Miracle-dz/UniFAR-FTUs huggingface dataset:
Early gonadotropin-releasing hormone antagonist protocol in women with polycystic ovary syndrome: A preliminary randomized trial

Objective To prospectively evaluate the efficacy and safety of a fixed early gonadotropin-releasing hormone (GnRH) antagonist protocol compared to a conventional midfollicular GnRH antagonist protocol and a long GnRH agonist protocol for in vitro fertilization (IVF) in patients with polycystic ovary syndrome (PCOS). Methods Randomized patients in all three groups (early antagonist, n=14; conventional antagonist, n=11; long agonist, n=11) received 21 days of oral contraceptive pill treatment prior to stimulation. The GnRH antagonist was initiated on the 1st day of stimulation in the early antagonist group and on the 6th day in the conventional antagonist group. The GnRH agonist was initiated on the 18th day of the preceding cycle. The primary endpoint was the number of oocytes retrieved, and the secondary endpoints included the rate of moderate-to-severe ovarian hyperstimulation syndrome (OHSS) and the clinical pregnancy rate. Results The median total number of oocytes was similar among the three groups (early, 16; conventional, 12; agonist, 19; p=0.111). The early GnRH antagonist protocol showed statistically non-significant associations with a higher clinical pregnancy rate (early, 50.0%; conventional, 11.1%; agonist, 22.2%; p=0.180) and lower incidence of moderate-to-severe OHSS (early, 7.7%; conventional, 18.2%; agonist, 27.3%; p=0.463), especially among subjects at high risk for OHSS (early, 12.5%; conventional, 40.0%; agonist, 50.0%; p=0.324). Conclusion In PCOS patients undergoing IVF, early administration of a GnRH antagonist may possibly lead to benefits due to a reduced incidence of moderate-to-severe OHSS in high-risk subjects with a better clinical pregnancy rate per embryo transfer. Further studies with more subjects are required.
Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

Spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy-efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.
The Laughter Prescription

Laughter is a normal and natural physiologic response to certain stimuli with widely acknowledged psychological benefits. However, current research is beginning to show that laughter may also have serious positive physiological effects for those who engage in it on a regular basis. Providers who prescribe laughter to their patients in a structured way may be able to use these natural, free, and easily distributable positive benefits. This article reviews the current medical understanding of laughter’s physiologic effects and makes a recommendation for how physicians might best harness this natural modality for their patients.
Resveratrol Improves the Energy Sensing and Glycolipid Metabolism of Blunt Snout Bream Megalobrama amblycephala Fed High-Carbohydrate Diets by Activating the AMPK–SIRT1–PGC-1α Network

This study investigated the effects of resveratrol on the growth performance, energy sensing, glycolipid metabolism and glucose and insulin load of blunt snout bream Megalobrama amblycephala fed high-carbohydrate diets. Fish (39.44 ± 0.06 g) were randomly fed three diets: a control diet (30% carbohydrate), a high-carbohydrate diet (HC, 41% carbohydrate), and the HC diet supplemented with 0.04% resveratrol (HCR) for 12 weeks. Fish fed the HC diet had significantly high values of nitrogen and energy retention efficiency, hepatosomatic index, intraperitoneal fat ratio, whole-body lipid content and intraperitoneal fat glycogen and lipid contents compared to the control group, but showed little difference with the HCR treatment. Liver and muscle lipid contents and plasma levels of glucose, glycated serum protein, advanced glycation end products and total cholesterol of fish fed the HC diet were significantly higher than those of the control group, whereas the opposite was found with resveratrol supplementation. Fish fed the HC diet obtained significantly low values of plasma insulin levels and hepatic adenosine monophosphate (AMP) contents and NAD+/NADH ratio compared to HCR treatment, but showed little difference with the control group. The opposite was found for hepatic adenosine triphosphate (ATP) contents and the ATP/AMP ratio. In addition, fish fed the HC diet showed significantly high transcriptions of glucose transporter 2 (GLUT2), glucose-6-phosphate dehydrogenase, glycogen synthase, fatty acid synthetase (FAS), acetyl-CoA carboxylase α (ACCα), peroxisome proliferator-activated receptor γ and PPARα compared to the control group, whereas the opposite was found for protein levels of AMP-activated protein kinase α (t-AMPKα), phosphorylated AMP-activated protein kinase α (p-AMPKα), sirtuin-1 (SIRT1), and p-AMPKα/t-AMPKα ratio as well as the transcriptions of AMPKα1, AMPKα2, SIRT1, PPARγ coactivator-1α (PGC-1α), phosphoenolpyruvate carboxykinase, fructose-1,6-bisphosphatase (FBPase), glucose-6-phosphatase, carnitine palmitoyl transferase I (CPT I) and acyl-CoA oxidase. Resveratrol supplementation significantly up-regulated the protein levels of t-AMPK, p-AMPK, and SIRT1, p-AMPK/t-AMPK ratio as well as the transcriptions of AMPKα1, AMPKα2, SIRT1, PGC-1α, GLUT2, FBPase, and CPT I compared to HC group, while the opposite was found for sterol regulatory element-binding protein-1, FAS and ACCα. Furthermore, resveratrol improved glucose and insulin tolerance of fish fed the HC diet after glucose and insulin load.
Flood Detection in Gaofen-3 SAR Images via Fully Convolutional Networks

