Miracle-dz/UniFAR-FTUs huggingface dataset:
A higher ratio of refined grain to whole grain is associated with a greater likelihood of chronic kidney disease: a population-based study

Abstract A growing number of studies suggest that diet and renal function are related. However, little is known about the link between both whole grain (WG) and refined grain (RG) consumption and kidney function parameters. Thus, we investigated the association of WG and RG with urinary albumin to creatinine ratio (ACR) and prevalent chronic kidney disease (CKD). Data from participants of the National Health and Nutrition Examination Surveys (NHANES) from 2005 to 2010 were collected. Estimated glomerular filtration rate (eGFR) was calculated by the CKD Epidemiology Collaboration equation. Survey design and sample weights were taken into consideration for statistical analyses. Finally, we included 16 325 participants from NHANES, 6·9 % of whom had prevalent CKD. In models adjusted for age, sex, race, fasting blood glucose, blood pressure, adiposity, hypertension and diabetes status, mean eGFR significantly increased across increasing quartiles of WG (Q1: 88·2 v. Q4: 95·4 ml/min per 1·73 m2, P<0·001), whereas it significantly decreased across increasing quartiles of RG (Q1: 97·2 v. Q4: 88·4 ml/min per 1·73 m2, P<0·001). Furthermore, serum uric acid levels and ACR significantly decreased across quartiles of WG (both P<0·001). In multivariable-adjusted logistic regression models, the likelihood of prevalent CKD was 21 % lower in the highest WG quartile compared with the lowest one. In conclusion, our results shed light on the beneficial impact of WG on kidney function and CKD, whereas RG is adversely associated with eGFR.
Win-Concurrent Sensory Cues Can Promote Riskier Choice

Reward-related stimuli can potently influence behavior; for example, exposure to drug-paired cues can trigger drug use and relapse in people with addictions. Psychological mechanisms that generate such outcomes likely include cue-induced cravings and attentional biases. Recent animal data suggest another candidate mechanism: reward-paired cues can enhance risky decision making, yet whether this translates to humans is unknown. Here, we examined whether sensory reward-paired cues alter decision making under uncertainty and risk, as measured respectively by the Iowa Gambling Task and a two-choice lottery task. In the cued versions of both tasks, gain feedback was augmented with reward-concurrent audiovisual stimuli. Healthy human volunteers (53 males, 78 females) performed each task once, one with and the other without cues (cued Iowa Gambling Task/uncued Vancouver Gambling Task: n = 63; uncued Iowa Gambling Task/cued Vancouver Gambling Task: n = 68), with concurrent eye-tracking. Reward-paired cues did not affect choice on the Iowa Gambling Task. On the two-choice lottery task, the cued group displayed riskier choice and reduced sensitivity to probability information. The cued condition was associated with reduced eye fixations on probability information shown on the screen and greater pupil dilation related to decision and reward anticipation. This pupil effect was unrelated to the risk-promoting effects of cues: the degree of pupil dilation for risky versus risk-averse choices did not differ as a function of cues. Together, our data show that sensory reward cues can promote riskier decisions and have additional and distinct effects on arousal. SIGNIFICANCE STATEMENT Animal data suggest that reward-paired cues can promote maladaptive reward-seeking by biasing cost-benefit decision making. Whether this finding translates to humans is unknown. We examined the effects of salient reward-paired audiovisual cues on decision making under risk and uncertainty in human volunteers. Cues had risk-promoting effects on a risky choice task and independently increased task-related arousal as measured by pupil dilation. By demonstrating risk-promoting effects of cues in human participants, our data identify a mechanism whereby cue reactivity could translate into maladaptive behavioral outcomes in people with addictions.
Frequency of medication error in pediatric anesthesia: A systematic review and meta‐analytic estimate

Clinical evidence shows that pediatric anesthesia patients are subject to a higher rate of life‐threatening medical errors than their adult counterparts. Medication error in adult anesthesia is estimated to occur to 1 in 133 anesthetic administrations, but such a figure has not been determined for pediatric anesthesia patients. Individual studies of medication error in pediatric anesthesia have ranged from rates of 0.01% to 1.92% of anesthetic uses. The present study is a systematic review that employs a meta‐analytic estimate to determine the rate of medication error in pediatric anesthesia.
Reflections on PPI from the ‘Action on Living Well: Asking You’ advisory network of people with dementia and carers as part of the IDEAL study

This article describes the work of the ‘Action on Living Well: Asking You’ group – an involvement group of people with dementia and carers attached to the IDEAL research study. The article describes the work of the group, the methods that have helped them to stay involved and people’s perspectives on their experiences of being involved and the impact it has had, for themselves and others. The article has been written following a reflective piece of work with the ‘Action on Living Well: Asking You’ group to review and remember the work of the past four years. An accompanying film brings to life the work and activities of the group, available at www.idealproject. org.uk/mclass/
Comparison of Noninvasive Dynamic Indices of Fluid Responsiveness Among Different Ventilation Modes in Dogs Recovering from Experimental Cardiac Surgery

Background Fluid resuscitation is a cornerstone of minimizing morbidity and mortality in critically ill patients, but the techniques for predicting fluid responsiveness is still a matter of debate. In this study, we aimed to evaluate the utility of noninvasive stroke volume variation (SVV), pulse pressure variation (PPV), and systolic pressure variation (SPV) as a dynamic predictor for assessing fluid responsiveness during different ventilation modes in anaesthetized, intubated dogs recovering from cardiac surgery. Material/Methods Thirty-six adult Beagle dogs undergoing experimental surgery for isolated right ventricular failure were monitored for SVV, PPV, and SPV simultaneously using electrical velocimetry device. The relationships between each indicator and SVI before and after volume loading were compared in 3 ventilatory modes: assist control (A/C), synchronized intermittent mandatory ventilation (SIMV), and continuous positive airway pressure (CPAP). Responders were defined as those whose stroke volume index increased by ≥10%. Results In all of the indices, the baseline values were greater in responders than in nonresponders (P<0.01) under A/C and SIMV. Receiver operating curve analysis confirmed the best predictive value during A/C [area under the curve (AUC): SVV, 0.90; PPV, 0.88; SPV, 0.85; P<0.05] followed by SIMV (AUC: SVV, 0.86; PPV, 0.83; CPAP, 0.80; P<0.05), with their sensitivities and specificities of ≥7 5%. By contrast, no statistically significance detected in any parameter during CPAP (AUC: SVV, 0.71; PPV, 0.66; CPAP, 0.65; P>0.05). Conclusions SVV, PPV, and SVV are all useful to predict cardiac response to fluid loading in dogs during A/C and SIMV, while their reliabilities during CPAP are poor.
Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model

PURPOSE Due to the low contrast, blurry boundaries, and large amount of shadows in breast ultrasound (BUS) images, automatic tumor segmentation remains a challenging task. Deep learning provides a solution to this problem, since it can effectively extract representative features from lesions and the background in BUS images. METHODS A novel automatic tumor segmentation method is proposed by combining a dilated fully convolutional network (DFCN) with a phase-based active contour (PBAC) model. The DFCN is an improved fully convolutional neural network with dilated convolution in deeper layers, fewer parameters, and batch normalization techniques; and has a large receptive field that can separate tumors from background. The predictions made by the DFCN are relatively rough due to blurry boundaries and variations in tumor sizes; thus, the PBAC model, which adds both region-based and phase-based energy functions, is applied to further improve segmentation results. The DFCN model is trained and tested in dataset 1 which contains 570 BUS images from 89 patients. In dataset 2, a 10-fold support vector machine (SVM) classifier is employed to verify the diagnostic ability using 460 features extracted from the segmentation results of the proposed method. RESULTS Advantages of the present method were compared with three state-of-the-art networks; the FCN-8s, U-net, and dilated residual network (DRN). Experimental results from 170 BUS images show that the proposed method had a Dice Similarity coefficient of 88.97 ± 10.01%, a Hausdorff distance (HD) of 35.54 ± 29.70 pixels, and a mean absolute deviation (MAD) of 7.67 ± 6.67 pixels, which showed the best segmentation performance. In dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.795 which is similar to the classification using the manual segmentation results. CONCLUSIONS The proposed automatic method may be sufficiently accurate, robust, and efficient for medical ultrasound applications.
CONVERSATIONAL MACHINE COMPREHENSION

Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context and the current question. To enable traditional, single-turn models to encode the history comprehensively, we introduce FLOW, a mechanism that can incorporate intermediate representations generated during the process of answering previous questions, through an alternating parallel processing structure. Compared to approaches that concatenate previous questions/answers as input, FLOW integrates the latent semantics of the conversation history more deeply. Our model, FLOWQA, shows superior performance on two recently proposed conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The effectiveness of FLOW also shows in other tasks. By reducing sequential instruction understanding to conversational machine comprehension, FLOWQA outperforms the best models on all three domains in SCONE, with +1.8% to +4.4% improvement in accuracy.
On achieving throughput optimality with energy prediction–based power allocation in 5G networks

The expected presence of huge number of low data rate devices in 5G/beyond 5G networks can compromise its overall capacity and cause an increase in carbon footprint. With continuous improvement in radio access technologies, these low data rate devices can be offloaded to self sustainable small cells operating in spectrum extensions (licensed/unlicensed). To ensure self‐sustainability and end‐to‐end connectivity of these low data rate devices naturally calls for energy harvesting (EH)–based cooperative communication solution. Therefore, in this paper, we propose an energy prediction–based power allocation (EPPA) policy to improve the utilization of intermittent EH arrivals to maximize throughput. Moreover, EH predictions are obtained through the training of an M‐order Markov model, which are further used to derive estimated throughput optimal (EPPA‐NO) and suboptimal (EPPA‐I) power allocation/data rate selection policies. An EPPA‐K scheme with exact knowledge of future arrivals are used as an upper bound to benchmark the performance of EPPA‐NO and EPPA‐I in terms of throughput, delay, energy consumption per bit, and respective complexity. It is observed that the performance of these schemes is highly dependent on the prediction accuracy. To this end, we further propose a learning‐based weighted linear regression scheme. For an energy‐constrained scenario, using weighted linear regression further improves energy efficiency over EPPA‐NO and EPPA‐I and reaches within 1% of EPPA‐K for a buffer size of 700 bits.
Emerging Potential of Immediate Early Response Gene X‐1 in Cardiovascular and Metabolic Diseases

Immediate early response gene X‐1 (IEX‐1) or immediate early response 3 is an immediate early response gene that is highly expressed in epithelial, endothelial, and immune cells, including macrophages.1, 2, 3, 4 Similar to other immediate early response genes, its induction can be rapidly induced in response to several stimuli, such as UV light, inflammatory cytokines, and mechanical strain.4, 5, 6, 7, 8 Under stress, many immediate early response genes, such as c‐Fos, c‐Jun, c‐Myc, Egr‐1, and other zinc‐finger proteins, usually serve as transcription factors, activators, or repressors to regulate the transcriptional activity of several genes that are critical for a cell to appropriately respond to stress.4, 9, 10, 11, 12 However, IEX‐1 lacks a DNA binding domain and its putative transcription activity remains elusive.4, 13 Investigations from our laboratory show that IEX‐1 controls mitochondrial superoxide production by regulating the electron transport chain and oxidative phosphorylation.13, 14, 15, 16 This action of IEX‐1 might explain the transcriptional activity associated with IEX‐1 because the reactive oxygen species (ROS) at low levels can act as a signaling molecule to alter gene transcription through reduction‐oxidation–sensitive transcription factors or a signaling pathway, such as nuclear factor‐κB (NF‐κB) and nuclear factor erythroid 2‐related factor.17, 18, 19 Consistent with a key role of mitochondria in vascular and metabolic functions, IEX‐1 deficiency impairs vascular function and increases energy expenditure in mice. A key role of IEX‐1 in regulation of mitochondrial respiration and ROS production may also help to explain how IEX‐1 produces both antiapoptotic and proapoptotic actions in different cell types. Macrophages, the central player in the pathogenesis of many chronic inflammatory conditions, express high levels of IEX‐1. In agreement with the putative role of IEX‐1 in macrophage function, we recently identified a crucial role of IEX‐1 in many chronic conditions, including hypertension, sepsis, insulin resistance, obesity, and arthritis,16, 20, 21, 22 revealing previously unknown functions of IEX‐1 in cardiovascular and metabolic diseases. In this article, we reviewed the findings in IEX‐1 knockout mice from our laboratory and others and primarily focused on the roles of IEX‐1 in cardiovascular and metabolic disorders. We also alluded to the important findings from other laboratories that are relevant for its role in cardiovascular and metabolic diseases. In addition, an attempt is made to underline the involvement of IEX‐1 in inflammation and its possible link with the lipid‐mediated metabolic diseases. However, because of the limitations and scope of this article, we refrained from discussing an important role of IEX‐1 in many types of cancers that has been widely reported before.
Clinical outcomes in hypertensive patients treated with a single‐pill fixed‐dose combination of renin‐angiotensin system inhibitor and thiazide diuretic

Two or more antihypertensive agents are required to achieve blood pressure control for the most hypertensive patients. However, comparison of clinical outcomes between fixed‐dose combinations (FDC) and free‐equivalent combinations of renin‐angiotensin system (RAS) inhibitor and thiazide diuretic is lacking nowadays. Patients who were newly diagnosed with hypertension between July 1st, 2008 and December 31st, 2011 and prescribed with FDC (n = 13 176) or free combinations of RAS inhibitors and thiazide diuretic (n = 4392) were identified from the National Health Insurance Research Database of Taiwan and matched in 3:1 ratio using the propensity score method. The primary end point was major adverse cardiovascular events (MACE). The secondary end points were hospitalization of heart failure, new diagnosis of chronic kidney disease, and the initiation of dialysis. Compared with he FDC group was associated with better medication adherence compared with the free combination group. FDC of RAS inhibitor and thiazide diuretic reduced MACE (hazard ratio [HR]: 0.85; 95% confidence interval [CI]: 0.74‐0.97; P = 0.017), hospitalization for heart failure and initiation of dialysis compared with the free combination regimens. The outcome benefits of FDC was mainly driven by reduced cardiovascular and renal events in the patients with proportion of days covered <80%. In this retrospective claims database analysis, compared with the free combination regimens, the use of FDC of RAS inhibitor and thiazide diuretic was associated with improved medication compliance and clinical outcomes in the management of hypertension, particularly in the patients with poor medication adherence.
Evaluating Text GANs as Language Models

Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of “exposure bias”. However, A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric. In this work, we propose to approximate the distribution of text generated by a GAN, which permits evaluating them with traditional probability-based LM metrics. We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than state-of-the-art LMs. Our evaluation procedure promotes better understanding of the relation between GANs and LMs, and can accelerate progress in GAN-based text generation.
Two Independent Predictors of Nightmares in Posttraumatic Stress Disorder.

