Miracle-dz/UniFAR-FTUs huggingface dataset:
New Insights into the Evolutionary and Genomic Landscape of Molluscum Contagiosum Virus (MCV) based on Nine MCV1 and Six MCV2 Complete Genome Sequences

Molluscum contagiosum virus (MCV) is the sole member of the Molluscipoxvirus genus and the causative agent of molluscum contagiosum (MC), a common skin disease. Although it is an important and frequent human pathogen, its genetic landscape and evolutionary history remain largely unknown. In this study, ten novel complete MCV genome sequences of the two most common MCV genotypes were determined (five MCV1 and five MCV2 sequences) and analyzed together with all MCV complete genomes previously deposited in freely accessible sequence repositories (four MCV1 and a single MCV2). In comparison to MCV1, a higher degree of nucleotide sequence conservation was observed among MCV2 genomes. Large-scale recombination events were identified in two newly assembled MCV1 genomes and one MCV2 genome. One recombination event was located in a newly identified recombinant region of the viral genome, and all previously described recombinant regions were re-identified in at least one novel MCV genome. MCV genes comprising the identified recombinant segments have been previously associated with viral interference with host T-cell and NK-cell immune responses. In conclusion, the two most common MCV genotypes emerged along divergent evolutionary pathways from a common ancestor, and the differences in the heterogeneity of MCV1 and MCV2 populations may be attributed to the strictness of the constraints imposed by the host immune response.
Atypical longitudinal development of functional connectivity in adolescents with autism spectrum disorder

Autism spectrum disorder (ASD) is consistently associated with alterations in brain connectivity, but there are conflicting results as to where and when individuals with ASD display increased or reduced functional connectivity. Such inconsistent findings may be driven by atypical neurodevelopmental trajectories in ASD during adolescence, but no longitudinal studies to date have investigated this hypothesis. We thus examined the functional connectivity of three neurocognitive resting‐state networks—the default mode network (DMN), salience network, and central executive network (CEN)—in a longitudinal sample of youth with ASD (n = 16) and without ASD (n = 22) studied during early/mid‐ and late adolescence. Functional connectivity between the CEN and the DMN displayed significantly altered developmental trajectories in ASD: typically developing (TD) controls—but not youth with ASD—exhibited an increase in negative functional connectivity between these two networks with age. This significant interaction was due to the ASD group displaying less negative functional connectivity than the TD group during late adolescence only, with no significant group differences in early/mid‐adolescence. These preliminary findings suggest a localized age‐dependency of functional connectivity alterations in ASD and underscore the importance of considering age when examining brain connectivity. Autism Research 2019, 12: 53–65. © 2018 International Society for Autism Research, Wiley Periodicals, Inc.
Learning Two-layer Neural Networks with Symmetric Inputs

We give a new algorithm for learning a two-layer neural network under a general class of input distributions. Assuming there is a ground-truth two-layer network $$ y = A \sigma(Wx) + \xi, $$ where $A,W$ are weight matrices, $\xi$ represents noise, and the number of neurons in the hidden layer is no larger than the input or output, our algorithm is guaranteed to recover the parameters $A,W$ of the ground-truth network. The only requirement on the input $x$ is that it is symmetric, which still allows highly complicated and structured input. Our algorithm is based on the method-of-moments framework and extends several results in tensor decompositions. We use spectral algorithms to avoid the complicated non-convex optimization in learning neural networks. Experiments show that our algorithm can robustly learn the ground-truth neural network with a small number of samples for many symmetric input distributions.
Visual Semantic Navigation using Scene Priors

How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we search cabinets near the coffee machine and for fruits we try the fridge. In this work, we focus on incorporating semantic priors in the task of semantic navigation. We propose to use Graph Convolutional Networks for incorporating the prior knowledge into a deep reinforcement learning framework. The agent uses the features from the knowledge graph to predict the actions. For evaluation, we use the AI2-THOR framework. Our experiments show how semantic knowledge improves performance significantly. More importantly, we show improvement in generalization to unseen scenes and/or objects. The supplementary video can be accessed at the following link: this https URL .
Oral Health-Related Quality of Life in People with Rare Hereditary Connective Tissue Disorders: Marfan Syndrome

