Association of adverse perinatal outcomes of intrahepatic cholestasis of pregnancy with biochemical markers

__Background__ Intrahepatic cholestasis of pregnancy is associated with adverse perinatal outcomes, but the association with the concentration of specific biochemical markers is unclear. We aimed to quantify the adverse perinatal effects of intrahepatic cholestasis of pregnancy in women with increased serum bile acid concentrations and determine whether elevated bile acid concentrations were associated with the risk of stillbirth and preterm birth. __Methods__ We did a systematic review by searching PubMed, Web of Science, and Embase databases for studies published from database inception to June 1, 2018, reporting perinatal outcomes for women with intrahepatic cholestasis of pregnancy when serum bile acid concentrations were available. Inclusion criteria were studies defining intrahepatic cholestasis of pregnancy based upon pruritus and elevated serum bile acid concentrations, with or without raised liver aminotransferase concentrations. Eligible studies were case-control, cohort, and populationbased studies, and randomised controlled trials, with at least 30 participants, and that reported bile acid concentrations and perinatal outcomes. Studies at potential higher risk of reporter bias were excluded, including case reports, studies not comprising cohorts, or successive cases seen in a unit; we also excluded studies with high risk of bias from groups selected (eg, a subgroup of babies with poor outcomes were explicitly excluded), conference abstracts, and Letters to the Editor without clear peer review. We also included unpublished data from two UK hospitals. We did a random effects meta-analysis to determine risk of adverse perinatal outcomes. Aggregate data for maternal and perinatal outcomes were extracted from case-control studies, and individual patient data (IPD) were requested from study authors for all types of study (as no control group was required for the IPD analysis) to assess associations between biochemical markers and adverse outcomes using logistic and stepwise logistic regression. This study is registered with PROSPERO, number CRD42017069134. __Findings__ We assessed 109 full-text articles, of which 23 studies were eligible for the aggregate data meta-analysis (5557 intrahepatic cholestasis of pregnancy cases and 165136 controls), and 27 provided IPD (5269 intrahepatic cholestasis of pregnancy cases). Stillbirth occurred in 45 (0·91%) of 4936 intrahepatic cholestasis of pregnancy cases and 519 (0·32%) of 163947 control pregnancies (odds ratio [OR] 1·46 [95% CI 0·73–2·89]

A Comfort-Aware Energy Efficient HVAC System Based on the Subspace Identification Method

A proactive heating method is presented aiming at reducing the energy consumption in a HVAC system while maintaining the thermal comfort of the occupants. The proposed technique fuses time predictions for the zones’ temperatures, based on a deterministic subspace identification method, and zones’ occupancy predictions, based on a mobility model, in a decision scheme that is capable of regulating the balance between the total energy consumed and the total discomfort cost. Simulation results for various occupation-mobility models demonstrate the efficiency of the proposed technique

Security of Gas Pipelines

Security of the natural gas supply strongly depends on the integrity of the transportation pipelines. The statistical evidence shows that the most probable cause of the break occurrence at natural gas pipeline is the external third party interference. A potential damage to the surrounding objects and violation of the people lives during the pipeline accident depends oil the mass flow rate of the natural gas leakage from the break. The paper presents an efficient method for the prediction of the natural gas leakage rates in case of pipeline accidents, as well as for the prediction of transient gas dynamic forces that are generated in case of an unsteady fluid flow and a fluid discharge from the pressurized volume to the Surrounding. The method application is demonstrated on the test cases of natural gas Outflow from a high pressure main transportation gas pipeline and a break occurrence at the distribution pipeline. Obtained data are a necessary input to the safe design of pipeline structures and supports

Sandfox Project: Optimizing the Relationship between the User Interface and Artificial Intelligence to Improve Energy Management in Smart Buildings

International audienceThis paper deals with energy efficiency in buildings in order to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some software exist to typically record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts, as well as adapted reports to the different stakeholders. The system designed in the sandfox project has for objective to fill this gap. To improve the energy management, an innovative system should aim at visualizing the streaming data, editing reports, and detecting anomalies, for different stakeholders, such as policy makers, energy managers , researchers, technical staff or end-users of these buildings. The paper presents the User-Centred Design approach that was used to collect the required needs from different stakeholders. The developed AI system is called sandman (semi-Supervised ANomaly Detection with Multi-AgeNt systems). It processes data in a time constrained manner to detect anomalies as early as possible. sandman is based on the paradigm of self-adaptive multi-agent systems. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling