Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple celestial bodies is here framed as a multi-objective bilevel optimization problem. A comparative study is performed to assess the performance of different Beam Search algorithms at tackling the combinatorial problem of finding the ideal sequence of bodies. Special focus is placed on the development of a new hybridization between Beam Search and the Population-based Ant Colony Optimization algorithm. An experimental evaluation shows all algorithms achieving exceptional performance on a hard benchmark problem. It is found that a properly tuned deterministic Beam Search always outperforms the remaining variants. Beam P-ACO, however, demonstrates lower parameter sensitivity, while offering superior worst-case performance. Being an anytime algorithm, it is then found to be the preferable choice for certain practical applications.Comment: Code available at https://github.com/lfsimoes/beam_paco__gtoc

Intake of heterocyclic aromatic amines and the risk of prostate cancer in the EPIC-Heidelberg cohort

BACKGROUND: Heterocyclic amines (HCA) are positively associated with prostate cancer risk in animal models. Because of mostly inconsistent results of epidemiological studies, we examined the association between intake of HCA and prostate cancer risk. METHODS: In the EPIC-Heidelberg cohort, detailed information on diet, anthropometry, and lifestyle was assessed between 1994 and 1998. Dietary HCA intake was estimated using information on meat consumption, cooking methods, and preferred degree of browning. During 104,195 person-years of follow-up, 337 incident cases of prostate cancer (123 advanced cases) were identified among 9,578 men with valid dietary information. Multivariate Cox proportional hazards regression was used to examine the association between intake of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MeIQx), and 2-amino-3,4,8-dimethylimidazo[4,5-f]quinoxaline (DiMeIQx) and prostate cancer. RESULTS: Men in the highest quartiles of PhIP, MeIQx, and DiMeIQx intake, respectively, had no increased risk of prostate cancer compared with men in the lowest quartiles (HR = 0.89, 95% CI 0.66-1.22 [PhIP]; 1.06, 0.77-1.45 [MeIQx]; 0.98, 0.72-1.34 [DiMeIQx]). There were no associations between HCA intake and advanced prostate cancer or between high consumption of strongly browned meat and prostate cancer. DISCUSSION: Our data do not support the hypothesis that HCA intake as consumed in a regular diet is a risk factor for prostate cancer

Predictors of Poor Perinatal Outcome following Maternal Perception of Reduced Fetal Movements: A Prospective Cohort Study

Background Maternal perception of reduced fetal movement (RFM) is associated with increased risk of stillbirth and fetal growth restriction (FGR). RFM is thought to represent fetal compensation to conserve energy due to insufficient oxygen and nutrient transfer resulting from placental insufficiency. Objective To identify predictors of poor perinatal outcome after maternal perception of reduced fetal movements (RFM). Design Prospective cohort study. Methods 305 women presenting with RFM after 28 weeks of gestation were recruited. Demographic factors and clinical history were recorded and ultrasound performed to assess fetal biometry, liquor volume and umbilical artery Doppler. A maternal serum sample was obtained for measurement of placentally-derived or modified proteins including: alpha fetoprotein (AFP), human chorionic gonadotrophin (hCG), human placental lactogen (hPL), ischaemia-modified albumin (IMA), pregnancy associated plasma protein A (PAPP-A) and progesterone. Factors related to poor perinatal outcome were determined by logistic regression. Results 22.1% of pregnancies ended in a poor perinatal outcome after RFM. The most common complication was small-for-gestational age infants. Pregnancy outcome after maternal perception of RFM was related to amount of fetal activity while being monitored, abnormal fetal heart rate trace, diastolic blood pressure, estimated fetal weight, liquor volume, serum hCG and hPL. Following multiple logistic regression abnormal fetal heart rate trace (Odds ratio 7.08, 95% Confidence Interval 1.31–38.18), (OR) diastolic blood pressure (OR 1.04 (95% CI 1.01–1.09), estimated fetal weight centile (OR 0.95, 95% CI 0.94–0.97) and log maternal serum hPL (OR 0.13, 95% CI 0.02–0.99) were independently related to pregnancy outcome. hPL was related to placental mass. Conclusion Poor perinatal outcome after maternal perception of RFM is closely related to factors which are connected to placental dysfunction. Novel tests of placental function and associated fetal response may provide improved means to detect fetuses at greatest risk of poor perinatal outcome after RFM

Maternal Perception of Reduced Fetal Movements Is Associated with Altered Placental Structure and Function

Maternal perception of reduced fetal movement (RFM) is associated with increased risk of stillbirth and fetal growth restriction (FGR). DFM is thought to represent fetal compensation to conserve energy due to insufficient oxygen and nutrient transfer resulting from placental insufficiency. To date there have been no studies of placental structure in cases of DFM.To determine whether maternal perception of reduced fetal movements (RFM) is associated with abnormalities in placental structure and function.Placentas were collected from women with RFM after 28 weeks gestation if delivery occurred within 1 week. Women with normal movements served as a control group. Placentas were weighed and photographs taken. Microscopic structure was evaluated by immunohistochemical staining and image analysis. System A amino acid transporter activity was measured as a marker of placental function. Placentas from all pregnancies with RFM (irrespective of outcome) had greater area with signs of infarction (3.5% vs. 0.6%; p<0.01), a higher density of syncytial knots (p<0.001) and greater proliferation index (p<0.01). Villous vascularity (p<0.001), trophoblast area (p<0.01) and system A activity (p<0.01) were decreased in placentas from RFM compared to controls irrespective of outcome of pregnancy.This study provides evidence of abnormal placental morphology and function in women with RFM and supports the proposition of a causal association between placental insufficiency and RFM. This suggests that women presenting with RFM require further investigation to identify those with placental insufficiency