Background: The aim of this study was to analyze data on oral health-related quality of life (OHRQoL) in people with Marfan syndrome and to obtain information on the diagnosis period, orthodontic treatment, and oral symptoms. Methods: A questionnaire was developed consisting of open questions and the standardized German version of the OHIP-14 (Oral Health Impact Profile) questionnaire for the evaluation of OHRQoL. The age of diagnosis, time period from the first signs of the disease to diagnosis, and OHIP-values were compared between male and female participants. Additionally, the OHIP-values between participants who were orthodontically treated and those who were not treated were assessed. The statistical analysis was performed using the Mann–Whitney test with a significance level at p = 0.05. Results: A total of 51 questionnaires were evaluated, which included 34 female and 17 male participants. Overall, 84% of respondents reported oral symptoms. Male respondents tended to diagnose the disease earlier (p = 0.00), with a smaller period between the first symptom and the diagnosis (p = 0.04). The OHIP-14 score was gender-neutral at 13.65 ± 13.53 points. Conclusion: In Marfan syndrome, many years (12.01 ± 11.61) elapse between the onset of first symptoms and correct diagnosis of the disease. People with Marfan syndrome have a worse OHRQoL than do the general population.
U-Net: Machine Reading Comprehension with Unanswerable Questions

Machine reading comprehension with unanswerable questions is a new challenging task for natural language processing. A key subtask is to reliably predict whether the question is unanswerable. In this paper, we propose a unified model, called U-Net, with three important components: answer pointer, no-answer pointer, and answer verifier. We introduce a universal node and thus process the question and its context passage as a single contiguous sequence of tokens. The universal node encodes the fused information from both the question and passage, and plays an important role to predict whether the question is answerable and also greatly improves the conciseness of the U-Net. Different from the state-of-art pipeline models, U-Net can be learned in an end-to-end fashion. The experimental results on the SQuAD 2.0 dataset show that U-Net can effectively predict the unanswerability of questions and achieves an F1 score of 71.7 on SQuAD 2.0.
The Effects of Mo and Nb on the Microstructures and Properties of CrFeCoNi(Nb,Mo) Alloys

The effects of niobium and molybdenum additions on the microstructures, hardness and corrosion behaviors of CrFeCoNi(Nb,Mo) alloys were investigated. All of the CrFeCoNi(Nb,Mo) alloys displayed dendritic microstructures. The dendrites of CrFeCoNiNb and CrFeCoNiNb0.5Mo0. 5 alloys were a hexagonal close packing (HCP) phase and the interdendrites were a eutectic structure of HCP and face-centered cubic (FCC) phases. Additionally, the dendrites of CrFeCoNiMo alloys were a simple cubic (SC) phase and the interdendrites were a eutectic structure of SC and FCC phases. The volume fraction of dendrites and interdendrites in these alloys were calculated. The influences of the volume fraction of dendrite in the alloys on the overall hardness were also discussed. The CrFeCoNiNb alloy had the larger volume fraction of dendrite and thus had the highest hardness among these alloys. The CrFeCoNi(Nb,Mo) alloys also showed better corrosion resistances in 1 M H2SO4 and 1 M NaCl solutions by comparing with commercial 304 stainless steel. The CrFeCoNiNb0.5Mo0. 5 alloy possessed the best corrosion resistances in these solutions among the CrFeCoNi(Nb,Mo) alloys.
Poincaré GloVe: Hyperbolic Word Embeddings

Words are not created equal. In fact, they form an aristocratic graph with a latent hierarchical structure that the next generation of unsupervised learned word embeddings should reveal. In this paper, justified by the notion of delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in a Cartesian product of hyperbolic spaces which we theoretically connect to the Gaussian word embeddings and their Fisher geometry. This connection allows us to introduce a novel principled hypernymy score for word embeddings. Moreover, we adapt the well-known Glove algorithm to learn unsupervised word embeddings in this type of Riemannian manifolds. We further explain how to solve the analogy task using the Riemannian parallel transport that generalizes vector arithmetics to this new type of geometry. Empirically, based on extensive experiments, we prove that our embeddings, trained unsupervised, are the first to simultaneously outperform strong and popular baselines on the tasks of similarity, analogy and hypernymy detection. In particular, for word hypernymy, we obtain new state-of-the-art on fully unsupervised WBLESS classification accuracy.
The Effectiveness of Nonprescription Drug Labels in the United States: Insights from Recent Research and Opportunities for the Future

Despite providing a consistent and comprehensible format for over-the-counter (OTC) drug communication, research suggests important limitations in the communication effectiveness of the Drug Facts Label required on OTC drugs in the United States. This literature is reviewed and some of these critical limitations of the Drug Facts Label (DFL) highlighted. These include difficulty communicating complex information that requires integration of multiple pieces of label information and limited adaptability to serve the unique needs of individual populations (e.g., low literacy or older consumers). Potential ways to improve the DFL’s communication effectiveness are identified along with complementary opportunities to improve OTC drug communication by leveraging the role of pharmacists and use of adjunctive technologies.
Time on task matters most in video game expertise

