Miracle-dz/UniFAR-FTUs huggingface dataset:
Parallel Excitatory and Inhibitory Neural Circuit Pathways Underlie Reward-Based Phasic Neural Responses

Phasic activity of dopaminergic (DA) neurons in the ventral tegmental area or substantia nigra compacta (VTA/SNc) has been suggested to encode reward-prediction error signal for reinforcement learning. Recent studies have shown that the lateral habenula (LHb) neurons exhibit a similar response, but for nonrewarding or punishment signals. Hence, the transient signaling role of LHb neurons is opposite that of DA neurons and also that of several other brain nuclei such as the border region of the globus pallidus internal segment (GPb) and the rostral medial tegmentum (RMTg). Previous theoretical models have investigated the neural circuit mechanism underlying reward-based phasic activity of DA neurons, but the feasibility of a larger neural circuit model to account for the observed reward-based phasic activity in other brain nuclei such as the LHb has yet to be shown. Here, we propose a large-scale neural circuit model and show that parallel excitatory and inhibitory pathways underlie the learned neural responses across multiple brain regions. Specifically, the model can account for the phasic neural activity observed in the GPb, LHb, RMTg, and VTA/SNc. Based on sensitivity analysis, the model is found to be robust against changes in the overall neural connectivity strength. The model also predicts that striosomes play a key role in the phasic activity of VTA/SNc and LHb neurons by encoding previous and expected rewards. Taken together, our model identifies the important role of parallel neural circuit pathways in accounting for phasic activity across multiple brain areas during reward and punishment processing.
Increasing Staphylococcus Species Resistance in Peritoneal Dialysis-Related Peritonitis over a 10-Year Period in a Single Taiwanese Center

Background: Peritonitis is a major complication of peritoneal dialysis (PD). Staphylococcus species are gram-positive bacteria that are most commonly associated with peritoneal peritonitis. The increasing antimicrobial resistance rate is a severe burden when considering the initial choice of antibiotics. This investigation examined the trends of staphylococcal infection as well as the resistance rate and clinical outcomes from 2006 to 2015 in southern Taiwan. Methods: We retrospectively investigated all PD-related peritonitis episodes in southern Taiwan between January 2006 and December 2015 and evaluated the clinical characteristics of peritonitis, microbiological prevalence and resistance of Staphylococcus species, and outcomes in patients. Results: Among 244 episodes of peritonitis, Staphylococcus species accounted for approximately 65% of the gram-positive bacteria that caused the infection. The methicillin resistance rate among Staphylococcus species substantially increased to 64% by 2015 in both Staphylococcus aureus and coagulase-negative staphylococci in southern Taiwan. Notably, patients with methicillin-resistant staphylococcal infection exhibited a significantly higher hospitalization rate than those with methicillin-sensitive staphylococcal infection. However, the catheter removal rate and transfer to hemodialysis exhibited no differences between the 2 groups. Conclusion: Peritonitis is the most serious complication in patients on PD, and microbiological trends have changed over the past 10 years at a single center in southern Taiwan. The number of methicillin-resistant Staphylococcus species has substantially increased. Empirical initial antibiotic therapy should be adapted on the basis of the growing microbiological resistance.
Two New Chromone Glycosides from the Roots of Saposhnikovia divaricata

Five chromone glycosides were isolated from the water‐soluble portions of 70% EtOH extract of the roots of Saposhnikovia divaricata, including two new chromone glycosides 1 and 2. The structures of the chromone glycosides were identified as (3′S)‐3′‐O‐β‐d‐apiofuranosyl‐(1 → 6)‐β‐d‐glucopyranosylhamaudol (1), (2′S)‐4′‐O‐β‐d‐apiofuranosyl‐(1 → 6)‐β‐d‐glucopyranosylvisamminol (2), 3′‐O‐glucopyranosylhamaudol (3), 4′‐O‐β‐d‐glucopyranosylvisamminol (4), and 4′‐O‐β‐d‐glucopyranosyl‐5‐O‐methylvisamminol (5) on the basis of extensive spectroscopic methods, and the absolute configurations of the new compounds were elucidated by the electronic circular dichroism (ECD) calculation and acid hydrolysis. The cytotoxic activities of the glycosides 1 – 5 against three human cancer cell lines (PC‐3, SK‐OV‐3, and H460) were evaluated. The result showed that compounds 1 – 5 had weak cytotoxic activities against the human cancer cell lines with IC50 values in the range of 48.54 ± 0.80 – 94.25 ± 1.45 μm.
Listen, Think and Listen Again: Capturing Top-down Auditory Attention for Speaker-independent Speech Separation