Emergency flood monitoring and rescue need to first detect flood areas. This paper provides a fast and novel flood detection method and applies it to Gaofen-3 SAR images. The fully convolutional network (FCN), a variant of VGG16, is utilized for flood mapping in this paper. Considering the requirement of flood detection, we fine-tune the model to get higher accuracy results with shorter training time and fewer training samples. Compared with state-of-the-art methods, our proposed algorithm not only gives robust and accurate detection results but also significantly reduces the detection time.
Multitask Learning for Query Segmentation in Job Search

In this paper, we present the first attempt to use multitask learning for query segmentation. We use the semantic category of the words as an auxiliary task and show that segmentation improves when the model is also trained to predict the semantic category of the query terms, outperforming benchmark methods over a novel dataset from a popular job search engine. Our further experiments show that the task of modeling the query term semantics performs better as a standalone task, without adding segmentation as an auxiliary task.
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines

Learning strategies for imperfect information games from samples of interaction is a challenging problem. A common method for this setting, Monte Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term convergence rates due to high variance. In this paper, we introduce a variance reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR. Using this technique, periteration estimated values and updates are reformulated as a function of sampled values and state-action baselines, similar to their use in policy gradient reinforcement learning. The new formulation allows estimates to be bootstrapped from other estimates within the same episode, propagating the benefits of baselines along the sampled trajectory; the estimates remain unbiased even when bootstrapping from other estimates. Finally, we show that given a perfect baseline, the variance of the value estimates can be reduced to zero. Experimental evaluation shows that VR-MCCFR brings an order of magnitude speedup, while the empirical variance decreases by three orders of magnitude. The decreased variance allows for the first time CFR+ to be used with sampling, increasing the speedup to two orders of magnitude.
Relationships of Hyperhomocysteinemia and Hyperuricemia With Metabolic Syndrome and Renal Function in Chinese Centenarians

As the first time worldwide, this study aimed to investigate the relationships of hyperhomocysteinemia and hyperuricemia with metabolic syndrome (MetS) and renal function in Chinese centenarians. The China Hainan Centenarian Cohort Study was performed in 18 cities and counties of the Hainan Province. Home interview, physical examination, and blood analysis were performed on 808 centenarians following standard procedures. All centenarians had a median age of 102 (100–115) years. Prevalence of hyperhomocysteinemia and hyperuricemia was 91.6% (740 centenarians) and 28.5% (230 centenarians), respectively. The MetS was present in 117 centenarians (14.5%). In simple correlation analyses, hyperhomocysteinemia and hyperuricemia were significantly correlated with MetS and glomerular filtration rate (GFR) < 60 ml/min/1.73 m2 (P < 0.05 for all). Serum homocysteine levels were significantly correlated with GFR, waist circumference (WC), and triglyceride levels, while serum uric acid levels were significantly correlated with these variables plus high-density lipoprotein cholesterol (HDL-C) levels (P < 0.05 for all). In logistic regression analyses, hyperhomocysteinemia and hyperuricemia were significantly associated with MetS and GFR < 60 ml/min/1.73 m2 (P < 0.05 for all). In linear regression analyses, serum homocysteine levels were significantly associated with GFR, WC, and triglyceride, while serum uric acid levels were significantly associated with these variables plus HDL-C (P < 0.05 for all). Both hyperhomocysteinemia and hyperuricemia had important relationships with MetS and renal function in Chinese centenarians. Hyperuricemia and hyperhomocysteinemia that could help identify, while also affecting, the development of MetS and renal function may unfold complex relationships between MetS, renal function, and cardiovascular risk and provide effective prevention strategies for these conditions.
Streams and Tables: Two Sides of the Same Coin

Stream processing has emerged as a paradigm for applications that require low-latency evaluation of operators over unbounded sequences of data. Defining the semantics of stream processing is challenging in the presence of distributed data sources. The physical and logical order of data in a stream may become inconsistent in such a setting. Existing models either neglect these inconsistencies or handle them by means of data buffering and reordering techniques, thereby compromising processing latency. In this paper, we introduce the Dual Streaming Model to reason about physical and logical order in data stream processing. This model presents the result of an operator as a stream of successive updates, which induces a duality of results and streams. As such, it provides a natural way to cope with inconsistencies between the physical and logical order of streaming data in a continuous manner, without explicit buffering and reordering. We further discuss the trade-offs and challenges faced when implementing this model in terms of correctness, latency, and processing cost. A case study based on Apache Kafka illustrates the effectiveness of our model in the light of real-world requirements.
Learning Multiple Kernel Metrics for Iterative Person Re-Identification