In this study, we investigated whether working memory capacity (WMC), personality characteristics (grit) and number of matches played (time on task) can predict performance score (matchmaking rating [MMR]) in experienced players of a popular video game called Dota 2. A questionnaire and four online-based cognitive tasks were used to gather the data, and structural equation modelling (SEM) was used to investigate the interrelationships between constructs. The results showed that time on task was the strongest predictor of MMR, and grit also significantly influenced performance. However, WMC did not play a substantial role in predicting performance while playing Dota 2. These results are discussed in relation to sample characteristics and the role of deliberate practice and skill acquisition within the domain of playing Dota 2. Further, we suggest that future research investigates the social aspects of attaining skill, the relationship between personality and performance, and the qualitative aspects of time spent on a task.
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension

We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at this http URL
Reduced cortical brain activity with the use of microprocessor-controlled prosthetic knees during walking

Background: Individuals using a lower-limb prosthesis indicate that they need to concentrate on every step they take. Despite self-reports of increased cognitive demand, there is limited understanding of the link between cognitive processes and walking when using a lower-limb prosthesis. Objective: The objective was to assess cortical brain activity during level walking in individuals using different prosthetic knee components and compare them to healthy controls. It was hypothesized that the least activity would be observed in the healthy control group, followed by individuals using a microprocessor-controlled prosthetic knee and finally individuals using a non-microprocessor-controlled prosthetic knee. Study design: Cross-sectional study. Methods: An optical brain imaging system was used to measure relative changes in concentration of oxygenated and de-oxygenated haemoglobin in the frontal and motor cortices during level walking. The number of steps and time to walk 10 m was also recorded. The 6-min walk test was assessed as a measure of functional capacity. Results: Individuals with a transfemoral or knee-disarticulation amputation, using non-microprocessor-controlled prosthetic knee (n = 14) or microprocessor-controlled prosthetic knee (n = 15) joints and healthy controls (n = 16) participated in the study. A significant increase was observed in cortical brain activity of individuals walking with a non-microprocessor-controlled prosthetic knee when compared to healthy controls (p < 0.05) and individuals walking with an microprocessor-controlled prosthetic knee joint (p < 0.05). Conclusion: Individuals walking with a non-microprocessor-controlled prosthetic knee demonstrated an increase in cortical brain activity compared to healthy individuals. Use of a microprocessor-controlled prosthetic knee was associated with less cortical brain activity than use of a non-microprocessor-controlled prosthetic knee. Clinical relevance Increased understanding of cognitive processes underlying walking when using different types of prosthetic knees can help to optimize selection of prosthetic components and provide an opportunity to enhance functioning with a prosthesis.
Exploiting semantics in adversarial training for image-level domain adaptation

Performance achievable by modern deep learning approaches are directly related to the amount of data used at training time. Unfortunately, the annotation process is notoriously tedious and expensive, especially for pixel-wise tasks like semantic segmentation. Recent works have proposed to rely on synthetically generated imagery to ease the training set creation. However, models trained on these kind of data usually under-perform on real images due to the well known issue of domain shift. We address this problem by learning a domain-to-domain image translation GAN to shrink the gap between real and synthetic images. Peculiarly to our method, we introduce semantic constraints into the generation process to both avoid artifacts and guide the synthesis. To prove the effectiveness of our proposal, we show how a semantic segmentation CNN trained on images from the synthetic GTA dataset adapted by our method can improve performance by more than 16% mIoU with respect to the same model trained on synthetic images.
Media Exposure and General Trust as Predictors of Post-traumatic Stress Disorder: Ten Years after the 5.12 Wenchuan Earthquake in China

There is a paucity of literature on the roles of media exposure, general trust, and their interactions in long-term post-traumatic stress disorder (PTSD) symptoms after a natural disaster. Trying to address this knowledge gap, our study aimed to (a) investigate whether exposure to media coverage during the traumatic event and general trust directly affected adult survivors’ long-term PTSD symptoms 10 years after the 5.12 Wenchuan earthquake, and (b) to identify the potential differential pattern of the influence of media exposure on PTSD symptoms for adult survivors with various levels of general trust. Using cross-sectional methodology, we surveyed participants (N = 1000) recruited from six disaster-affected counties. We assessed PTSD symptoms, media exposure, general trust, demographic characteristics, socioeconomic status, and earthquake exposure. Data were analyzed descriptively and with Tobit regression analyses. Reversed relationships between general trust and PTSD were verified, whereas no direct links were found between media exposure and PTSD. Interaction tests revealed that media exposure alleviated PTSD for high-trust survivors, but aggravated PTSD for low-trust survivors. These results suggest that general trust building should be considered in post-disaster construction activities.
Neural Nearest Neighbors Networks

Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r. t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of KNN selection that maintains differentiability w.r. t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification as well as for image restoration, including image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.
Emotion Sharing and Augmentation in Cooperative Virtual Reality Games

We present preliminary findings from sharing and augmenting facial expression in cooperative social Virtual Reality (VR) games. We implemented a prototype system for capturing and sharing facial expression between VR players through their avatar. We describe our current prototype system and how it could be assimilated into a system for enhancing social VR experience. Two social VR games were created for a preliminary study. We discuss our findings from our pilots, potential games for this system, and future directions for this research.
Nocturia in Parkinson's Disease: Why Does It Occur and How to Manage?

Nocturia is one of the commonest nonmotor symptoms in Parkinson's disease (PD) and has a significant impact on quality of life both for patients and their carers. There exists a relation between nocturia and poor sleep quality, falls, and institutionalization. Nocturia may manifest as a result of reduced functional bladder capacity or nocturnal polyuria; however, most often the cause is multifactorial. Disorders of circadian rhythm regulation are known to occur with sleep disturbances in PD may also contribute to nocturia.
Comparison of Surgically Excised Premacular Membranes in Eyes with Macular Pucker and Proliferative Vitreoretinopathy

ABSTRACT Purpose: To describe and compare characteristics of premacular membranes in idiopathic macular pucker (iMP) and proliferative vitreoretinopathy (PVR) using immunofluorescence and transmission electron microscopy. Materials and methods: For immunocytochemical and ultrastructural analyses, premacular membranes were harvested during vitrectomy from 16 eyes with iMP and 12 eyes with PVR. All specimens were processed as flat mounts for phase-contrast and fluorescence microscopy. We used 19 different primary antibodies such as anti-α-smooth muscle actin (α-SMA), anti-integrin-αv, anti-galectin, anti-IBA-1, anti-EMMPRIN (CD147), anti-ricinus (RCS) and anti-collagen-type I. Eight of 28 eyes were also prepared for transmission electron microscopy. Results: In all eyes with iMP and PVR, positive immunoreactivity of integrins, especially αvβ3 was found. There was also a strong staining of anti-α-SMA, anti-galectin, anti-EMMPRIN, anti-RCS, anti-IBA1 and anti-collagen-type I. Transmission electron microscopy showed that premacular membrane of iMP composed of myofibroblasts, glial cells and fibroblasts. In eyes with PVR, retinal pigment epithelial cells and myofibroblasts were seen as predominant cell types. Conclusion: Premacular membranes of iMP and PVR presented with similarities in cell distribution and immunoreactivity, but showed differences in cell composition. Herein, we demonstrate immunocytochemical characteristics involved in fibrotic processes. Cell transdifferentiation into myofibroblasts represents an important process in pathogenesis of both entities. In order to address future anti-fibrotic treatment strategies, we emphasize that both fibrotic diseases share distinct immunocytochemical and ultrastructural features.
Hypersexuality in Parkinson's Disease: Systematic Review and Report of 7 New Cases

Hypersexuality (HS) was one of the earliest examples of an impulse control disorder (ICD) or behavior to be associated with treatment for Parkinson's disease (PD), with an estimated prevalence of approximately 3.5%. Here, we report on a systematic review of the published literature of HS in PD with a view to uncovering evidence as to whether it is distinct from other ICDs. In addition, we report on 7 new cases that had broad neuropsychological testing, including a gambling test, which taps into reward and inhibitory mechanisms. The review uncovered a number of case series and cohorts that comment on the prevalence of HS, but very few made systematic comparisons with other ICDs, although younger onset and male sex are usually noted. A few studies have begun to map out a neuropsychological profile for HS, and our own cases show particular deficits in learning from negative outcomes, but, overall, there are insufficient data to draw firm conclusions. Functional imaging has shown patterns of increased content‐specific activation in response to sexual material and this might relate to increased dopamine release. We conclude with a brief survey of the neurobiology of sexuality, which suggests possible avenues for further research and treatment of HS.