Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Nutritional Strategies on Glucose Homeostasis in Gestational Diabetes Mellitus: A Systematic Review and Network Meta-Analysis

Background Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, and nutritional therapy is the basis of GDM treatment. However, the effects of different forms of nutritional supplementation on improving gestational diabetes are uncertain. Objective We conducted a network meta-analysis to evaluate the effects of supplementation with different nutrients on glucose metabolism in women with GDM. Methods We conducted a literature search using PubMed, EMBASE, and the Cochrane Library to identify randomized controlled trials (RCTs) comparing the differences between different nutritional strategies in women with GDM. The Cochrane tool was used to assess the risk of bias. Pairwise meta-analysis and network meta-analysis were used to compare and rank the effects of nutritional strategies for the improvement of fasting plasma glucose (FPG), serum insulin, and homeostasis model assessment-insulin resistance (HOMA-IR). Results We included thirteen RCTs with a total of 754 participants. Compared with placebo, omega-3, magnesium, vitamin D, zinc, and probiotics were more beneficial for improving FPG, serum insulin, and HOMA-IR. Network analysis showed that vitamin D supplementation was superior to omega-3 (-3.64 mg/dL, 95% CI: -5.77 to -1.51), zinc (-5.71 mg/dL, 95% CI: -10.19 to -1.23), probiotics (-6.76 mg/dL, 95% CI: -10.02 to -3.50), and placebo (-12.13 mg/dL, 95% CI: -14.55 to -9.70) for improving FPG. Magnesium supplementation was more beneficial for decreasing serum insulin compared with probiotics (-5.10 μIU/mL, 95% CI: -9.32 to -0.88) and placebo (-7.80 μIU/mL, 95% CI: -9.32 to -0.88) and placebo (-7.80 Conclusion Vitamin D supplementation significantly reduced FPG and regulated HOMA-IR. Magnesium supplementation was superior in decreasing serum insulin than supplementation with other nutrients. Nutrient supplementation seemed to have an effect on glucose homeostasis maintenance in patients with GDM and may be considered an adjunctive therapy.
A fertile matrix: the birth of the Forensic Psychotherapy Society

This paper traces some aspects of the fertile matrix within which, over many years, the application of a psychoanalytic perspective to clinical work with the most severely mentally ill, difficult and at times frightening patients in secure and community settings has evolved and now led to the recent inauguration of the Forensic Psychotherapy Society (FPS), a new Member Institution of the British Psychoanalytic Council. The Society provides both a framework for a national, multidisciplinary professional training in psychodynamic forensic psychotherapy and the potential launching pad for an active post-qualification organisation supporting CPD and all other forms of professional development. The particular contributions to the birth of the FPS of the Forensic Psychotherapy Department at West London Mental Health NHS Trust, partly because we know it best but also because it is part of the largest forensic psychiatric service in the United Kingdom, are described.
A qualitative exploration into the psychological support following weight loss surgery

According to the World Health Organisation, more than one in ten adults worldwide are obese (WHO, 2012), increasing the risk of comorbidities, resulting in public health concern worldwide. Unless medical intervention is offered preventing obesity, it is likely that it will cost the National Health Service (NHS) around £50 billion pounds by 2050. The increasing costs to the NHS more contemporarily include the cost and need for bariatric surgery. Therefore, the aim of this research is to ascertain whether people who have weight loss surgery, specifically, a gastric bypass, are offered enough psychological support post-operative. Such support has been proven essential, as losing the amount of weight with such procedure results in life changing physical and psychological experiences, therefore, support dealing with such changes is essential. A qualitative methodological approach was used with the expectation of receiving rich and meaningful data representing both participants’ worldviews. Semi-structured interviews incorporating two female participants both over eighteen years were conducted allowing the incorporation of questions about the participant’s beliefs and attitudes. An interpretive phenomenological approach (IPA) was used offering the researcher an understanding of the amount of psychological support received post-operatively, aiding them in accomplishing their weight loss goals and positive psychological well-being.
Évolution spontanément favorable d'une membrane épirétinienne

Une patiente de 26 ans presente une membrane epimaculaire associe a une baisse de l'acuite visuelle a 4/10 cinq ans apres une photocoagulation peripherique. L'evolution spontanee se fait vers la contraction et le detachement partiel de la membrane sur une periode de 5 ans permettant une remonte de l'acuite a 10/10. Six angiographies ont ete realisees durant cette periode et permettent de suivre cette evolution. Ce cas clinique nous rappelle que l'evolution d'une membrane peut etre favorable chez le patient jeune et que l'indication operatoire doit etre discutee en l'absence d'aggravation de la fonction visuelle.
Corticosteroid treatment of optic neuritis: A need to change treatment practices

