Miracle-dz/UniFAR-FTUs huggingface dataset:
Country-Level Analysis of the Association between Maternal Obesity and Neonatal Mortality in 34 Sub-Saharan African Countries

Background: Prior work examining the association of maternal obesity and neonatal mortality indicate the presence of a positive relationship. However, regional evidence to provide insight on country-level heterogeneities within sub-Saharan Africa (SSA) with nationally representative datasets are non-existent. Objective: We aimed to determine the relationship between maternal obesity and neonatal mortality at the country level in SSA countries. Moreover, we also estimate regional measures of association to complement previous findings. Methods: Demographic and Health Survey (DHS) data from 34 SSA countries conducted from 2006–2016 were used for this study. After missing data (36.9% of cases) were addressed with multiple imputations, we identified a total of 175,860 women for the analysis. Complete case and multiply imputed datasets were analyzed individually with multilevel logistic regression models. Potential confounders adjusted for in the regression model included maternal age, level of educational attainment, area of residence, access to prenatal care, birth order and multiple birth (singleton vs twin birth). Regional and country-specific associations were computed, and unadjusted and adjusted odds ratios (ORs), along with the confidence intervals (CIs) were reported. Findings: Of the total study population, 8,451 (7.6%) were obese. In the regional level analyses, maternal obesity was associated with 40% increased odds of neonatal deaths. This finding was consistent in subgroup analyses by urban and rural residence, and geographic region of residence in SSA. Additionally, obese women were more likely to report neonatal death in the first week of life (OR, days 0–1: 1.39, 95% CI 1.15–1.69; OR, days 2–6: 1.35, 95% CI 1.02–1.79). In the individual country analyses, majority of the countries studied had central estimates supporting elevated odds of neonatal mortality, but the confidence intervals were imprecise. Conclusion: This study highlights the potential burden of neonatal mortality borne by obese women in SSA. There is, however, a need for longitudinal studies to confirm the results.
A(eye): A Review of Current Applications of Artificial Intelligence and Machine Learning in Ophthalmology

Artificial intelligence (AI) is a branch of computer science that aims to enable computers to perform human-like tasks. Although AI is a broad discipline, machine learning is a specific branch of AI that uses computer algorithms capable of “learning” through the simulation of human intelligence. Machine learning algorithms have been applied to the medical field since the 1970s,1 and since that time have proven useful in computerassisted diagnosis, screening, and prognostication of disease.2–7 Ophthalmology is uniquely capable of capitalizing on the promise of AI. Ophthalmologists, during routine clinical encounters, generate robust data sources capable of supporting machine learning algorithms including multimodal ophthalmic images and quantifiable metrics such as visual acuity (VA), intraocular pressure, and cup to disk ratio. To date, AI techniques have been applied to ophthalmology to screen for and diagnose diseases, such as diabetic retinopathy (DR), age-related macular degeneration (AMD), macular edema (ME), glaucoma, keratoconus, postlaserassisted in situ keratomileusis corneal ectasia, retinopathy of prematurity (ROP), and cataracts, as well as predict the prognosis of various ophthalmic diseases. Advances in ophthalmology-specific AI stand to increase patient access to clinical screening and diagnosis as well as decrease health care costs, especially when applied to high-risk populations, low-resource communities, or when combined with telemedicine initiatives. This review provides an introduction to AI andmachine learning, as well as an overview of current applications in the field of ophthalmology.
Dynamic hyperpolarized 13C MR spectroscopic imaging using SPICE in mouse kidney at 9.4 T