In person re-identification most metric learning methods learn from training data only once, and then they are deployed for testing. Although impressive performance has been achieved, the discriminative information from successfully identified test samples are ignored. In this work, we present a novel re-identification framework termed Iterative Multiple Kernel Metric Learning (IMKML). Specifically, there are two main modules in IMKML. In the first module, multiple metrics are learned via a new derived Kernel Marginal Nullspace Learning (KMNL) algorithm. Taking advantage of learning a discriminative nullspace from neighborhood manifold, KMNL can well tackle the Small Sample Size (SSS) problem in re-identification distance metric learning. The second module is to construct a pseudo training set by performing re-identification on the testing set. The pseudo training set, which consists of the test image pairs that are highly probable correct matches, is then inserted into the labeled training set to retrain the metrics. By iteratively alternating between the two modules, many more samples will be involved for training and significant performance gains can be achieved. Experiments on four challenging datasets, including VIPeR, PRID450S, CUHK01, and Market-1501, show that the proposed method performs favorably against the state-of-the-art approaches, especially on the lower ranks.
Assessment Through the Short Physical Performance Battery of the Functionality in Individuals With Metabolic Syndrome Exposed to Whole-Body Vibration Exercises

Physical activity is recommended in the management of individuals with metabolic syndrome (MetS), and recent studies have suggested whole-body vibration exercise (WBVe) for this population. The aim of this study was to evaluate the functionality through the Short Physical Performance Battery (SPPB) in individuals with MetS after WBVe. The SPPB evaluates the balance, the gait speed, and the lower limb strength (five-chair stand [5CS] test). Forty-four individuals with MetS were divided into WBVe (WBVeG) and control (CG) groups. The individuals of the WBVeG performed 10 sessions of WBVe in an oscillating/vibratory platform (OVP), barefoot, for 3 minutes at the peak-to-peak displacements of 2.5, 5.0, and 7.5 mm, with a resting period of 1 minute (total time: 18 minutes/session). The frequencies ranged from 5 up to 14 Hz. The individuals of the CG performed all the steps of the study, but the OVP was turned off. Before the first and after the tenth session, the individuals performed the SPPB. Significant responses were found in the WBVeG, analyzing the total score of the SPPB (P = .005), the balance test (P = .01), the gait speed (P = .006), and the 5CS test (P = .03), resulting in the improvement of the functionality of individuals with MetS.
Closely Related Fruit Fly Species Living at Different Latitudes Diverge in Their Circadian Clock Anatomy and Rhythmic Behavior

Recently, we reported differences in the expression pattern of the blue light-sensitive flavoprotein cryptochrome (CRY) and the neuropeptide pigment-dispersing factor (PDF) in the neuronal clock network of high-latitude Drosophila species, belonging to the Drosophila subgenus (virilis-repleta radiation), compared with cosmopolitan D. melanogaster flies, belonging to the Sophophora subgenus. Alterations in rhythmic patterns of activity due to these differences might have adaptive significance for colonizing high-latitude habitats and, hence, adjusting to long photoperiods. Here, we show that these differing CRY/PDF expression patterns are only present in those species of the virilis-repleta radiation that colonized high latitudes. The cosmopolitan species D. mercatorum and D. hydei have a D. melanogaster-like clock network and behavior despite belonging to the virilis-repleta radiation. Similarly, 2 species of the holotropical Zaprionus genus, more closely related to the Drosophila subgenus than to the Sophophora subgenus, retain a D. melanogaster-like clock network and rhythmic behavior. We therefore suggest that the D. melanogaster-like clock network is the “ancestral fly clock phenotype” and that alterations in the CRY/PDF clock neurochemistry have allowed some species of the virilis-repleta radiation to colonize high-latitude environments.
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

Making the right decision in traffic is a challenging task that is highly dependent on individual preferences as well as the surrounding environment. Therefore it is hard to model solely based on expert knowledge. In this work we use Deep Reinforcement Learning to learn maneuver decisions based on a compact semantic state representation. This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training. The input for the neural network is a simulated object list similar to that of Radar or Lidar sensors, superimposed by a relational semantic scene description. The state as well as the reward are extended by a behavior adaptation function and a parameterization respectively. With little expert knowledge and a set of mid-level actions, it can be seen that the agent is capable to adhere to traffic rules and learns to drive safely in a variety of situations.
Klotho and Postmenopausal Hormone Replacement Therapy in Women with Chronic Kidney Disease