History of corticosteroid and ACTH use in optic neuritis. Interest in the use of corticosteroids for many conditions, including optic neuritis, developed following the 1949 report of Hench et a12 concerning the effectiveness of these drugs in the treatment of rheumatoid arthritis. In general, the studies, which have mainly assessed ACTH, have demonstrated that treatment speeds recovery of optic neuritis. Because of small sample size and a reliance on visual acuity as the primary measure of outcome, these studies were inconclusive as to the visual benefit after 6 to 12 months of follow-up. ACTH. Numerous case reports proclaimed that ACTH was beneficial for optic neuritis prior to the 1966 publication of the first systematic study on the ~ub jec t . ~ In a double-blind prospective study of the treatment of 50 patients with acute retrobulbar neuritis with either ACTH or placebo, Rawson et a13 found that the ACTH-treated group recovered vision faster, but at 1 year the two groups were not significantly diflerent.3,4 In the Cooperative Multiple Sclerosis Study published in 1970; there were 41 cases in which optic neuritis was the acute exacerbation of MS. The 22 patients treated with ACTH improved faster than did the 19 patients treated with placebo, but there was no long-term follow-up. In 1974, Bowden et a16 reported on a prospective clinical trial, finding no benefit for ACTH compared with placebo in 54 patients with optic neuritis. Retrobulbar corticosteroids. Gould et a17 in 1977 reported on a prospective single-blind controlled clinical trial that randomized 74 patients with optic neuritis to a retrobulbar injection of triamcinolone or to a control group. The treated patients improved faster, but there were no differences between the two groups after 6 months. Prednisone. Although corticosteroids are prescribed commonly for optic neuritis, there have been no satisfactory controlled studies on the treatment of optic neuritis with oral prednisone. Intravenous methylprednisolone. Although there have been several randomized studies of intravenous corticosteroids for MS, reports of use of this treatment for optic neuritis have been limited to case reports. Spoor6 and Spoor and Rockwellg described rapid recovery of vision in two small series of patients with optic neuritis treated with intravenous methylprednisolone. Current treatment practices. Despite the paucity of evidence demonstrating efficacy, corticosteroids are commonly prescribed for optic neuritis. A 1986 survey of ophthalmologists and neurologists in Michigan and Florida (Beck RW: unpublished data) indicated that 65% of ophthalmologists and 90% of neurologists prescribed corticosteroids for optic neuritis; the remaining physicians prescribed no treatment. When treatment was prescribed, oral prednisone was utilized for about 90% of cases, with the remainder treated by an intravenous route or with ACTH.
Non specific immunological response in moderate malnutrition.

: The phagocytic and chemotactic activity of mononuclear leucocytes was assessed "in vitro", as were the total complement and component C3 and C4 serum levels, in twenty children suffering from moderate primary protein-calorie malnutrition in the 6-month to 5-year age group. The results were compared to those from well-nourished children of the same age and showed a reduction of the non-specific immunological response in the malnourished children, due to the intrinsic problem of the mononuclear leucocytes.
Planned health care non-attendance and asthma control among asthma patients in real life setting in an African country: a prospective cohort study

BACKGROUND: Compliance with scheduled medical visit is an indirect indicator of the adherence to treatment. The aim of this study was to determine the incidence of non-compliance with scheduled medical visits and investigate the predictors and relationship with disease control among patient with asthma in the sub-Saharan African setting. METHODS: We conducted a prospective study between January 2012 and August 2013. All patients with asthma receiving care at the Yaounde Jamot Hospital and CEDIMER medical practice were included. Planned medical visit occurred at month one, three, six and twelve following baseline evaluation. Regression models were employed to investigate the predictors of non-compliance. RESULTS: 186 patients [121 (65.1%) being female] were included. Their median age (25th-75th percentiles) was 35.5 (20.0-52.3) years. A total of 67 (36%) failed to attend at least one of the scheduled visits. The proportion of patients who failed to comply with scheduled visits was 48.4%, 68.8%, 73.5% and 79.5% at month one, three, six and twelve respectively. The accompanying rates of unacceptable asthma control were 17.2%, 24.4% and 20.8% respectively at three, six and twelve months of follow-up. Inadequate asthma control at baseline was the main predictor of non-compliance with medical visits, with adjusted odds ratio ranging from 2.00 to 4.67. Furthermore, non-sensitisation to non-pollinic aeroallergens was associated with 2.79 (95%CI 1.58-6.07) risk of non-compliance at one month of follow-up. CONCLUSION: Targeting education of patients with poorly controlled disease at baseline can potentially improve adherence to medical visit and anti-asthmatic treatments and outcomes of care.
Effect of a low fat elemental diet on pancreatic secretion during pancreatitis.