This study aims to investigate the feasibility of dynamic hyperpolarized 13C MR spectroscopic imaging (MRSI) using the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) technique and an estimation of the spatially resolved conversion constant rate (kpl). An acquisition scheme comprising a single training dataset and several imaging datasets was proposed considering hyperpolarized 13C circumstances. The feasibility and advantage of the scheme were investigated in two parts: (a) consistency of spectral basis over time and (b) accuracy of the estimated kpl. The simulations and in vivo experiments support accurate kpl estimation with consistent spectral bases. The proposed method was implemented in an enzyme phantom and via in vivo experiments. In the enzyme phantom experiments, spatially resolved homogeneous kpl maps were observed. In the in vivo experiments, normal diet (ND) mice and high‐fat diet (HFD) mice had kpl (s−1) values of medullar (ND: 0.0119 ± 0.0022, HFD: 0.0195 ± 0.0005) and cortical (ND: 0.0148 ±0.0023, HFD: 0.0224 ±0.0054) regions which were higher than vascular (ND: 0.0087 ±0.0013, HFD: 0.0132 ±0.0050) regions. In particular, the kpl value in the medullar region exhibited a significant difference between the two diet groups. In summary, the feasibility of using modified SPICE for dynamic hyperpolarized 13C MRSI was demonstrated via simulations and in vivo experiments. The consistency of spectral bases over time and the accuracy of the estimated kpl values validate the proposed acquisition scheme, which comprises only a single training dataset. The proposed method improved the spatial resolution of dynamic hyperpolarized 13C MRSI, which could be used for kpl estimation using high signal‐to‐noise ratio spectral bases.
What is known about the quality of out-of-hospital emergency medical services in the Arabian Gulf States? A systematic review

Background The Emergency Medical Services (EMS) have been developed in the Arabian Gulf States (AGS) in the last three decades. The EMS needs continuous quality assessment of their performance to improve and provide the best out-of-hospital care. This study aims to assess the quality of EMS in the AGS according to the six quality domains of the Institute of Medicine. Methods We searched four databases (i.e., PubMed, EMBASE, Web of Science, and CINAHL) for studies that reported on the quality of EMS in any of the AGS using clinical or non-clinical performance indicators. To quantify study quality and risk of bias, the adapted Newcastle Ottawa Scale was used. We focused on structural and functional indicators, clinical and non-clinical. Results Twenty-five studies were eligible for inclusion. One study contained result of safety, fifteen time-centeredness, twenty effectiveness, five patient-centeredness, and thirteen studies reported on equity of EMS. None of the studies reported on efficiency of EMS. A significant proportion of studies showed high scores on the Newcastle-Ottawa scale. Limited studies on EMS quality were available, not covering all relevant quality domains and not covering the whole AGS region. The equity domain showed the best outcome performance finding, whereas finding of the patient-centeredness domain showed room for improvement in the foreseeable future. Conclusion This review highlights the need for more and better studies of sufficient quality about all domains of quality in EMS in all the AGS. EMS research in Kuwait and Bahrain is warranted, as currently studies of EMS quality are unavailable for these States. Moreover, efficiency researches exploring this discipline should be conducted specially no studies were found has been searching this domain. Trial registration PROSPERO registration number: CRD42019123896.
Building a Semantic Health Data Warehouse in the Context of Clinical Trials: Development and Usability Study

Background The huge amount of clinical, administrative, and demographic data recorded and maintained by hospitals can be consistently aggregated into health data warehouses with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a semantic health data warehouse enabling both semantic description and retrieval of health information. Objective This study aimed to present a proof of concept of this semantic health data warehouse, based on the data of 250,000 patients from RUH, and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trials context. Methods The semantic health data warehouse relies on 3 distinct semantic layers: (1) a terminology and ontology portal, (2) a semantic annotator, and (3) a semantic search engine and NoSQL (not only structured query language) layer to enhance data access performances. The system adopts an entity-centered vision that provides generic search capabilities able to express data requirements in terms of the whole set of interconnected conceptual entities that compose health information. Results We assessed the ability of the system to assist the search for 95 inclusion and exclusion criteria originating from 5 randomly chosen clinical trials from RUH. The system succeeded in fully automating 39% (29/74) of the criteria and was efficiently used as a prescreening tool for 73% (54/74) of them. Furthermore, the targeted sources of information and the search engine–related or data-related limitations that could explain the results for each criterion were also observed. Conclusions The entity-centered vision contrasts with the usual patient-centered vision adopted by existing systems. It enables more genericity in the information retrieval process. It also allows to fully exploit the semantic description of health information. Despite their semantic annotation, searching within clinical narratives remained the major challenge of the system. A finer annotation of the clinical texts and the addition of specific functionalities would significantly improve the results. The semantic aspect of the system combined with its generic entity-centered vision enables the processing of a large range of clinical questions. However, an important part of health information remains in clinical narratives, and we are currently investigating novel approaches (deep learning) to enhance the semantic annotation of those unstructured data.
LOBSTER: an environment to design bioimage analysis workflows for large and complex fluorescence microscopy data

