Miracle-dz/UniFAR-FTUs huggingface dataset:
Receptor usage and cell entry of bat coronavirus HKU4 provide insight into bat-to-human transmission of MERS coronavirus

Significance A constant and long-term threat to human health is cross-species transmission of Middle East respiratory syndrome coronavirus (MERS-CoV) from bats to humans. However, this process is poorly understood. Examining the cross-species transmissibility of bat coronavirus HKU4, which is genetically related to MERS-CoV, can provide critical information about the likely causes of MERS-CoV infections in humans. Here we investigate the receptor usage and cell entry mechanism of HKU4 compared with MERS-CoV. Our results reveal that MERS-CoV has adapted to use human receptor and cellular proteases for efficient human cell entry, whereas HKU4 can potentially follow-up and also infect human cells. These findings are critical for evaluating emerging disease potentials of bat coronaviruses and for preventing and controlling their spread in humans. Middle East respiratory syndrome coronavirus (MERS-CoV) currently spreads in humans and causes ∼36% fatality in infected patients. Believed to have originated from bats, MERS-CoV is genetically related to bat coronaviruses HKU4 and HKU5. To understand how bat coronaviruses transmit to humans, we investigated the receptor usage and cell entry activity of the virus-surface spike proteins of HKU4 and HKU5. We found that dipeptidyl peptidase 4 (DPP4), the receptor for MERS-CoV, is also the receptor for HKU4, but not HKU5. Despite sharing a common receptor, MERS-CoV and HKU4 spikes demonstrated functional differences. First, whereas MERS-CoV prefers human DPP4 over bat DPP4 as its receptor, HKU4 shows the opposite trend. Second, in the absence of exogenous proteases, both MERS-CoV and HKU4 spikes mediate pseudovirus entry into bat cells, whereas only MERS-CoV spike, but not HKU4 spike, mediates pseudovirus entry into human cells. Thus, MERS-CoV, but not HKU4, has adapted to use human DPP4 and human cellular proteases for efficient human cell entry, contributing to the enhanced pathogenesis of MERS-CoV in humans. These results establish DPP4 as a functional receptor for HKU4 and host cellular proteases as a host range determinant for HKU4. They also suggest that DPP4-recognizing bat coronaviruses threaten human health because of their spikes’ capability to adapt to human cells for cross-species transmissions.
The Development of Gender Constancy in Early Childhood and Its Relation to Time Comprehension and False-Belief Understanding

What is the developmental course of children's gender constancy? Do other cognitive abilities such as time comprehension and false-belief understanding foster gender constancy and the subcomponents gender stability and gender consistency? We examined the development of gender constancy and its relation to time comprehension and false-belief understanding in children aged 3 to 6 years old. A new gender constancy task was designed, consisting of video sequences presenting a boy and a girl dressing up in clothes typically worn by the opposite sex and playing with toys usually played with by the opposite sex. Time comprehension was measured by a comparison of three hourglasses containing different amounts of sand (as in Bischof-Köhler, 2000). False-belief understanding was tested with an adaptation of Wimmer and Perner's (1983) seminal change-of-location task. Regression analyses revealed that false-belief understanding predicted gender constancy and gender consistency and that time comprehension predicted gender stability. These relations are discussed according to task-specific characteristics of gender constancy and a domain-general mechanism underlying these abilities.
A Mutually Recurrent Interval Type-2 Neural Fuzzy System (MRIT2NFS) With Self-Evolving Structure and Parameters

In this paper, a mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) is proposed for the identification of nonlinear and time-varying systems. The MRIT2NFS uses type-2 fuzzy sets in order to enhance noise tolerance of the system. In the MRIT2NFS, the antecedent part of each recurrent fuzzy rule is defined using interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang type with interval weights. The antecedent part of MRIT2NFS forms a local internal feedback and interaction loop by feeding the rule firing strength of each rule to others including itself. The consequent is a linear combination of exogenous input variables. The learning of MRIT2NFS starts with an empty rule base and all rules are learned online via structure and parameter learning. The structure learning of MRIT2NFS uses online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by rule-ordered Kalman filter algorithm to reinforce parameter learning ability. The type-2 fuzzy sets in the antecedent and weights representing the mutual feedback are learned by the gradient descent algorithm. After the training, a weight-elimination scheme eliminates feedback connections that do not have much effect on the network behavior. This method can efficiently remove redundant recurrence and interaction weights. Finally, the MRIT2NFS is used for system identification under both noise-free and noisy environments. For this, we consider both time series prediction and nonlinear plant modeling. Compared with type-1 recurrent fuzzy neural networks, simulation results show that our approach produces smaller root-mean-squared errors using the same number of iterations.
Depressive Symptoms, Anger/Hostility, and Relationship Quality in Young Couples

depression is associated with lower relationship quality, but this association could overlap with effects of other affective characteristics correlated with depression, specifically anger and hostility. We examined actor and partner associations of depressive symptoms and anger/hostility with relationship quality in 152 Caucasian and 42 african american young married or cohabitating couples. in significant actor and partner effects, higher levels of depressive symptoms and anger/hostility were independently associated with lower overall relationship quality, though they were associated with differing aspects of relationship quality. although findings were generally similar across ethnic groups, partners’ depression was significantly more closely related to relationship quality for Caucasian than african american couples. hence, models of emotional adjustment and relationship quality should be expanded beyond depression to include anger/hostility, and preliminary evidence suggests potential ethnic similarities and differences.
Plasma and erythrocyte zinc in pre-eclampsia and its correlation with foetal outcome.

OBJECTIVES To determine the plasma and erythrocyte zinc levels in women with preeclampsia and eclampsia after 28 weeks of gestation and compare with normal pregnancy and to correlate with the foetal outcome. MATERIAL AND METHODS 150 antenatal women were divided into Study group A (75 women with preeclampsia or eclampsia); Control group B (75 women with normal pregnancy). Plasma and erythrocyte zinc levels were estimated in both the groups and correlated with the foetal outcome. RESULTS The plasma zinc levels were significantly low (p < 0.01) in women with severe pre-eclampsia (9.28 +/- 1.63 micromol/l) and eclampsia (9.28 +/- 2.61 micromol/l) as compared to controls (10.63 +/- 1.82 micromol/l). The difference in the erythrocyte zinc levels was not statistically significant in the two groups. There were 4 (5.33%) stillbirths and 8 (10.66%) neonatal deaths in the study group as compared to 2 (2.6%) neonatal deaths in the control group. There was no statistically significant difference in the plasma and erythrocyte zinc levels in infants weighing less than 2500 gm in both the groups.The maternal mortality rate was 1.33% and the overall perinatal mortality rate in the study group was 17.3% as compared to 2.6% in the control group. CONCLUSIONS Plasma zinc levels were significantly lowered in severe preeclampsia and eclampsia while the erythrocyte zinc levels did not show any significant change. There was no correlation between plasma or erythrocyte zinc levels and intrauterine growth restriction in pre-eclampsia.
Current Methods for Protein Secondary‐Structure Prediction Based on Support Vector Machines

The desire to understand protein structure has produced many approaches over the last four decades since Blout et al. (1960) attempted to correlate the sequence information of amino acids with their structural elements (Casbon, 2002). Instead of costly and time-consuming experimental approaches, effective prediction methods have been developed continuously. With the help of growing databases and the evolutionary information available from multiple-sequence alignments, resources for secondary-structure prediction became abundant. Also, progress in machine learning technology provided various advanced tools for prediction. Among the many machine learning approaches, support vector machine (SVM) methods are the most recent to be used for structure prediction. SVMs perform successfully, but compared with other machine learning approaches, there is no systematic review in the SVM approach when applied to secondary-structure prediction. Therefore, this study focuses mainly on methods of predicting secondary structure based on support vector machines. The organization of this chapter is as follows. In Section 1.1, traditional secondary-structure prediction approaches are described. In Section 1.2, various SVM-based prediction methods are introduced. In Section 1.3, the performance of SVM methods is evaluated, and in Section 1.4, problems with the SVM approach and efforts to overcome them are discussed.
Social workers' assessments of needs and interventions for adolescents with substance misuse problems, criminal behaviour and mental health difficulties: a vignette study

The aim of the current vignette study was to examine how social workers assess adolescents with substance misuse problems, criminal behaviour and mental health difficulties, and how they make decisions about treatment interventions to reduce these problems. Earlier research has shown lack of knowledge concerning factors and processes that govern assessments and decisions in social work practice. In this anonymously administered survey, social workers in Stockholm, Sweden, were invited to assess fictitious youth issues. We found that social workers recognise the problems and needs of young people, but that they find it harder to link needs to evidence-based interventions. Also, there is a tendency to overrate alleged non-serious problems in the vignettes. We conclude that there is a need for increased knowledge about evidence-based methods and more structured procedures to broaden the basis for decisions.
N ov 2 01 7 Data Augmentation in Emotion Classification using Generative Adversarial Networks

It is a difficult task to classify images with multiple labels only using a small number of labeled samples and especially, with unbalanced distribution. In this paper we propose a data augmentation method using generative adversarial networks(GAN), that can complement and complete the data manifold, assist the classifier better to find margins or hyper-planes of neighboring classes, and finally lead to a better performance in emotion classification task. Specifically, we design a pipeline containing a CNN model as classifier and a cycle-consistent adversarial networks(CycleGAN) to generate supplementary data from given classes. In order to avoid gradient vanishing, we apply a least-squared distance in least squares generative adversarial networks(LSGAN) to adversarial loss. We also propose several evaluation methods on three benchmark facial epression datasets to validate GAN’s contribution in data augmentation. Qualitative evaluation indicates that data manifolds show a significant improvement in distribution integrity and margin clarity between classes. Quantitative comparisons show that we can obtain 5%∼10% increase in the classification accuracy after employing our data augmentation technique.
Unsupervised language model adaptation for automatic speech recognition of broadcast news using web 2.0

We improve the automatic speech recognition of broadcast news using paradigms from Web 2.0 to obtain timeand topicrelevant text data for language modeling. We elaborate an unsupervised text collection and decoding strategy that includes crawling appropriate texts from RSS Feeds, complementing it with texts from Twitter, language model and vocabulary adaptation, as well as a 2-pass decoding. The word error rates of the tested French broadcast news shows from Europe 1 are reduced by almost 32% relative with an underlying language model from the GlobalPhone project [1] and by almost 4% with an underlying language model from the Quaero project. The tools that we use for the text normalization, the collection of RSS Feeds together with the text on the related websites, a TF-IDF-based topic words extraction, as well as the opportunity for language model interpolation are available in our Rapid Language Adaptation Toolkit [2] [3].
Immobilisation of hydroxyapatite-collagen on polydopamine grafted stainless steel 316L: Coating adhesion and in vitro cells evaluation

The utilisation of hydroxyapatite and collagen as bioactive coating materials could enhance cells attachment, proliferation and osseointegration. However, most methods to form crystal hydroxyapatite coating do not allow the incorporation of polymer/organic compound due to production phase of high sintering temperature. In this study, a polydopamine film was used as an intermediate layer to immobilise hydroxyapatite-collagen without the introduction of high sintering temperature. The surface roughness, coating adhesion, bioactivity and osteoblast attachment on the hydroxyapatite-collagen coating were assessed as these properties remains unknown on the polydopamine grafted film. The coating was developed by grafting stainless steel 316L disks with a polydopamine film. Collagen type I fibres were then immobilised on the grafted film, followed by the biomineralisation of hydroxyapatite. The surface roughness and coating adhesion analyses were later performed by using AFM instrument. An Alamar Blue assay was used to determine the cytotoxicity of the coating, while an alkaline phosphatase activity test was conducted to evaluate the osteogenic differentiation of human fetal osteoblasts on the coating. Finally, the morphology of cells attachment on the coating was visualised under FESEM. The highest RMS roughness and coating adhesion were observed on the hydroxyapatite-collagen coating (hydroxyapatite-coll-dopa). The hydroxyapatite-coll-dopa coating was non-toxic to the osteoblast cells with greater cells proliferation, greater level of alkaline phosphate production and more cells attachment. These results indicate that the immobilisation of hydroxyapatite and collagen using an intermediate polydopamine is identical to enhance coating adhesion, osteoblast cells attachment, proliferation and differentiation, and thus could be implemented as a coating material on orthopaedic and dental implants.
POLG2 deficiency causes adult‐onset syndromic sensory neuropathy, ataxia and parkinsonism

Mitochondrial dysfunction plays a key role in the pathophysiology of neurodegenerative disorders such as ataxia and Parkinson's disease. We describe an extended Belgian pedigree where seven individuals presented with adult‐onset cerebellar ataxia, axonal peripheral ataxic neuropathy, and tremor, in variable combination with parkinsonism, seizures, cognitive decline, and ophthalmoplegia. We sought to identify the underlying molecular etiology and characterize the mitochondrial pathophysiology of this neurological syndrome.
A hybrid brain-computer interface control strategy in a virtual environment

This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.
Automated image analysis of protein localization in budding yeast

