10,571 research outputs found

    Small newborns in post-conflict Northern Uganda: Burden and interventions for improved outcomes

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    Introduction: A small newborn can be the result of either a low birthweight (LBW), or a preterm birth (PB), or both. LBW can be due to either a preterm appropriate-for gestational-age (preterm-AGA), or a term small-for-gestational age (term-SGA) or intrauterine growth restriction (IUGR). An IUGR is a limited in-utero foetal growth rates or foetal weight < 10th percentile. Small newborns have an increased risk of dying, particularly in low-resource settings. We set out to assess the burden, the modifiable risk factors and health outcomes of small newborns in the post-conflict Northern Ugandan district of Lira. In addition, we studied the use of video-debriefing when training health staff in Helping Babies Breathe. Subjects and methods: In 2018-19, we conducted a community-based cohort study on 1556 mother-infant dyads, nested within a cluster randomized trial. In our cohort study, we estimated the incidence and risk factors for LBW and PB and the association of LBW with severe outcomes. We explored the prevalence of and factors associated with neonatal hypoglycaemia, as well as any association between neonatal death and hypoglycaemia. In addition, we conducted a cluster randomized trial to compare Helping Babies Breathe (HBB) training in combination with video debriefing to the traditional HBB training alone on the attainment and retention of health worker neonatal resuscitation competency. Results: The incidence of LBW and PB in our cohort was lower than the global estimates, 7.3% and 5.0%, respectively. Intermittent preventive treatment for malaria was associated with a reduced risk of LBW. HIV infection was associated with an increased risk of both LBW and PB, while maternal formal education (schooling) of ≥7 years was associated with a reduced risk of LBW and PB. The proportions of neonatal deaths were many-folds higher among LBW infants compared to their non-LBW counterparts. The proportion of neonatal deaths among LBW was 103/1000 live births compared to 5/1000 among the non-LBW. The prevalence of neonatal hypoglycaemia in our cohort was 2.5%. LBW and PB each independently were associated with an increased risk of neonatal hypoglycaemia. Neonatal hypoglycaemia was associated with an increased risk of hospitalisation and severe outcomes. We demonstrated that neonatal resuscitation training with video debriefing, improved competence attainment and retention among health workers, compared to traditional HBB training alone. Conclusion: In northern Uganda, small infants still have a many-fold higher risk of dying compared to normal infants. In addition, small infants are also at more risk of neonatal hypoglycaemia compared to normal infants. Efforts are needed to secure essential newborn care, should we reach the target of Sustainable Development Goal number 3.2 of reducing infant mortality to less than 12/1000 live births by 2030

    Fully-Autonomous, Vision-based Traffic Signal Control: from Simulation to Reality

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    Ineffective traffic signal control is one of the major causes of congestion in urban road networks. Dynamically changing traffic conditions and live traffic state estimation are fundamental challenges that limit the ability of the existing signal infrastructure in rendering individualized signal control in real-time. We use deep reinforcement learning (DRL) to address these challenges. Due to economic and safety constraints associated training such agents in the real world, a practical approach is to do so in simulation before deployment. Domain randomisation is an effective technique for bridging the reality gap and ensuring effective transfer of simulation-trained agents to the real world. In this paper, we develop a fully-autonomous, vision-based DRL agent that achieve adaptive signal control in the face of complex, imprecise, and dynamic traffic environments. Our agent uses live visual data (i.e. a stream of real-time RGB footage) from an intersection to extensively perceive and subsequently act upon the traffic environment. Employing domain randomisation, we examine our agent’s generalisation capabilities under varying traffic conditions in both the simulation and the real-world environments. In a diverse validation set independent of training data, our traffic control agent reliably adapted to novel traffic situations and demonstrated a positive transfer to previously unseen real intersections despite being trained entirely in simulation

    QSAR based virtual screening derived identification of a novel hit as a SARS CoV-229E 3CLpro Inhibitor: GA-MLR QSAR modeling supported by molecular Docking, molecular dynamics simulation and MMGBSA calculation approaches