SUMMARY Open source software such as ImageJ and CellProfiler greatly simplified the quantitative analysis of microscopy images but their applicability is limited by the size, dimensionality and complexity of the images under study. In contrast, software optimized for the needs of specific research projects can overcome these limitations, but they may be harder to find, set up and customize to different needs. Overall, the analysis of large, complex, microscopy images is hence still a critical bottleneck for many Life Scientists. We introduce LOBSTER (Little Objects Segmentation & Tracking Environment), an environment designed to help scientists design and customize image analysis workflows to accurately characterize biological objects from a broad range of fluorescence microscopy images, including large images exceeding workstation main memory. LOBSTER comes with a starting set of over 75 image analysis workflows and images stemming from state-of-the-art image-based research projects. AVAILABILITY AND IMPLEMENTATION LOBSTER requires MATLAB (version > = 2015a), MATLAB Image processing toolbox, and MATLAB statistics & machine learning toolbox. Code source, online tutorials, video demonstrations, documentation and sample images are freely available from: https://sebastients.github. io. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A phase 1 study of the antibody‐drug conjugate brentuximab vedotin with re‐induction chemotherapy in patients with CD30‐expressing relapsed/refractory acute myeloid leukemia

Outcomes for patients with relapsed/refractory acute myeloid leukemia (R/R AML) remain poor. Novel therapies specifically targeting AML are of high interest. Brentuximab vedotin (BV) is an antibody‐drug conjugate that is specific for human CD30. In this phase 1 dose escalation study, the authors evaluated the safety of BV combined with mitoxantrone, etoposide, and cytarabine (MEC) re‐induction chemotherapy for patients with CD30‐expressing R/R AML.
Does type 2 diabetes confer higher relative rates of cardiovascular events in women compared with men?

Abstract Aims To investigate whether diabetes confers higher relative rates of cardiovascular events in women compared with men using contemporary data, and whether these sex-differences depend on age. Methods and results All Danish residents aged 40–89 years without a history major adverse cardiovascular events, including heart failure, as of 1 January 2012 until 31 December 2016 were categorized by diabetes-status and characterized by individual-level linkage of Danish nationwide administrative registers. We used Poisson regression to calculate overall and age-dependent incidence rates, incidence rate ratios, and women-to-men ratios for myocardial infarction, heart failure, ischaemic stroke, or cardiovascular death (MACE-HF). Among 218 549 (46% women) individuals with diabetes, the absolute rate of MACE-HF was higher in men than in women (24.9 vs. 19.9 per 1000 person-years). Corresponding absolute rates in men and women without diabetes were 10.1 vs. 7.0 per 1000 person-years. Comparing individuals with and without diabetes, women had higher relative rates of MACE-HF than men [2.8 (confidence interval, CI 2.9–2.9) in women vs. 2.5 (CI 2.4–2.5) in men] with a women-to-men ratio of 1.15 (CI 1.11–1.19, P < 0.001). The relative rates of MACE-HF were highest in the youngest and decreased with advancing age for both men and women, but the relative rates were higher in women across all ages, with the highest women-to-men ratio between age 50 and 60 years. Conclusion Although men have higher absolute rates of cardiovascular complications, the relative rates of cardiovascular complications associated with diabetes are higher in women than in men across all ages in the modern era.
Randomized controlled trial of a positive affect intervention to reduce HIV viral load among sexual minority men who use methamphetamine

In the era of HIV treatment as prevention (TasP), evidence‐based interventions that optimize viral suppression among people who use stimulants such as methamphetamine are needed to improve health outcomes and reduce onward transmission risk. We tested the efficacy of positive affect intervention delivered during community‐based contingency management (CM) for reducing viral load in sexual minority men living with HIV who use methamphetamine.
Weekday and Weekend Physical Activity and Stationary Behavior Patterns of People Living With HIV.