RTL551 Treatment of EAE Reduces CD226 and T-bet+ CD4 T Cells in Periphery and Prevents Infiltration of T-bet+ IL-17, IFN-γ Producing T Cells into CNS

Recombinant T cell receptor ligands (RTLs) that target encephalitogenic T-cells can reverse clinical and histological signs of EAE, and are currently in clinical trials for treatment of multiple sclerosis. To evaluate possible regulatory mechanisms, we tested effects of RTL therapy on expression of pathogenic and effector T-cell maturation markers, CD226, T-bet and CD44, by CD4+ Th1 cells early after treatment of MOG-35-55 peptide-induced EAE in C57BL/6 mice. We showed that 1–5 daily injections of RTL551 (two-domain I-Ab covalently linked to MOG-35-55 peptide), but not the control RTL550 (“empty” two-domain I-Ab without a bound peptide) or Vehicle, reduced clinical signs of EAE, prevented trafficking of cells outside the spleen, significantly reduced the frequency of CD226 and T-bet expressing CD4+ T-cells in blood and inhibited expansion of CD44 expressing CD4+ T-cells in blood and spleen. Concomitantly, RTL551 selectively reduced CNS inflammatory lesions, absolute numbers of CNS infiltrating T-bet expressing CD4+ T-cells and IL-17 and IFN-γ secretion by CNS derived MOG-35-55 reactive cells cultured ex vivo. These novel results demonstrate that a major effect of RTL therapy is to attenuate Th1 specific changes in CD4+ T-cells during EAE and prevent expansion of effector T-cells that mediate clinical signs and CNS inflammation in EAE

Computer Simulations Provide Guidance for Molecular Medicine through Insights on Dynamics and Mechanisms at the Atomic Scale

International audienceComputer simulations provide crucial insights and rationales for the design of molecular approaches in medicine. Several case studies illustrate how molecular model building and molecular dynamics simulations of complex molecular assemblies such as membrane proteins help in that process. Important aspects relate to build relevant molecular models with and without a crystal structure, to model membrane aggregates, then to link (dynamic) models to function, and finally to understand key disease-triggering phenomena such as aggregation. Through selected examples-including key signaling pathways in neurotransmission-the links between a molecular-level understanding of biological mechanisms and original approaches to treat disease conditions will be illuminated. Such treatments may be symptomatic, e.g. by better understanding the function and pharmacology of macromolecular key players, or curative, e.g. through molecular inhibition of disease-inducing molecular processes

Beyond microarrays: Finding key transcription factors controlling signal transduction pathways

BACKGROUND: Massive gene expression changes in different cellular states measured by microarrays, in fact, reflect just an "echo" of real molecular processes in the cells. Transcription factors constitute a class of the regulatory molecules that typically require posttranscriptional modifications or ligand binding in order to exert their function. Therefore, such important functional changes of transcription factors are not directly visible in the microarray experiments. RESULTS: We developed a novel approach to find key transcription factors that may explain concerted expression changes of specific components of the signal transduction network. The approach aims at revealing evidence of positive feedback loops in the signal transduction circuits through activation of pathway-specific transcription factors. We demonstrate that promoters of genes encoding components of many known signal transduction pathways are enriched by binding sites of those transcription factors that are endpoints of the considered pathways. Application of the approach to the microarray gene expression data on TNF-alpha stimulated primary human endothelial cells helped to reveal novel key transcription factors potentially involved in the regulation of the signal transduction pathways of the cells. CONCLUSION: We developed a novel computational approach for revealing key transcription factors by knowledge-based analysis of gene expression data with the help of databases on gene regulatory networks (TRANSFAC(® )and TRANSPATH(®)). The corresponding software and databases are available at

Prevalence and Determinants of Obesity among Primary School Children in Dar es Salaam, Tanzania.

Childhood obesity has increased dramatically and has become a public health concern worldwide. Childhood obesity is likely to persist through adulthood and may lead to early onset of NCDs. However, there is paucity of data on obesity among primary school children in Tanzania. This study assessed the prevalence and determinants of obesity among primary school children in Dar es Salaam. A cross sectional study was conducted among school age children in randomly selected schools in Dar es Salaam. Anthropometric and blood pressure measurements were taken using standard procedures. Body Mass Index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m2). Child obesity was defined as BMI at or above 95th percentile for age and sex. Socio-demographic characteristics of children were determined using a structured questionnaire. Logistic regression was used to determine association between independent variables with obesity among primary school children in Dar es Salaam. A total of 446 children were included in the analysis. The mean age of the participants was 11.1±2.0 years and 53.1% were girls. The mean BMI, SBP and DBP were 16.6±4.0 kg/m2, 103.9±10.3mmHg and 65.6±8.2mmHg respectively. The overall prevalence of child obesity was 5.2% and was higher among girls (6.3%) compared to boys (3.8%). Obese children had significantly higher mean values for age (p=0.042), systolic and diastolic blood pressures (all p<0.001). Most obese children were from households with fewer children (p=0.019) and residing in urban areas (p=0.002). Controlling for other variables, age above 10 years (AOR=3.3, 95% CI=1.5-7.2), female sex (AOR=2.6, 95% CI=1.4-4.9), urban residence (AOR=2.5, 95% CI=1.2-5.3) and having money to spend at school (AOR=2.6, 95% CI=1.4-4.8) were significantly associated with child obesity. The prevalence of childhood obesity in this population was found to be low. However, children from urban schools and girls were proportionately more obese compared to their counterparts. Primary preventive measures for childhood obesity should start early in childhood and address socioeconomic factors of parents contributing to childhood obesity

OmicsVis: an interactive tool for visually analyzing metabolomics data

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery