Miracle-dz/UniFAR-FTUs huggingface dataset:
Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

The autism spectrum hypothesis states that not only diagnosed individuals but also individuals from the general population exhibit a certain amount of autistic traits. While this idea is supported by neuroimaging studies, there have been few electrophysiological studies. In particular, there have been no spontaneous resting‐state studies yet. In order to examine the autism spectrum hypothesis, the present study tried to predict the level of autistic traits typically developing young adults (n = 93) exhibit from spontaneous resting‐state gamma power, a measure that has been linked to social functioning impairments seen in autism. The influence of age and gender was controlled for by employing regression. It was expected that enhanced gamma activity would be predictive of self‐reported autistic traits. The model with only age and gender included reached significance, with higher age within this student population being related to more autistic traits. However, no relationship between either low (30–50 Hz) or high (50–70 Hz) gamma power and autistic traits was found. Models with eyes closed low gamma asymmetry and eyes closed high gamma asymmetry included did reach significance, but these findings were not robust, and the gamma asymmetry explained very little additional variance above age and gender. In addition, exploratory correlation analyses showed no relationship between the other power spectra (delta, theta, alpha and beta) on the one hand and autistic traits on the other hand, suggesting that any relationship between spontaneous resting‐state brain electrophysiology and autistic traits might not be strong enough to be detected in the general population.
Series in Informatics P 4 xos : Consensus as a Network Service

In this paper, we explore how a programmable forwarding plane offered by a new breed of network switches might naturally accelerate consensus protocols, specifically focusing on Paxos. The performance of consensus protocols has long been a concern; By implementing Paxos in the forwarding plane, we are able to significantly increase throughput and reduce latency. Our P4-based implementation running on an ASIC in isolation can process over 2.5 billion consensus messages per second, a four orders of magnitude improvement in throughput over a widely-used software implementation. This effectively removes consensus as a bottleneck for distributed applications in data centers. Beyond sheer performance, our approach offers several other important benefits: it readily lends itself to formal verification; it does not rely on any additional network hardware; and as a full Paxos implementation, it makes only very weak assumptions about the network. Report Info
Diffusion of ring-shaped proteins along DNA: case study of sliding clamps

Abstract Several DNA-binding proteins, such as topoisomerases, helicases and sliding clamps, have a toroidal (i.e. ring) shape that topologically traps DNA, with this quality being essential to their function. Many DNA-binding proteins that function, for example, as transcription factors or enzymes were shown to be able to diffuse linearly (i.e. slide) along DNA during the search for their target binding sites. The protein's sliding properties and ability to search DNA, which often also involves hopping and dissociation, are expected to be different when it encircles the DNA. In this study, we explored the linear diffusion of four ring-shaped proteins of very similar structure: three sliding clamps (PCNA, β-clamp, and the gp45) and the 9-1-1 protein, with a particular focus on PCNA. Coarse-grained molecular dynamics simulations were performed to decipher the sliding mechanism adopted by these ring-shaped proteins and to determine how the molecular properties of the inner and outer ring govern its search speed. We designed in silico variants to dissect the contributions of ring geometry and electrostatics to the sliding speed of ring-shaped proteins along DNA. We found that the toroidal proteins diffuse when they are tilted relative to the DNA axis and able to rotate during translocation, but that coupling between rotation and translocation is quite weak. Their diffusion speed is affected by the shape of the inner ring and, to a lesser extent, by its electrostatic properties. However, breaking the symmetry of the electrostatic potential can result in deviation of the DNA from the center of the ring and cause slower linear diffusion. The findings are discussed in light of earlier computational and experimental studies on the sliding of clamps.
Knock , Knock , Neo ! Spawning Knock-Knock Jokes

Of the very little research done on computational humor, even little has been done on humor generation that too is limited to fill-a-phrase-in-the-blank kind of jokes and none of them even generate a “free sentence”. In this work, we focus on the task of generating jokes of a simple fixed structure: Knock-Knock jokes. Our approach is the combination of find-a-wordplay and generate-a-punchline routines. Our preliminary results suggest that more relaxed approach along these lines should be capable of generating sufficiently good quality jokes of similar classes.
Predict chronic kidney disease using data mining algorithms in hadoop

This paper presents the prediction of chronic kidney disease using data mining classifiers. Now a days, chronic diseases are escalating day by day and play a paramount role in an individual's life. To elicitate the hidden information about chronic disease from a given dataset, data mining technology is used to make decisions. Big data is another area of research used for the storage and processing of voluminous data which is structured, unstructured and semi-structured. In this paper, to predict the chronic kidney disease, two data mining classifiers are used. KNN (K-Nearest Neighbor) and SVM (Support Vector Machine). These approaches provide the following information: i. Accuracy ii. Error.
Efficient Algorithms for Finding Approximate Heavy Hitters in Personalized PageRanks