Recent deep learning methods have made significant progress in multi-talker mixed speech separation. However, most existing models adopt a driftless strategy to separate all the speech channels rather than selectively attend the target one. As a result, those frameworks may be failed to offer a satisfactory solution in complex auditory scene where the number of input sounds is usually uncertain and even dynamic. In this paper, we present a novel neural network based structure motivated by the top-down attention behavior of human when facing complicated acoustical scene. Different from previous works, our method constructs an inference-attention structure to predict interested candidates and extract each speech channel of them. Our work gets rid of the limitation that the number of channels must be given or the high computation complexity for label permutation problem. We evaluated our model on the WSJ0 mixed-speech tasks. In all the experiments, our model gets highly competitive to reach and even outperform the baselines.
Evaluation of implicit associations between back posture and safety of bending and lifting in people without pain

Abstract Background and aims Despite lack of support from recent in vivo studies, bending and lifting (especially with a round-back posture) are perceived as dangerous to the back. In light of this view, it has been proposed that pain-free people may hold a common implicit belief that is congruent with the idea that bending and lifting with a round-back represents danger to a person’s back, however this has not been evaluated. The aims of this study were: (1) to evaluate implicit associations between back posture and safety related to bending and lifting in pain-free people; (2) to explore correlations between the implicit measure and explicit measures of back beliefs, fear of movement and safety of bending; (3) to investigate self-reported qualitative appraisal of safe lifting. Methods Exploratory cross-sectional study including 67 pain-free participants (no pain, or average pain ≤3/10 for less than one week over the previous 12 months) (52% male), who completed an online survey containing demographic data and self-reported measures of: fear of movement (Tampa Scale for Kinesiophobia for General population – TSK-G), back beliefs (Back Pain Attitudes Questionnaire BackPAQ), and bending beliefs (Bending Safety Belief – BSB – a pictorial scale with images of a person bending/lifting with round and straight back postures). Implicit associations between back posture and safety related to bending and lifting were evaluated with the Implicit Association Test (IAT). A qualitative assessment of descriptions of safe lifting was performed. Results An implicit association between “danger” and “round-back” bending/lifting was evident in all participants (IATD-score=0.65 (SD=0.45; 95% CI [0.54, 0.76]). Participants’ profile indicated high fear of movement, unhelpful back beliefs, and perceived danger to round-back bending and lifting (BSBThermometer: 5.2 (SD=3.8; 95% CI [4.26, 6.13] range −10 to 10; t(67)=11.09, p<0.001). There was a moderate correlation between IAT and BSBThermometer (r=0.38, 95% CI [0.16, 0.62]). There were weaker and non-statistically significant correlations between IAT and TSK-G (r=0.28, 95% CI [−0.02, 0.47]), and between IAT and BackPAQDanger (r=0.21, 95% CI [−0.03, 0.45]). Qualitative assessment of safe lifting descriptions indicated that keeping a “straight back” and “squatting” when lifting were the most common themes. Conclusions Pain-free people displayed an implicit bias towards bending and lifting with a “round-back” as dangerous. Our findings support the idea that pain-free people may have a pre-existing belief about lifting, that the back is in danger when rounded. Research to evaluate the relationship between this implicit bias and lifting behaviour is indicated. Implications The findings of this study may have implications for ergonomic guidelines and public health information related to bending and lifting back postures. Additionally, clinicians may need to be aware of this common belief, as this may be reflected in how a person responds when they experience pain.
Epigenetics of addiction

Chronic opiate use represses expression of Brain-derived neurotrophic factor through changes in histone modifications. Brain-derived neurotrophic factor (BDNF) mediates changes in neural plasticity induced by opiates, such as morphine, and stimulants, such as cocaine. One of the neural circuits affected by both opiates and stimulants is the brain's mesolimbic dopamine system, which consists of the ventral tegmental area (VTA) and its contacts with the nucleus accumbens (NAc). In the mouse, cocaine induces BDNF signaling and neural plasticity in the VTA-NAc circuit, whereas opiates, such as morphine, repress Bdnf expression in the VTA. Koo et al. found that, compared with controls, the abundance of Bdnf transcripts was reduced in VTA samples from human heroin addicts, from heroin self-administering rats, and from rats and mice treated chronically with morphine. The authors used quantitative chromatin immunoprecipitation (qChIP) analysis to compare histone modifications and transcription factor binding at the Bdnf locus in VTA cells from chronically morphine-treated and control rodents. Chronic morphine treatment caused RNA polymerase II to stall in the coding region of Bdnf and induced various changes in histone modifications at the Bdnf promoter, including an increase in the abundance of histone 3 trimethylated at Lys4 (H3K4me3). The abundance of H3K4me3 also increased in the promoter of Nuclear receptor–related 1 (Nurr1), which encodes a transcription factor that promotes Bdnf expression. Both Nurr1 and Bdnf are targets of the cAMP response element–binding protein (CREB), and the increased abundance of H3K4me3 correlated with reduced binding of activated CREB to these promoters in vivo. Furthermore, overexpression of the enzyme that catalyzes the H3K4me3 modification reduced activated CREB binding to the Bdnf promoter in the absence of morphine treatment. Overexpressing NURR1 in the VTA induced Bdnf expression, blocked morphine reward, and rescued a hyperlocomotive phenotype caused by morphine readministration after withdrawal. These findings highlight the complexity of epigenetic control of neural plasticity, though the mechanisms by which morphine induces histone modifications remain unidentified. J. W. Koo, M. S. Mazei-Robison, Q. LaPlant, G. Egervari, K. M. Braunscheidel, D. N. Adank, D. Ferguson, J. Feng, H. Sun, K. N. Scobie, D. M. Damez-Werno, E. Ribeiro, C. J. Peña, D. Walker, R. C. Bagot, M. E. Cahill, S. A. R. Anderson, B. Labonté, G. E. Hodes, H. Browne, B. Chadwick, A. J. Robison, V. F. Vialou, C. Dias, Z. Lorsch, E. Mouzon, M. K. Lobo, D. M. Dietz, S. J. Russo, R. L. Neve, Y. L. Hurd, E. J. Nestler, Epigenetic basis of opiate suppression of Bdnf gene expression in the ventral tegmental area. Nat. Neurosci. 18, 415–422 (2015). [PubMed]
Cross-Modality Person Re-Identification with Generative Adversarial Training

Person re-identification (Re-ID) is an important task in video surveillance which automatically searches and identifies people across different cameras. Despite the extensive Re-ID progress in RGB cameras, few works have studied the Re-ID between infrared and RGB images, which is essentially a cross-modality problem and widely encountered in real-world scenarios. The key challenge lies in two folds, i.e., the lack of discriminative information to re-identify the same person between RGB and infrared modalities, and the difficulty to learn a robust metric towards such a large-scale cross-modality retrieval. In this paper, we tackle the above two challenges by proposing a novel cross-modality generative adversarial network (termed cmGAN). To handle the issue of insufficient discriminative information, we leverage the cutting-edge generative adversarial training to design our own discriminator to learn discriminative feature representation from different modalities. To handle the issue of large-scale cross-modality metric learning, we integrates both identification loss and cross-modality triplet loss, which minimize inter-class ambiguity while maximizing cross-modality similarity among instances. The entire cmGAN can be trained in an end-to-end manner by using standard deep neural network framework. We have quantized the performance of our work in the newly-released SYSU RGB-IR Re-ID benchmark, and have reported superior performance, i.e., Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP), over the state-of-the-art works [Wu et al., 2017], respectively.
The Effects of Sodium Phosphate Supplementation on Physiological Responses to Submaximal Exercise and 20 km Cycling Time-Trial Performance