: With the use of a precise and unique model, pancreatic secretion was studied in three humans who had chronic pancreatitis. The observed results show a decrease in pancreatic secretory pressure and volume during the administration of an elemental diet, Vivonex HN, compared with regular feedings. Levels of secretory function were near those observed during fasting in the two patients receiving the elemental diet. The variably low levels of enzyme activity observed during the administration of Vivonex HN suggest only minimal stimulation of cholecystokinin. It would seem that the primary inhibitory effect of the elemental diet may be more specifically related to a reduction of secretin release. Although the numbers of observations carried out during this study were small, the precise nature of the model being studied allows the conclusion that the administration of a low fat elemental diet to patients with chronic pancreatitis produces minimal stimulation of pancreatic secretion.
NOMA Design With Power-Outage Tradeoff for Two-User Systems

This letter proposes a modified non-orthogonal multiple-access (NOMA) scheme for systems with a multi-antenna base station (BS) and two single-antenna users, where NOMA transmissions are conducted only when the absolute correlation coefficient (ACC) between the user channels exceeds a threshold and the BS uses matched-filter (MF) precoding along the user with the stronger average channel gain. We derive the average required transmit power to guarantee the signal-to-interference-plus-noise-ratio (SINR) levels of both users. Our results show that the average required transmit power grows logarithmically in the reciprocal of the ACC threshold and a non-zero threshold is necessary for finite average transmit power. Further, for the massive MIMO scenario, we derive scaling laws of the average transmit power and outage probability with respect to the antenna numbers, as well as their tradeoff law. Simulation results are shown to validate our theoretical results.
A neural network model of perception and reasoning

How perception and reasoning arise from neuronal network activity is poorly understood. This is reflected in the fundamental limitations of connectionist artificial intelligence, typified by deep neural networks trained via gradient-based optimization. Despite success on many tasks, such networks remain unexplainable black boxes incapable of symbolic reasoning and concept generalization. Here we show that a simple set of biologically consistent organizing principles confer these capabilities to neuronal networks. To demonstrate, we implement these principles in a novel machine learning algorithm, based on concept construction instead of optimization, to design deep neural networks that reason with explainable neuron activity. On a range of tasks including NP-hard problems, their reasoning capabilities grant additional cognitive functions, like deliberating through self-analysis, tolerating adversarial attacks, and learning transferable rules from simple examples to solve problems of unencountered complexity. The networks also naturally display properties of biological nervous systems inherently absent in current deep neural networks, including sparsity, modularity, and both distributed and localized firing patterns. Because they do not sacrifice performance, compactness, or training time on standard learning tasks, these networks provide a new black-box-free approach to artificial intelligence. They likewise serve as a quantitative framework to understand the emergence of cognition from neuronal networks.
Nonreciprocal Quantum Entanglement Against Backscattering

We propose how to achieve nonreciprocal optomechanical entanglement in a spinning resonator and maintain its quality against backscattering losses. We find that by splitting the counter-propagating optical modes in the resonator via the Sagnac effect, photon-phonon entanglement can be created from one side while prohibited from the other--that is, achieving quantum nonreciprocity in such a system. An important and counterintuitive feature of the nonreciprocal entanglement is its robustness against backscattering losses induced by impurities or material imperfections in practical devices. Building a new bridge between quantum control and nonreciprocal physics, our work reveals a new strategy to protect quantum entanglement which has applications in such a wide range of fields as backaction-immune quantum metrology and chiral quantum information networks.
A Systematic Survey of General Sparse Matrix-matrix Multiplication

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications and computing architectures over the past decades. The objective of this article is to provide a structured and comprehensive overview of the researches on SpGEMM. Existing researches have been grouped into different categories based on target architectures and design choices. Covered topics include typical applications, compression formats, general formulations, key problems and techniques, architecture-oriented optimizations, and programming models. The rationales of different algorithms are analyzed and summarized. This survey sufficiently reveals the latest progress of SpGEMM research to 2021. Moreover, a thorough performance comparison of existing implementations is presented. Based on our findings, we highlight future research directions, which encourage better design and implementations in later studies.
Towards Digital Engineering - The Advent of Digital Systems Engineering

