B–like HER2-positive breast cancer

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Location of chlorogenic acid biosynthesis pathway and polyphenol oxidase genes in a new interspecific anchored linkage map of eggplant

© Gramazio et al.; licensee BioMed Central. 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

On the use of simulation as a Big Data semantic validator for supply chain management

Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.This work has been supported by FCT (Fundacao para a Ciencia e Tecnologia) within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

Comparative Proteomics of Inner Membrane Fraction from Carbapenem-Resistant Acinetobacter baumannii with a Reference Strain

Acinetobacter baumannii has been identified by the Infectious Diseases Society of America as one of the six pathogens that cause majority of hospital infections. Increased resistance of A. baumannii even to the latest generation of β-lactams like carbapenem is an immediate threat to mankind. As inner-membrane fraction plays a significant role in survival of A. baumannii, we investigated the inner-membrane fraction proteome of carbapenem-resistant strain of A. baumannii using Differential In-Gel Electrophoresis (DIGE) followed by DeCyder, Progenesis and LC-MS/MS analysis. We identified 19 over-expressed and 4 down-regulated proteins (fold change>2, p<0.05) in resistant strain as compared to reference strain. Some of the upregulated proteins in resistant strain and their association with carbapenem resistance in A. baumannii are: i) β-lactamases, AmpC and OXA-51: cleave and inactivate carbapenem ii) metabolic enzymes, ATP synthase, malate dehydrogenase and 2-oxoglutarate dehydrogenase: help in increased energy production for the survival and iii) elongation factor Tu and ribosomal proteins: help in the overall protein production. Further, entry of carbapenem perhaps is limited by controlled production of OmpW and low levels of surface antigen help to evade host defence mechanism in developing resistance in A. baumannii. Present results support a model for the importance of proteins of inner-membrane fraction and their synergistic effect in the mediation of resistance of A. baumannii to carbapenem