ABSTRACT The aim of this study was to examine the effects of sodium phosphate (SP) supplementation on physiological responses to submaximal exercise and 20 km cycling time-trial performance. Using a randomized, double-blind, crossover design, 20 endurance-trained male cyclists (age: 31 ± 6 years; height: 1.82 ± 0.07 m; body mass: 76.3 ± 7.0 kg; maximal oxygen uptake [V̇O2max]: 57.9 ± 5.5 mL·kg−1·min−1) completed two supplementation trials separated by a 14-day washout period. The trials consisted of 10 minutes of cycling at 65% V̇O2max followed by a 20 km time trial. Expired air was monitored throughout each trial for the evaluation of V̇O2, minute ventilation (V̇E), and respiratory exchange ratio (RER). Heart rate was monitored during each trial along with ratings of perceived exertion (RPE) and blood lactate concentration. For four days before each trial, participants ingested 50 mg·kg fat-free mass−1·day−1 of either SP or placebo. There were no effects (p ≥ .05) of supplementation on physiological responses during cycling at 65% V̇O2max. There were also no effects of supplementation on time-trial performance (placebo: 32.8 ± 2.2 min; SP: 32.8 ± 2.3 min). Nevertheless, relative to placebo, SP increased V̇E (mean difference: 3.81 L·min−1; 95% confidence interval: [0.16, 7.46 L·min−1]), RER (mean difference: 0.020; 95% confidence interval: [0.004, 0.036]), and RPE (mean difference: 0.39; 95% confidence interval: [0.04, 0.73]) during time trials, as well as post time-trial blood lactate concentration (mean difference: 1.06 mmol·L−1; 95% confidence interval: [0.31, 1.80 mmol·L−1]). In conclusion, SP supplementation has no significant effects on submaximal physiological responses or 20 km time-trial performance.
Densely Cascaded Shadow Detection Network via Deeply Supervised Parallel Fusion

Shadow detection is an important and challenging problem in computer vision. Recently, single image shadow detection had achieved major progress with the development of deep convolutional networks. However, existing methods are still vulnerable to background clutters, and often fail to capture the global context of an input image. These global contextual and semantic cues are essential for accurately localizing the shadow regions. Moreover, rich spatial details are required to segment shadow regions with precise shape. To this end, this paper presents a novel model characterized by a deeply supervised parallel fusion (DSPF) network and a densely cascaded learning scheme. The DSPF network achieves a comprehensive fusion of global semantic cues and local spatial details by multiple stacked parallel fusion branches, which are learned in a deeply supervised manner. Moreover, the densely cascaded learning scheme is employed to refine the spatial details. Our method is evaluated on two widely used shadow detection benchmarks. Experimental results show that our method outperforms state-of-the-arts by a large margin.
Physical Frailty and Amyloid-β Deposits in the Brains of Older Adults with Cognitive Frailty

Background: Cognitive frailty and impairment are phenotypically and pathophysiologically correlated with physical frailty. We examined associations between accumulation of amyloid-β in the brain as a brain imaging biomarker and phenotypes of physical frailty (weight loss, weakness, exhaustion, slowness, low physical activity) in older adults with mild cognitive impairment (MCI) and cognitive frailty. Methods: Cross-sectional associations between brain amyloid-β accumulation measured with 11C-Pittsburgh compound B (PiB)-positron emission tomography (PET) and physical frailty were examined in 48 elderly participants (mean age: 75.1 ± 6.6 years; 73% female). Cortical and regional standard uptake value ratios (SUVRs) were obtained. Main outcome measures included frailty phenotypes and physical functions (gait speed, short physical performance battery, and Timed Up and Go tests). Results: Mean cortical region of interest and regional SUVRs (frontal cortex, temporal cortex, parietal cortex, precuneus/posterior cingulate cortex (PC/PCC), hippocampus, basal ganglia, and global SUVR) were associated with gait speed, Timed Up and Go, and short physical performance battery (PC/PCC, basal ganglia). In addition, SUVRs of all brain regions were significantly linked to weakness. Conclusion: SUVRs of all brain regions revealed an association between brain amyloid-β accumulation and weakness. Furthermore, global SUVRs (frontal cortex, temporal cortex, parietal cortex, PC/PCC, hippocampus, basal ganglia) were associated with gait parameters.
Trajectory-User Linking via Variational AutoEncoder

