The causal association between smoking initiation, alcohol and coffee consumption, and women’s reproductive health: A two-sample Mendelian randomization analysis

Objective: A number of epidemiological studies have demonstrated that smoking initiation and alcohol and coffee consumption were closely related to women’s reproductive health. However, there was still insufficient evidence supporting their direct causality effect.Methods: We utilized two-sample Mendelian randomization (TSMR) analysis with summary datasets from genome-wide association study (GWAS) to investigate the causal relationship between smoking initiation, alcohol and coffee consumption, and women’s reproductive health-related traits. Exposure genetic instruments were used as variants significantly related to traits. The inverse-variance weighted (IVW) method was used as the main analysis approach, and we also performed MR-PRESSO, MR-Egger, weighted median, and weighted mode to supplement the sensitivity test. Then, the horizontal pleiotropy was detected by using MRE intercept and MR-PRESSO methods, and the heterogeneity was assessed using Cochran’s Q statistics.Results: We found evidence that smoking women showed a significant inverse causal association with the sex hormone-binding globulin (SHBG) levels (corrected β = −0.033, p = 9.05E-06) and age at menopause (corrected β = −0.477, p = 6.60E-09) and a potential positive correlation with the total testosterone (TT) levels (corrected β = 0.033, p = 1.01E-02). In addition, there was suggestive evidence for the alcohol drinking effect on the elevated TT levels (corrected β = 0.117, p = 5.93E-03) and earlier age at menopause (corrected β = −0.502, p = 4.14E-02) among women, while coffee consumption might decrease the female SHBG levels (corrected β = −0.034, p = 1.33E-03).Conclusion: Our findings suggested that smoking in women significantly decreased their SHBG concentration, promoted earlier menopause, and possibly reduced the TT levels. Alcohol drinking had a potential effect on female higher TT levels and earlier menopause, while coffee consumption might lead to lower female SHBG levels

Fibroblast growth factor 23 is associated with proteinuria and smoking in chronic kidney disease: An analysis of the MASTERPLAN cohort

Contains fulltext : 107913.pdf (postprint version ) (Open Access)BACKGROUND: Fibroblast growth factor 23 (FGF23) has emerged as a risk factor for cardiovascular disease and mortality throughout all stages of chronic kidney disease (CKD), independent from established risk factors and markers of mineral homeostasis. The relation of FGF23 with other renal and non-renal cardiovascular risk factors is not well established. METHODS: Using stored samples, plasma FGF23 was determined in 604 patients with moderate to severe kidney disease that participated in the MASTERPLAN study (ISRCTN73187232). The association of FGF23 with demographic and clinical parameters was evaluated using multivariable regression models. RESULTS: Mean age in the study population was 60 years and eGFR was 37 (+/- 14) ml/min/1.73 m(2). Median proteinuria was 0.3 g/24 hours [IQR 0.1-0.9]. FGF23 level was 116 RU/ml [67-203] median and IQR. Using multivariable analysis the natural logarithm of FGF23 was positively associated with history of cardiovascular disease (B = 0.224 RU/ml; p = 0.002), presence of diabetes (B = 0.159 RU/ml; p = 0.035), smoking (B = 0.313 RU/ml; p < 0.001), phosphate level (B = 0.297 per mmol/l; p = 0.0024), lnPTH (B = 0.244 per pmol/l; p < 0.001) and proteinuria (B = 0.064 per gram/24 hrs; p = 0.002) and negatively associated with eGFR (B = -0.022 per ml/min/1.73 m(2); p < 0.001). CONCLUSIONS: Our study demonstrates that in patients with CKD, FGF23 is related to proteinuria and smoking. We confirm the relation between FGF23 and other cardiovascular risk factors

Germline Polymorphisms in MGMT Associated With Temozolomide-Related Myelotoxicity Risk in Patients With Glioblastoma Treated on NRG Oncology/RTOG 0825

Background: We sought to identify clinical and genetic predictors of temozolomide-related myelotoxicity among patients receiving therapy for glioblastoma. Methods: Patients (n = 591) receiving therapy on NRG Oncology/RTOG 0825 were included in the analysis. Cases were patients with severe myelotoxicity (grade 3 and higher leukopenia, neutropenia, and/or thrombocytopenia); controls were patients without such toxicity. A risk-prediction model was built and cross-validated by logistic regression using only clinical variables and extended using polymorphisms associated with myelotoxicity. Results: 23% of patients developed myelotoxicity (n = 134). This toxicity was first reported during the concurrent phase of therapy for 56 patients; 30 stopped treatment due to toxicity. Among those who continued therapy (n = 26), 11 experienced myelotoxicity again. The final multivariable clinical factor model included treatment arm, gender, and anticonvulsant status and had low prediction accuracy (area under the curve [AUC] = 0.672). The final extended risk prediction model including four polymorphisms in MGMT had better prediction (AUC = 0.827). Receiving combination chemotherapy (OR, 1.82; 95% CI, 1.02-3.27) and being female (OR, 4.45; 95% CI, 2.45-8.08) significantly increased myelotoxicity risk. For each additional minor allele in the polymorphisms, the risk increased by 64% (OR, 1.64; 95% CI, 1.43-1.89). Conclusions: Myelotoxicity during concurrent chemoradiation with temozolomide is an uncommon but serious event, often leading to treatment cessation. Successful prediction of toxicity may lead to more cost-effective individualized monitoring of at-risk subjects. The addition of genetic factors greatly enhanced our ability to predict toxicity among a group of similarly treated glioblastoma patients

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. Methods : We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. Results: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. Conclusions: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain

Evaluating the risk for Usutu virus circulation in Europe : comparison of environmental niche models and epidemiological models