STUDY OBJECTIVES Understanding nightmares (NM) and disturbing dreams (DD) in posttraumatic stress disorder (PTSD) has been limited by the unpredictability of these events and their nonappearance in the sleep laboratory. This study used intensive, longitudinal, ambulatory methods to predict morning reports of NM/DD in veterans in whom chronic, severe PTSD was diagnosed. METHODS Participants were 31 male United States military veterans engaged in residential treatment for PTSD and participating in a service animal training intervention. Participants slept on mattress actigraphs and provided reports of momentary mood, as well as morning NM/DD reports, for up to 6 weeks. Mattress actigraphy provided sleep-period heart rate and respiratory sinus arrhythmia (RSA), and an actigraphic estimate of sleep efficiency. On one night, a respiratory event index (REI) was obtained using an ambulatory system. RESULTS A total of 468 morning reports were obtained, of which 282 endorsed NM/DD during the prior night, and 186 did not. After accounting for multiple predictors, only elevated REI and lower prior-night sleep RSA predicted morning endorsement of NM/DD. These two predictors did not interact. CONCLUSIONS Elevated REI and lower sleep period RSA were independently predictive of NM/DD. The former result is consistent with studies showing that sleep-disordered breathing (SDB) is a factor in NM/DD, and that continuous positive airway pressure (CPAP) can reduce these symptoms in patients with comorbid PTSD and SDB. The latter result implicates dysregulated arousal modulation during sleep in trauma-related NM/DD. It is consistent with findings that NM/DD are reported in patients without SDB and can persist in patients with comorbid PTSD and SDB even when CPAP successfully remediates SDB.
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space

Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hybrid space by discretization, or relax it into a continuous set. In this paper, we propose a parametrized deep Q-network (P- DQN) framework for the hybrid action space without approximation or relaxation. Our algorithm combines the spirits of both DQN (dealing with discrete action space) and DDPG (dealing with continuous action space) by seamlessly integrating them. Empirical results on a simulation example, scoring a goal in simulated RoboCup soccer and the solo mode in game King of Glory (KOG) validate the efficiency and effectiveness of our method.
Identifying Recurrent Malignant Glioma after Treatment Using Amide Proton Transfer-Weighted MR Imaging: A Validation Study with Image-Guided Stereotactic Biopsy

Purpose: To quantify the accuracy of amide proton transfer-weighted (APTw) MRI for identifying active glioma after treatment via radiographically guided stereotactic tissue validation. Experimental Design: Twenty-one patients who were referred for surgery for MRI features concerning for tumor progression versus treatment effect underwent preoperative APTw imaging. Stereotactic biopsy samples were taken from regions of interest with varying APTw signal intensities. The relationship between final clinical pathology and the histopathology of each of the 64 specimens was analyzed relative to APTw results. Analysis of confirmed recurrent tumor or treatment effect tissue was used to perform ROC analysis. Results: Eighteen of 21 patients had recurrent tumor, and 3 had treatment effect on clinical pathology. In 12 patients, there were multiple histopathologic assignments confirmed within the same tumor. Of the 64 total specimens, 20 specimens were active glioma, 27 mixed active and quiescent glioma, and 17 quiescent/no identifiable tumor. APTw signal intensity and histopathologic assignment, cellularity, and proliferation index had significant positive correlations (R = 0.651, 0.580, and 0.458, respectively; all P < 0.001). ROC analysis with a 1.79% APTw intensity cutoff differentiated active from nonactive tumor (AUC of 0.881) with 85.1% sensitivity and 94.1% specificity. Analysis of clinical pathology showed the mean APTw intensity for each patient had 94.4% sensitivity and 100% positive predictive value for identifying recurrent glioma at this cutoff. Conclusions: APTw imaging hyperintensity may be a marker of active malignant glioma. It is able to distinguish between regions of heterogeneous abnormality on anatomic brain MRI with high sensitivity and specificity.
Tangible 3D Printed Workshop for introducing Art and Creativity in Engineering Drawing Subject

In studies of engineering, creative competence is becoming more and more important. In order to develop this ability, a three-dimensional modelling workshop called STELLA 3D was designed in collaboration with teachers from fine arts studies, based on the pictorial work of Frank Stella. In this article, a tangible version of this workshop is presented, printed in 3D, which was tested during the 2016-2017 academic year by 31 students in the First Year of Engineering at the University of La Laguna. The data obtained from the experimental group show that the students have increased their creativity values in comparison with the Pre-test, going from a score of 93,8 to 132,0. The resource developed in this work includes QR codes, which enable digital files of the pieces for 3D printing to be downloaded, and makes it possible to replace them in case of loss or breakage.