HIV infection can be successfully treated using antiretroviral therapy (ART), which consists of a set of drugs that restore the immune system and prevent HIV replication. Since the introduction of ART, there has been a dramatic reduction in HIV-related mortality and morbidity. However, because the medications helped to transitionHIV to a chronic disease, people living with HIV (PLWH) have experienced elevated rates of cardiovascular disease, dyslipidemia, diabetes, osteoporosis, and other noncommunicable diseases when compared with people without HIV (Eyawo et al., 2017). The elevated rates of comorbidities require attention to lifestyle factors that can affect health and quality of life for PLWH, such as physical activity (PA) and stationary behavior (SB; Tremblay et al., 2017; Vancampfort et al., 2018). Studies have demonstrated that physically active PLWH have lower rates of chronic diseases, decreased prevalence of fatigue and depression, better mental health, decreased risk of cardiovascular disease, and better quality of life (Monroe et al., 2017; Quiles, Garber, & Ciccolo, 2018). SB refers to any waking behavior done while lying, reclining, sitting, or standing, with no ambulation, irrespective of energy expenditure (it differs from sedentary behavior, which also includes energy expenditure measurement; Tremblay et al., 2017). Moreover, time spent in SB is independently associated with deleterious health outcomes (i.e., all-cause mortality, cardiovascular disease incidence and mortality, cancer incidence and mortality, and Type 2 diabetes incidence) regardless of PA in the general population (Biswas et al., 2015). In general, PLWHspendmost of their time engaged in SB (Vancampfort,Mugisha,DeHert, Probst,&Stubbs, 2017) and a considerable proportion of PLWH do not meet the recommendations for PA (that adults engage in at least 150 min of moderate-to-vigorous PA [MVPA] during theweek; Piercy et al., 2018; Vancampfort et al., 2018). Considering that PLWH experience high symptom burden, and the importance of PA and SB on health-related outcomes, reliable and accurate data regarding PA and SB in this population are necessary (Eyawo et al., 2017; Piercy et al., 2018). The accurate measurement of PA and SB is also important to identify correlates and determinants and to inform policies for health promotion. Moreover, it serves to evaluate the efficacy of interventions to increase PA and to lower SB levels. However, PA and SB are difficult to measure accurately, and in different measurement methods, increasing attention is being given to accelerometry. Accelerometry is one objective method to measure PA, and different parameters can be estimated using accelerometers, such as time spent in different intensities of PA, number and length of activity bouts, number of steps, amount of time spent in SB, breaks in SB, and others. Given the characteristics of PLWH, who are known to have a high prevalence of comorbidities, fatigue, Sponsorships or competing interests thatmay be relevant to content are disclosed at the end of this article.
Generalized AIC and chi-squared statistics for path models consistent with directed acyclic graphs.

We explain how to obtain a generalized maximum likelihood chi-square statistic, X2 ML and a full-model Akaike Information Criterion (AIC) statistic for piecewise structural equation modeling (SEM); i.e. structural equations without latent variables whose causal topology can be represented as a directed acyclic graph (DAG). The full piecewise SEM is decomposed into submodels as a Markov network, each of which can have different distributional assumptions or functional links and that can be modeled by any method that produces maximum likelihood parameter estimates. The generalized X2 ML is a function of the difference in the maximum likelihoods of the model and its saturated equivalent and the full-model AIC is calculated by summing the AIC statistics of each of the submodels.
Efficiency and Effectiveness of some Compounds on Aonidiella aurantii (Maskell) and its Associated Natural Enemies on Mandarin Trees at Qena Governorate