Kidney function is highly susceptible to age-related changes, with chronic kidney disease (CKD) serving as an important cause of morbidity and mortality in older patients. The prevalence of CKD in Korea is higher among the elderly, relative to the general population, with the most significant increases seen following the onset of menopause. Under normal conditions, estrogen attenuates renal superoxide production and protects the kidney from oxidative damage. As estrogen levels are known to decrease by as much as 80% during menopause, this represents a significant risk for older women. Postmenopausal hormone replacement therapy (HRT) modulates the renin-angiotensin system, thereby reducing the progressive deterioration of renal function. Use of estrogen-based HRT has been shown to ameliorate renal function in postmenopausal women, and delay CKD progression. Renal expression of klotho, an important suppressor of aging, is markedly decreased in CKD patients, making it a promising candidate for use as a prognostic biomarker in CKD. Here, we review the key links between renal function, sex, age, and estrogen levels during menopause, and discuss the use of postmenopausal HRT in CKD attenuation.
Dual Attention Network for Scene Segmentation

In this paper, we address the scene segmentation task by capturing rich contextual dependencies based on the self-attention mechanism. Unlike previous works that capture contexts by multi-scale features fusion, we propose a Dual Attention Networks (DANet) to adaptively integrate local features with their global dependencies. Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively. The position attention module selectively aggregates the features at each position by a weighted sum of the features at all positions. Similar features would be related to each other regardless of their distances. Meanwhile, the channel attention module selectively emphasizes interdependent channel maps by integrating associated features among all channel maps. We sum the outputs of the two attention modules to further improve feature representation which contributes to more precise segmentation results. We achieve new state-of-the-art segmentation performance on three challenging scene segmentation datasets, i.e., Cityscapes, PASCAL Context and COCO Stuff dataset. In particular, a Mean IoU score of 81.5% on Cityscapes test set is achieved without using coarse data.
Disruption of symmetry: A quantitative assessment of facial skeleton anatomy in children born with unilateral cleft lip and palate

Unilateral cleft lip and palate (UCLP) is a craniofacial deformity characterized by lip and palate clefting on one side of the face. UCLP originates from failures in neural crest migration and differentiation during embryological development, impairing facial primordia fusion (medial nasal, maxillary, and lateral processes) resulting in clefting. Persons with UCLP experience issues in nasal breathing, speaking, and mastication. Facial directional asymmetry (DA), consisting of left–right side differences biased toward one particular side, can arise from environmental or hereditary factors. This retrospective, cross‐sectional study, quantifies DA in the facial skeleton of children with surgically repaired UCLP. We tested the hypothesis that DA is significantly increased in persons with UCLP. Twenty‐three anatomical landmark coordinates were measured from cone beam computed tomography images of two age‐ and sex‐matched samples: (1) persons with UCLP post‐surgery (n = 26, 7–17 years); (2) typical children (n = 26, 7–17 years). From these coordinates, 81 bilateral paired measurements were calculated and statistically assessed for DA differences using Principal Components Analysis and Euclidean Distance Matrix Analysis. Samples separate in high‐dimensional space and 35.8% of bilateral measures are statistically significant. Patterns of significant DA differences between samples were explored based on magnitude. Compared with the control group, in all but five cases, significant DA was greater in persons with UCLP. Regions derived from the maxillary and nasal prominences demonstrated the most DA. These results are important for medical practitioners for identifying the extent to which patients with UCLP deviate from the norm. Clin. Anat. 31:1129–1136, 2018. © 2018 Wiley Periodicals, Inc.
Are We Doing More Than We Know? Possible Mechanisms of Response to Music Therapy

Due to advances in medical knowledge the population of older adults struggling with issues of aging like Alzheimer's disease (AD), Parkinson's disease (PD), and stroke is growing. There is a need for therapeutic interventions to provide adaptive strategies to sustain quality of life, decrease neurologic impairment, and maintain or slow cognitive decline and function due to degenerative neurologic diseases. Musical interventions with adults with cognitive impairments have received increased attention over the past few years, such as the value of personalized music listening in the iPod project for AD (1); music as a tool to decrease agitation and anxiety in dementia (2); and music to aid in episodic memory (3); Rhythmic Auditory Stimulation as rehabilitation for PD (4); and recently the potential of 40 Hz sensory brain stimulation with AD and PD (5, 6). These approaches indicate the expanding scope and efficacy of music therapy and the potential mechanisms involved. This paper explicates a four-level model of mechanisms of music response (7, 8) that may help understand current music therapy approaches and treatments and help focus future research. Each level will be illustrated with research and suggestions for research directions.