Digital Engineering, the digital transformation of engineering to leverage digital technologies, is coming globally. This paper explores digital systems engineering, which aims at developing theory, methods, models, and tools to support the emerging digital engineering. A critical task is to digitalize engineering artifacts, thus enabling information sharing across platform, across life cycle, and across domains. We identify significant challenges and enabling digital technologies; analyze the transition from traditional engineering to digital engineering; define core concepts, including "digitalization", "unique identification", "digitalized artifacts", "digital augmentation", and others; present a big picture of digital systems engineering in four levels: vision, strategy, action, and foundation; briefly discuss each of main areas of research issues. Digitalization enables fast infusing and leveraging novel digital technologies; unique identification enables information traceability and accountability in engineering lifecycle; provenance enables tracing dependency relations among engineering artifacts; supporting model reproducibility and replicability; helping with trustworthiness evaluation of digital engineering artifacts.
PHS: A Toolbox for Parellel Hyperparameter Search

—We introduce an open source python framework named PHS - Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modiﬁcations inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efﬁcient method to propose the next query set of parameters.
An Optimal Statistical and Computational Framework for Generalized Tensor Estimation

This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imaging, genomics, and network analysis. The proposed estimator consists of finding a low-rank tensor fit to the data under generalized parametric models. To overcome the difficulty of non-convexity in these problems, we introduce a unified approach of projected gradient descent that adapts to the underlying low-rank structure. Under mild conditions on the loss function, we establish both an upper bound on statistical error and the linear rate of computational convergence through a general deterministic analysis. Then we further consider a suite of generalized tensor estimation problems, including sub-Gaussian tensor denoising, tensor regression, and Poisson and binomial tensor PCA. We prove that the proposed algorithm achieves the minimax optimal rate of convergence in estimation error. Finally, we demonstrate the superiority of the proposed framework via extensive experiments on both simulated and real data.
Thermal conductivity of free-standing silicon nanowire using Raman spectroscopy

Low dimensional systems, nanowires (NWs), in particular, have exhibited excellent optical and electronic properties. Understanding the thermal properties in semiconductor NWs is very important for their applications in electronic devices. In the present study, the thermal conductivity of a freestanding silicon NW is estimated by employing Raman spectroscopy. The advantage of this technique is that the excitation source (laser) acts as both the heater and probe. The variations of the first-order Raman peak position of the freestanding silicon NW with respect to temperature and laser power are recorded. From the analysis of effective laser power absorbed by exposed silicon NW and a detailed Raman study along with the concept of longitudinal heat distribution in silicon NW, the thermal conductivity of the freestanding silicon NW of ∼112 nm diameter is estimated to be ∼53 W m−1 K− 1.
CLARA: Clinical Report Auto-completion

Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to write accurate and detailed reports. Most existing methods try to generate the whole reports from the raw input with limited success because 1) generated reports often contain errors that need manual review and correction, 2) it does not save time when doctors want to write additional information into the report, and 3) the generated reports are not customized based on individual doctors’ preference. We propose CLinicAl Report Auto-completion (, an interactive method that generates reports in a sentence by sentence fashion based on doctors’ anchor words and partially completed sentences. earches for most relevant sentences from existing reports as the template for the current report. The retrieved sentences are sequentially modified by combining with the input feature representations to create the final report. In our experimental evaluation chieved 0.393 CIDEr and 0.248 BLEU-4 on X-ray reports and 0.482 CIDEr and 0.491 BLEU-4 for EEG reports for sentence-level generation, which is up to 35% improvement over the best baseline. Also via our qualitative evaluation, s shown to produce reports which have a significantly higher level of approval by doctors in a user study (3.74 out of 5 for s 2.52 out of 5 for the baseline).
Non-Asymptotic Bounds for Zeroth-Order Stochastic Optimization

We consider the problem of optimizing an objective function with and without convexity in a simulation-optimization context, where only stochastic zeroth-order information is available. We consider two techniques for estimating gradient/Hessian, namely simultaneous perturbation (SP) and Gaussian smoothing (GS). We introduce an optimization oracle to capture a setting where the function measurements have an estimation error that can be controlled. Our oracle is appealing in several practical contexts where the objective has to be estimated from i.i. d. samples, and increasing the number of samples reduces the estimation error. In the stochastic non-convex optimization context, we analyze the zeroth-order variant of the randomized stochastic gradient (RSG) and quasi-Newton (RSQN) algorithms with a biased gradient/Hessian oracle, and with its variant involving an estimation error component. In particular, we provide non-asymptotic bounds on the performance of both algorithms, and our results provide a guideline for choosing the batch size for estimation, so that the overall error bound matches with the one obtained when there is no estimation error. Next, in the stochastic convex optimization setting, we provide non-asymptotic bounds that hold in expectation for the last iterate of a stochastic gradient descent (SGD) algorithm, and our bound for the GS variant of SGD matches the bound for SGD with unbiased gradient information. We perform simulation experiments on synthetic as well as real-world datasets, and the empirical results validate the theoretical findings.