Trajectory-User Linking (TUL) is an essential task in Geo-tagged social media (GTSM) applications, enabling personalized Point of Interest (POI) recommendation and activity identification. Existing works on mining mobility patterns often model trajectories using Markov Chains (MC) or recurrent neural networks (RNN) -- either assuming independence between non-adjacent locations or following a shallow generation process. However, most of them ignore the fact that human trajectories are often sparse, high-dimensional and may contain embedded hierarchical structures. We tackle the TUL problem with a semi-supervised learning framework, called TULVAE (TUL via Variational AutoEncoder), which learns the human mobility in a neural generative architecture with stochastic latent variables that span hidden states in RNN. TULVAE alleviates the data sparsity problem by leveraging large-scale unlabeled data and represents the hierarchical and structural semantics of trajectories with high-dimensional latent variables. Our experiments demonstrate that TULVAE improves efficiency and linking performance in real GTSM datasets, in comparison to existing methods.
No unexpected CRISPR-Cas9 off-target activity revealed by trio sequencing of gene-edited mice

CRISPR-Cas9 technologies have transformed genome-editing of experimental organisms and have immense therapeutic potential. Despite significant advances in our understanding of the CRISPR-Cas9 system, concerns remain over the potential for off-target effects. Recent studies have addressed these concerns using whole-genome sequencing (WGS) of gene-edited embryos or animals to search for de novo mutations (DNMs), which may represent candidate changes introduced by poor editing fidelity. Critically, these studies used strain-matched, but not pedigree-matched controls and thus were unable to reliably distinguish generational or colony-related differences from true DNMs. Here we used a trio design and whole genome sequenced 8 parents and 19 embryos, where 10 of the embryos were mutagenised with well-characterised gRNAs targeting the coat colour Tyrosinase (Tyr) locus. Detailed analyses of these whole genome data allowed us to conclude that if CRISPR mutagenesis were causing SNV or indel off-target mutations in treated embryos, then the number of these mutations is not statistically distinguishable from the background rate of DNMs occurring due to other processes.
Unsupervised Learning based Jump-Diffusion Process for Object Tracking in Video Surveillance

This paper presents a principled way for dealing with occlusions in visual tracking which is a long-standing issue in computer vision but largely remains unsolved. As the major innovation, we develop a learning-based jump-diffusion process to jointly track object locations and estimate their visibility statuses over time. Our method employs in particular a set of jump dynamics to change object's visibility statuses and a set of diffusion dynamics to track objects in videos. Different from the traditional jump-diffusion process that stochastically generates dynamics, we utilize deep policy functions to determine the best dynamic at the present step and learn the optimal policies from raw videos using reinforcement learning methods.Our method is capable of tracking objects with severe occlusions in crowded scenes and thus recovers the complete trajectories of objects that undergo multiple interactions with others. We evaluate the proposed method on challenging video sequences and compare it to alternative methods. Significant improvements are obtained particularly for the videos including frequent interactions or occlusions.
Robust Face Sketch Synthesis via Generative Adversarial Fusion of Priors and Parametric Sigmoid

Despite the extensive progress in face sketch synthesis, existing methods are mostly workable under constrained conditions, such as fixed illumination, pose, background and ethnic origin that are hardly to control in real-world scenarios. The key issue lies in the difficulty to use data under fixed conditions to train a model against imaging variations. In this paper, we propose a novel generative adversarial network termed pGAN, which can generate face sketches efficiently using training data under fixed conditions and handle the aforementioned uncontrolled conditions. In pGAN, we embed key photo priors into the process of synthesis and design a parametric sigmoid activation function for compensating illumination variations. Compared to the existing methods, we quantitatively demonstrate that the proposed method can work well on face photos in the wild.
Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation

Sequence to sequence (Seq2Seq) approach has gained great attention in the field of single-turn dialogue generation. However, one serious problem is that most existing Seq2Seq based models tend to generate common responses lacking specific meanings. Our analysis show that the underlying reason is that Seq2Seq is equivalent to optimizing Kullback–Leibler (KL) divergence, thus does not penalize the case whose generated probability is high while the true probability is low. However, the true probability is unknown, which poses challenges for tackling this problem. Inspired by the fact that the coherence (i.e. similarity) between post and response is consistent with human evaluation, we hypothesize that the true probability of a response is proportional to the coherence degree. The coherence scores are then used as the reward function in a reinforcement learning framework to penalize the case whose generated probability is high while the true probability is low. Three different types of coherence models, including an unlearned similarity function, a pretrained semantic matching function, and an end-to-end dual learning architecture, are proposed in this paper. Experimental results on both Chinese Weibo dataset and English Subtitle dataset show that the proposed models produce more specific and meaningful responses, yielding better performances against Seq2Seq models in terms of both metric-based and human evaluations.