The toxic effect of six compounds were determined on A. aurantii different stages attacking mandarin trees in the field at Nag-Hammady, Qena Governorate, during 2010 and 2011 seasons, also their side effect on the associated natural enemies were studied . The compounds used were Super Misrona + Actilic, KZOil + Admiral, KZoil, Super Misrona, Actelic and Admiral. Data clearly showed that the differences between treatments were significant in regardless of sampling dates and seasons. Super Misrona+Actilic and KZOil+Admiral were the most effective compound against nymph, young adult female, adult gravid female and total population of A. aurantii, while, Admiral recorded the lowest effect during both seasons. In regard of natural enemies, Actelic was found to be the most toxic and harmful one in both seasons. However, the lower toxic effects were found in KZ -Oil and Super Misrona on parasitoid and Admiral on predator.
Multi-objective Analysis for Mixed-model Assembly Line Using Elite-induced PSA

Contemporary diversified customer demands boost the importance of just-in-time and agile manufacturing in much greater extent. Accordingly, a mixed-model assembly line, which is small-lot-multi-kinds production, has been introduced and popularized. Focusing on a car production as a typical example, in this paper, we concern with a sequencing problem with a paint line as its preceding process. Then, we noticed that it is necessary to install the work-in-process (WIP) inventory between the two lines since they are to be operated in different manners. Though a large volume of WIP inventory can lessen defects caused by various deviations occurred during the production, it will aversively increase operating cost and production lead time and vice versa. Such trade-off analysis is amenable to multi-objective approach. Hence, we propose a new method for multi-objective analysis named as E-PSA (Elite-induced Pareto Simulated Annealing) where traditional weighting method and evolutionary method known as PSA (Pareto Simulated Annealing) are being integrated. Through numerical experiments, evaluation of our new method is carried out. Results are analyzed and compared with a conventional PSA method based on our previous study.
Cross-Layer Group Regularization for Deep Neural Network Pruning

Improving weights sparsity is a common strategy for deep neural network pruning. Most existing methods use regularizations that only consider structural sparsity within an individual layer. In this paper, we propose a cross-layer group regularization taking into account the statistics from multiple layers. For residual networks, we use this approach to align kernel sparsity across layers that are tied to each other through element-wise operations: the ith kernel of these layers are put into one regularization group, they either stay or be removed simultaneously during pruning. In this way, the computational and parameter storage cost could be significantly reduced. Experimental results show that this method does not only improve weights sparsity but also align kernel weights sparsity across related layers. Our method is able to prune ResNet up to 90.4% of parameters and improve runtime by 1.5x speedup, without loss of accuracy.
Compiling a cognition-based thematic monolingual lexicon

This study falls within the scope of cognitive lexicography which uses cognitive linguistic theories in lexicographic practice. The main objective of the study is to create a cognition-based monolingual thematic lexicon. The lexicon tests the validity of using cognitive linguistics, which uses language to reveal the human perception of a concept, in defining controversial multidisciplinary concepts. To that end, violence is selected as a case study and FrameNet is recruited as a cognitive linguistic resource. Cambridge Smart Thesaurus and WordNet are used as secondary resources to FrameNet. English TenTen corpus is employed to authenticate the findings before placing them in the lexicon. A twelve-frame lexicon is the result of the study. The constructed lexicon linguistically includes more than 250 violenceexpressing word senses, defined and placed within their violence-associated frames. Some frames are cited from FrameNet without modification, while others are conceptually and linguistically modified. More important, some violencespecific frames are newly-reported. Evidently, studying how physical violence is linguistically expressed displays how the concept is structured in the human cognition. Thus, an empirical cognition-based definition of violence is suggested. This meets the challenge of the multiple sociological, psychological, political and criminological definitions. Moreover, a comprehensive definition of violence is recommended to include both its associated frames and expressing words.