Abstract Background Usutu virus (USUV) is a mosquito-borne flavivirus, reported in many countries of Africa and Europe, with an increasing spatial distribution and host range. Recent outbreaks leading to regional declines of European common blackbird (Turdus merula) populations and a rising number of human cases emphasize the need for increased awareness and spatial risk assessment. Methods Modelling approaches in ecology and epidemiology differ substantially in their algorithms, potentially resulting in diverging model outputs. Therefore, we implemented a parallel approach incorporating two commonly applied modelling techniques: (1) Maxent, a correlation-based environmental niche model and (2) a mechanistic epidemiological susceptible-exposed-infected-removed (SEIR) model. Across Europe, surveillance data of USUV-positive birds from 2003 to 2016 was acquired to train the environmental niche model and to serve as test cases for the SEIR model. The SEIR model is mainly driven by daily mean temperature and calculates the basic reproduction number R0. The environmental niche model was run with long-term bio-climatic variables derived from the same source in order to estimate climatic suitability. Results Large areas across Europe are currently suitable for USUV transmission. Both models show patterns of high risk for USUV in parts of France, in the Pannonian Basin as well as northern Italy. The environmental niche model depicts the current situation better, but with USUV still being in an invasive stage there is a chance for under-estimation of risk. Areas where transmission occurred are mostly predicted correctly by the SEIR model, but it mostly fails to resolve the temporal dynamics of USUV events. High R0 values predicted by the SEIR model in areas without evidence for real-life transmission suggest that it may tend towards over-estimation of risk. Conclusions The results from our parallel-model approach highlight that relying on a single model for assessing vector-borne disease risk may lead to incomplete conclusions. Utilizing different modelling approaches is thus crucial for risk-assessment of under-studied emerging pathogens like USUV

Deep reinforcement learning-based dynamic scheduling

Attempts to address the production scheduling problem thus far rely on simplifying assumptions, such as static environment and inflexible size of the problem, which compromises the schedule performance in practice due to many unpredictable disruptions to the system. Thus, the study of scheduling in the presence of real-time events, termed dynamic scheduling, continues to attract attention given the agility, flexibility, and timeliness modern production systems must deliver. Additionally, the changing nature of the manufacturing system also raises new challenges to existing scheduling strategies. At the front-end, the development of advanced data creation and exchange frameworks such as the Internet of things and cyber-physical system and their applications to the industrial environment have created an abundance of industrial data, while at the backend, edge and cloud computing technologies greatly enhance the capacity to process that data. Industrial data must be mined and analyzed so that the investment in infrastructure is not wasted, and the production system managed more effectively and in real-time. Many data-driven technologies have been adopted in scheduling research, a promising candidate among them being reinforcement learning (RL) which is able to build a direct mapping from observation of environment to actions that improve its performance. In this thesis, a deep multi-agent reinforcement learning (deep MARL) architecture is proposed to solve the dynamic scheduling problem (DSP). The deep reinforcement learning (DRL) algorithm is used to train the decentralized scheduling agents, to capture the relationship between information on the factory floor and scheduling objectives, with the aim of making real-time decisions for a manufacturing system with frequent unexpected events. Two major aspects of deep MARL application to DSP are addressed in this work, namely the conversion from traditional static scheduling problem (SSP) to dynamic scheduling in a practical context, and the adaptation of existing deep MARL algorithms to solve the scheduling problem in such an environment. Some impractical constraints of traditional studies are removed to create a research context that is closer to actual practice, result in a scheduling problem of variable size and scope. Specialized state and action representations that can handle the ever-changing specification of problem are developed; the criteria of feature selection in dynamic environment are also discussed. Recent progressions in DRL and MARL research are integrated into the proposed approach after selection and adaptation. In addition, various improvements to common deep MARL architecture are proposed, including the lightweight multilayer perceptron (MLP) encoder that is efficient in handling unstructured industrial data, a training scheme under the multi-agent architecture to improve the stability of training and overall performance, and knowledge-based reward-shaping techniques to decompose the joint reward signal into individual utilities to speed up the learning and encourage cooperative behavior between agents. Simulation studies are then conducted for the ablation study and validation. In the first stage, the performance of the proposed approach, either as individual components or as an integrated model, are tested in iterative simulation runs within which a unique instance of production is created. Meanwhile, a set of DRL-based approaches from recent publications are run in parallel. Results suggest that the contribution of each improvement is significant; the integrated architecture also delivers stronger performance than peer DRL-based approaches. For the validation, a set of priority rules that have strong performance in specified context and are widely applied in actual production scheduling are used as the benchmark. Proposed approach also provides performance gain compared to the strongest rule, with a minor increase in computation cost and negligible latency in decision-making.Doctor of Philosoph

Deep reinforcement learning for dynamic scheduling of a flexible job shop

The ability to handle unpredictable dynamic events is becoming more important in pursuing agile and flexible production scheduling. At the same time, the cyber-physical convergence in production system creates massive amounts of industrial data that needs to be mined and analysed in real-time. To facilitate such real-time control, this research proposes a hierarchical and distributed architecture to solve the dynamic flexible job shop scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agents, to capture the relationship between production information and scheduling objectives, and make real-time scheduling decisions for a flexible job shop with constant job arrivals. Specialised state and action representations are proposed to handle the variable specification of the problem in dynamic scheduling. Additionally, a surrogate reward-shaping technique to improve learning efficiency and scheduling effectiveness is developed. A simulation study is carried out to validate the performance of the proposed approach under different scenarios. Numerical results show that not only does the proposed approach deliver superior performance as compared to existing scheduling strategies, its advantages persist even if the manufacturing system configuration changes