Given a directed graph G, a source node s, and a target node t, the personalized PageRank (PPR of t with respect to s is the probability that a random walk starting from s terminates at t. The average of the personalized PageRank score of t with respect to each source node υ∈ V is exactly the PageRank score π( t ) of node t , which denotes the overall importance of node t in the graph. A heavy hitter of node t is a node whose contribution to π( t ) is above a φ fraction, where φ is a value between 0 and 1. Finding heavy hitters has important applications in link spam detection, classification of web pages, and friend recommendations. In this paper, we propose BLOG, an efficient framework for three types of heavy hitter queries: the pairwise approximate heavy hitter (AHH), the reverse AHH, and the multi-source reverse AHH queries. For pairwise AHH queries, our algorithm combines the Monte-Carlo approach and the backward propagation approach to reduce the cost of both methods, and incorporates new techniques to deal with high in-degree nodes. For reverse AHH and multi-source reverse AHH queries, our algorithm extends the ideas behind the pairwise AHH algorithm with a new "logarithmic bucketing'' technique to improve the query efficiency. Extensive experiments demonstrate that our BLOG is far more efficient than alternative solutions on the three queries.
Block-optimized Variable Bit Rate Neural Image Compression

In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in achieving binarization simulation, variable bit rates with multiple networks, entropy-friendly representations, inference-stage code optimization and performance-improving normalization layers in the auto-encoder. We evaluate and show the incremental performance increase of each of our contributions.
How Do Thinking Styles Influence Collaborative Dispositions? A Study on the Relationships between Thinking Styles and Collaborative Dispositions for Youngsters in Taiwan

Collaboration dispositions keep attracting high attention in the business world for organizational competition and teamwork efficiency. Educators also highly value the cultivation of youngsters' thinking strategies and styles which facilitate their learning performance and even career achievement. This study was conducted to identify the relationship among thinking styles (including positive thinking and negative thinking) and collaborative dis- positions. Three self-rating instruments were employed to survey 901 college students' experience as research data. A series of analyses on the reliability and validity of instruments were conducted to validate the measure- ment model. The SEM analysis results indicated the structural model and the influences of both thinking styles on the youngsters' collaborative dispositions. In addition, it was concluded that only the individuals highly evalu- ating others were willing to and able to collaborate with others well. On the other hand, the factor of maladjust- ment and little desire for change was the dominant negative thinking which severely eliminated collaborative dispositions. Several suggestions were accordingly provided on the bases of research conclusions.
Correlation of Hepatitis B Virus Surface Antigen Titer withHepatitis Delta Virus Replication Level and HistologicalActivity in Chronic Hepatitis D

Chronic hepatitis delta (CHD) is a serious cause of viral hepatitis throughout the world, with high morbidity and mortality rates [1-3]. Estimates suggest that 15-20 million people worldwide have been infected by hepatitis delta virus (HDV). In Turkey, HDV remains a serious health problem, with epidemiologic studies in East and Southwest Anatolia regions showing delta super infection in 9-58% of HBV infected individuals [4]. HDV was first isolated in 1977 from the hepatocytes of hepatitis B virus (HBV) surface antigen carriers (HBsAg) as a nuclear antigen that differed from HBsAg, core (HBcAg) and e (HBeAg) antigens [5,6]. HDV is a defective virus, requiring the HBV envelope proteins, but can replicate within cells in the absence of HBV [7]. HBsAg, however, is required not only for HDV infectivity [8]. but also for its packing and extracellular release [9]. Treatment of CHD is complicated by features of HDV. For example, many other viruses encode the enzymes related to their replication and development. Thus, viral replication can be inhibited by inhibiting the synthesis or activity of these enzymes. HDV, however, encodes only HDV RNA-dependent ribosome, with other enzymes involved in viral replication being cellular enzymes. Treatment of CHD should be related to the structure and life cycle of HDV and its relationships with HBV.
Prevalence and predictors of major depressive disorder in bereaved caregivers at 6 and 13 months

Abstract Objective Depressive symptoms are common in bereaved caregivers; however, there have been few prospective studies using a structured interview. This study investigated the prevalence and preloss predictors of major depressive disorder (MDD) in bereaved caregivers of patients in a palliative care unit. Method This prospective cohort study collected caregiver sociodemographic and psychological data before the death of a palliative care unit patient, including MDD, care-burden, coping style, and hopeful attitude. Postloss MDD was assessed 6 and 13 months after death, and a multivariate logistic regression analysis was conducted to identify its predictors. Result Of 305 caregivers contacted, 92 participated in this study. The prevalence of preloss MDD was 21.8%; the prevalences of postloss MDD were 34.8% and 24.7% at 6 and 13 months, respectively. Preloss MDD predicted postloss MDD at 6 months (odds ratio [OR] = 5.38, 95% confidence interval [CI95%] = 1.29, 22.43); preloss nonhopeful attitude and unemployment status of caregivers predicted postloss MDD at 13 months (OR = 8.77, CI95% = 1.87, 41.13 and OR = 7.10, CI95% = 1.28, 39.36, respectively). Significance of results Approximately 35% of caregivers suffered from MDD at 6 months postloss, but the prevalence of MDD decreased to about 25% at 13 months. Preloss MDD significantly predicted postloss MDD at 6 months, whereas hopeful attitude and unemployment at baseline were significantly associated with postloss MDD at 13 months.
Mcl‐1 targeting could be an intriguing perspective to cure cancer

The Bcl‐2 family, which plays important roles in controlling cancer development, is divided into antiapoptotic and proapoptotic members. The change in the balance between these members governs the life and death of the cells. Mcl‐1 is an antiapoptotic member of this family and its distribution in normal and cancerous tissues strongly differs from that of Bcl‐2. In human cancers, where upregulation of antiapoptotic proteins is common, Mcl‐1 expression is regulated independent of Bcl‐2 and its inhibition promotes senescence, a major barrier to tumorigenesis. Cancer chemotherapy determines various kinds of responses, such as senescence and autophagy; however, the ideal response to chemotherapy is apoptosis. Mcl‐1 is a potent oncogene that is regulated at the transcriptional, posttranscriptional, and posttranslational levels. Mcl‐1 is a short‐lived protein that, in the NH2 terminal region, contains sites for posttranslational regulation that can lead to proteasomal degradation. The USP9X Mcl‐1 deubiquitinase regulates Mcl‐1 and the levels of these two proteins are strongly correlated. Mcl‐1 has three splicing variants (the antiapoptotic protein Mcl‐1L and the proapoptotic proteins Mcl‐1S and Mcl‐1ES), each contributing toward apoptosis regulation. In cancers responsible for the most deaths in the world, the presence of Mcl‐1 is associated with malignant cell growth and evasion of apoptosis. Mcl‐1 is also one of the key regulators of cancer stem cells’ self‐renewal that contributes to tumor survival. A great number of indirect and selective Mcl‐1 inhibitors have been produced and some of these have shown efficacy in several clinical trials. Thus, therapeutic manipulation of Mcl‐1 can be a useful strategy to combat cancer.
Distinct domains of Escherichia coli IgaA connect envelope stress sensing and down-regulation of the Rcs phosphorelay across subcellular compartments

In enterobacteria, the Rcs system (Regulator of capsule synthesis) monitors envelope integrity and induces a stress response when damages occur in the outer membrane or in the peptidoglycan layer. Built around a two-component system, Rcs controls gene expression via a cascade of phosphoryl transfer reactions. Being particularly complex, Rcs also involves the outer membrane lipoprotein RcsF and the inner membrane essential protein IgaA (Intracellular growth attenuator). RcsF and IgaA, which are located upstream of the phosphorelay, are required for normal Rcs functioning. Here, we establish the stress-dependent formation of a complex between RcsF and the periplasmic domain of IgaA as the molecular signal triggering Rcs. Moreover, molecular dissection of IgaA reveals that its negative regulatory role on Rcs is mostly carried by its first N-terminal cytoplasmic domain. Altogether, our results support a model in which IgaA regulates Rcs activation by playing a direct role in the transfer of signals from the cell envelope to the cytoplasm. This remarkable feature further distinguishes Rcs from other envelope stress response systems.
Predicting the potential distributions of the invasive cycad scale Aulacaspis yasumatsui (Hemiptera: Diaspididae) under different climate change scenarios and the implications for management

Cycads are an ancient group of gymnosperms that are popular as landscaping plants, though nearly all of them are threatened or endangered in the wild. The cycad aulacaspis scale (CAS), Aulacaspis yasumatsui Takagi (Hemiptera: Diaspididae), has become one of the most serious pests of cycads in recent years; however, the potential distribution range and the management approach for this pest are unclear. A potential risk map of cycad aulacaspis scale was created based on occurrence data under different climatic conditions and topology factors in this study. Furthermore, the future potential distributions of CAS were projected for the periods 2050s and 2070s under three different climate change scenarios (GFDL-CM3, HADGEM2-AO and MIROC5) described in the Special Report on Emissions Scenarios of the IPCC (Intergovernmental Panel on Climate Change). The model suggested high environmental suitability for the continents of Asia and North America, where the species has already been recorded. The potential distribution expansions or reductions were also predicted under different climate change conditions. Temperature of Driest Quarter (Bio9) was the most important factor, explaining 48.1% of the distribution of the species. The results also suggested that highly suitable habitat for CAS would exist in the study area if the mean temperature of 15–20 °C in the driest quarter and a mean temperature of 25–28 °C the wettest quarter. This research provides a theoretical reference framework for developing policy to manage and control this invasive pest.
Malicious Code Detection based on Image Processing Using Deep Learning

In this study, we have used the Image Similarity technique to detect the unknown or new type of malware using CNN ap- proach. CNN was investigated and tested with three types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images that have been extracted from the same number of malware samples that come from 25 differ- ent malware families, and second was benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset vision research lab were initially exe- cutable files which were converted in to binary code and then converted in to image files. We obtained a testing ac- curacy of 98% on Vision Research dataset.
Integrating Authoritative and Volunteered Geographic Information for spatial planning

This contribution concerns ongoing research by the authors on the integrated use of Social Media Geographic Information (SMGI) and Authoritative Geographic Information (A-GI) as a support in urban and regional planning. Advances in Information and Communication Technologies (ICT) are fostering the production and the sharing of georeferenced user-generated contents, namely Volunteered Geographic Information (VGI) and SMGI, which may complement traditional spatial data sources. VGI is a voluntary contribution by users in order to collect or to disseminate geographic knowledge, while SMGI may be considered a deviation from VGI nature, due to the implicit and passive mode in disseminating geographic information, which is exclusively one embedded attribute of the main shared information. However, SMGI may offer unprecedented opportunities to investigate users’ needs, opinions, behaviors and movements, thus representing a potential support for analysis and decision-making in spatial planning. In this respect, the authors present an original tool called Spatext, which allows collection, management and analysis of SMGI in GIS environment, easing the integration of SMGI with official information. Afterwards, the opportunities for spatial planning arising from
Neurodevelopmental protein Musashi-1 interacts with the Zika genome and promotes viral replication

Inherited microcephaly exposes Zika culprit Microcephaly has been the terrifying hallmark of the recent outbreak of Zika virus (ZIKV) in the Americas. How the virus damages brain development in the fetus is enigmatic. Chavali et al. found that in congenital microcephaly, mutations in a neural precursor protein, Musashi-1 (MSI1), impede RNA binding to neural stem cell targets, resulting in abnormal brain development (see the Perspective by Griffin). MSI1 also binds ZIKV RNA to amplify viral replication in cells. This interaction could put a pregnant woman at risk of giving birth to a microcephalic child. Furthermore, MSI1 is expressed at high levels in the mouse testis, which may explain the sexual transmission of this virus. Science, this issue p. 83; see also p. 33 An inherited mutation fingerprints the molecular underpinnings of Zika–virus associated microcephaly. A recent outbreak of Zika virus in Brazil has led to a simultaneous increase in reports of neonatal microcephaly. Zika targets cerebral neural precursors, a cell population essential for cortical development, but the cause of this neurotropism remains obscure. Here we report that the neural RNA-binding protein Musashi-1 (MSI1) interacts with the Zika genome and enables viral replication. Zika infection disrupts the binding of MSI1 to its endogenous targets, thereby deregulating expression of factors implicated in neural stem cell function. We further show that MSI1 is highly expressed in neural progenitors of the human embryonic brain and is mutated in individuals with autosomal recessive primary microcephaly. Selective MSI1 expression in neural precursors could therefore explain the exceptional vulnerability of these cells to Zika infection.
Nd : Ta 2 O 5 Rib Waveguide Lasers

Ta2O5 waveguides offer great potential for high-density active photonic crystal circuits and their combination with rare-earth dopants for active devices is of interest for increasing their potential functionality. To this end, neodymium-doped Ta2O5 rib waveguide lasers have been fabricated on an oxidized silicon wafer by RF sputtering and argon ion-beam milling and the first laser action in this material has been demonstrated. Lasing was observed at wavelengths between 1060nm and 1080nm and an absorbed pump power threshold of 87mW was obtained. * Now with: Faculty of Health and Life Sciences, De Montfort University, Leicester, LE1 9BH, UK.
EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery

In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on a corpus to extract candidate hypernyms and represent their dependency paths as a feature vector. The feature vector is concatenated with a feature vector obtained using Wikipedia pre-trained term embedding model. The concatenated feature vector fits a supervised machine learning method to learn a classifier model. This model is able to classify new candidate hypernyms as hypernym or not. Our system performs well to discover new hypernyms not defined in gold hypernyms.