MOTIVATION The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast. RESULTS We developed computational methods to automatically analyze the images created by the UCSF yeast GFP fusion localization project. The system was trained to recognize the same location categories that were used in that study. We applied the system to 2640 images, and the system gave the same label as the previous assignments to 2139 images (81%). When only the highest confidence assignments were considered, 94.7% agreement was observed. Visual examination of the proteins for which the two approaches disagree suggests that at least some of the automated assignments may be more accurate. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins. AVAILABILITY The original images analyzed in this article are available at http://yeastgfp.ucsf. edu, and source code and results are available at http://murphylab.web. cmu.edu/software.
Dimethyl 4,4′-(diazenediyl)dibenzoate at 100 K

In the asymmetric part of the unit cell of the title compound, C16H14N2O4, there are two chemically equivalent but crystallographic independent half molecules. The geometric centre of each complete molecule lies on a crystallographic inversion centre. Both molecules are almost planar [mean deviations of atoms in the two molecules are 0.032 (2) and 0.044 (2) Å] and their geometries are similar. In the crystal, molecules are arranged in columns along the a axis. There are no intermolecular donor–acceptor distances shorter than 3.4 Å.
Emotions: A Bridge Between Nature and Society? ∗

The ﬁeld of Artiﬁcial Intelligence has, for a long time, neglected the role of emotions in human cognition, with few but notable exceptions. This has been motivated in part by the assumption that the emulation of human rationality by a machine is sufﬁcient for attaining general human-level intelligence. This paper reviews neuroscientiﬁc results showing empirical evidence, consistently for over a decade, sustaining that emotion mechanisms in the brain play a fundamental role in decision making processes, as well as in cognitive regulation. Moreover, this role takes place regardless of whether the subject is aware of any emotion. These mechanisms are particularly important in social contexts. Lesions in the pathways supporting these mechanisms provoke serious impairments on social behavior. For instance, subjects with lesions in the pathways between the orbitofrontal cortex and the amygdala are no longer able to sustain an healthy social live, despite their intact intellectual capabilities. Strik-ingly, these patients are even able to verbally describe what would be the proper social behavior, although are unable to follow it. One important mechanism in social contexts is empathy, fundamental for proper social relations. It has been proposed that empathy is founded on mechanisms analogous to the mirror neurons.
An improved color image demosaicking algorithm

Color image demosaicking is an important issue in color image acquisition procedures. Most of the existing demosaicking methods reduce the artifacts at the price of the high computational cost posterior processing. In this paper, we propose a more effective demosaicking algorithm. The proposed method improves the qualities of demosaicked images by interpolation, rather than posterior processing. In order to avoid the propagation of errors occurred during green channel reconstruction, we interpolate red and blue channels by original samples to the greatest extent. Experimental results show that the proposed algorithm yields better demosaicking images as compared with a number of typical demosaicking algorithms. Furthermore, the computational cost of the proposed method is lower.
Navigating and accessing higher education: The experiences of community college students with attention deficit hyperactivity disorder

Background: Studies conducted in the areas of attention deficit hyperactivity disorder (ADHD) and students support services at the postsecondary level are very limited. Therefore, there is a need to conduct a study that examines the effects of supports services on students with ADHD. Purpose: The purpose of this study was to gain an understanding of students with ADHD and their ability to access higher education. Method: Purposeful sampling of 10 students with ADHD enrolled at community college was selected to interview individually. Results: Research findings illuminate the complex ways that students with ADHD access higher education. Conclusion: Community colleges should increase outreach for students with ADHD and there should be continued training to instructors on how to meet the needs of students with disabilities.
Self-efficacy Beliefs in Mathematics, Native Language Literacy and Foreign Language Amongst Boys and Girls with and without Mathematic Difficulties

The aim was to examine achievement and self-efficacy in mathematics and native and foreign language literacy in children with specific mathematic LD (MD-only), children with comorbid mathematic and reading difficulties (MD-RD), and compare them with children without LD (controls), as well as to explore gender differences. Participants were 143 fifth-graders in Sweden who completed National Tests and measures of self-efficacy in mathematics and literacy. The MD-RD children displayed lower self-efficacy in all subjects compared to the controls, even when controlling for achievement. The MD-only children displayed lower self-efficacy in mathematics, completely accounted for by their lower mathematic achievement. The lower self-efficacy for children with learning disabilities may primarily be explained by their history of low achievement interpreted as failures and their emphasis on negative appraisals.