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    Congruous coronavirus drug targets and analogous lead molecules must be identified as quickly as possible to produce antiviral therapeutics against human coronavirus (HCoV SARS 3CLpro) infections. In the present communication, we bear recognized a HIT candidate for HCoV SARS 3CLpro inhibition. Four Parametric GA-MLR primarily based QSAR model (R2:0.84, R2adj:0.82, Q2loo: 0.78) was once promoted using a dataset over 37 structurally diverse molecules along QSAR based virtual screening (QSAR-VS), molecular docking (MD) then molecular dynamic simulation (MDS) analysis and MMGBSA calculations. The QSAR-based virtual screening was utilized to find novel lead molecules from an in-house database of 100 molecules. The QSAR-vS successfully offered a hit molecule with an improved PEC50 value from 5.88 to 6.08. The benzene ring, phenyl ring, amide oxygen and nitrogen, and other important pharmacophoric sites are revealed via MD and MDS studies. Ile164, Pro188, Leu190, Thr25, His41, Asn46, Thr47, Ser49, Asn189, Gln191, Thr47, and Asn141 are among the key amino acid residues in the S1 and S2 pocket. A stable complex of a lead molecule with the HCoV SARS 3CLpro was discovered using MDS. MM-GBSA calculations resulted from MD simulation results well supported with the binding energies calculated from the docking results. The results of this study can be exploited to develop a novel antiviral target, such as an HCoV SARS 3CLpro Inhibitor

    Assessing Consistency in Single-Case Data Features Using Modified Brinley Plots

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    The current text deals with the assessment of consistency of data features from experimentally similar phases and consistency of effects in single-case experimental designs. Although consistency is frequently mentioned as a critical feature, few quantifications have been proposed so far: namely, under the acronyms CONDAP (consistency of data patterns in similar phases) and CONEFF (consistency of effects). Whereas CONDAP allows assessing the consistency of data patterns, the proposals made here focus on the consistency of data features such as level, trend, and variability, as represented by summary measures (mean, ordinary least squares slope, and standard deviation, respectively). The assessment of consistency of effect is also made in terms of these three data features, while also including the study of the consistency of an immediate effect (if expected). The summary measures are represented as points on a modified Brinley plot and their similarity is assessed via quantifications of distance. Both absolute and relative measures of consistency are proposed: the former expressed in the same measurement units as the outcome variable and the latter as a percentage. Illustrations with real data sets (multiple baseline, ABAB, and alternating treatments designs) show the wide applicability of the proposals. We developed a user-friendly website to offer both the graphical representations and the quantifications

    Innovative Hybrid Approaches for Vehicle Routing Problems

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    This thesis deals with the efficient resolution of Vehicle Routing Problems (VRPs). The first chapter faces the archetype of all VRPs: the Capacitated Vehicle Routing Problem (CVRP). Despite having being introduced more than 60 years ago, it still remains an extremely challenging problem. In this chapter I design a Fast Iterated-Local-Search Localized Optimization algorithm for the CVRP, shortened to FILO. The simplicity of the CVRP definition allowed me to experiment with advanced local search acceleration and pruning techniques that have eventually became the core optimization engine of FILO. FILO experimentally shown to be extremely scalable and able to solve very large scale instances of the CVRP in a fraction of the computing time compared to existing state-of-the-art methods, still obtaining competitive solutions in terms of their quality. The second chapter deals with an extension of the CVRP called the Extended Single Truck and Trailer Vehicle Routing Problem, or simply XSTTRP. The XSTTRP models a broad class of VRPs in which a single vehicle, composed of a truck and a detachable trailer, has to serve a set of customers with accessibility constraints making some of them not reachable by using the entire vehicle. This problem moves towards VRPs including more realistic constraints and it models scenarios such as parcel deliveries in crowded city centers or rural areas, where maneuvering a large vehicle is forbidden or dangerous. The XSTTRP generalizes several well known VRPs such as the Multiple Depot VRP and the Location Routing Problem. For its solution I developed an hybrid metaheuristic which combines a fast heuristic optimization with a polishing phase based on the resolution of a limited set partitioning problem. Finally, the thesis includes a final chapter aimed at guiding the computational evaluation of new approaches to VRPs proposed by the machine learning community
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