Miracle-dz/UniFAR-FTUs huggingface dataset:
A longitudinal study of premorbid IQ Score and risk of developing schizophrenia, bipolar disorder, severe depression, and other nonaffective psychoses.

CONTEXT Longitudinal studies indicate that a lower IQ score increases risk of schizophrenia. Preliminary evidence suggests there is no such effect for nonpsychotic bipolar disorder. To our knowledge, there are no prior population-based, longitudinal studies of premorbid IQ score and risk of developing severe depression requiring hospital admission. OBJECTIVES To investigate the association between premorbid IQ score and risk of developing schizophrenia, other nonaffective psychoses, bipolar disorder, and severe depression and to investigate effects of confounding and examine possible causal pathways by which IQ may alter these risks. DESIGN Historical cohort study, using record linkage for hospital admissions during a 27-year follow-up period. SETTING Survey of Swedish conscripts (1969-1970). PARTICIPANTS Population-based sample of 50,087 male subjects. Data were available on IQ score at conscription and on other social and psychological characteristics. MAIN OUTCOME MEASURES International Classification of Diseases, Eighth Revision or Ninth Revision diagnoses of schizophrenia, bipolar disorder, severe depression, and other nonaffective psychoses. RESULTS There was no association between premorbid IQ score and risk of bipolar disorder. Lower IQ was associated with increased risk of schizophrenia, severe depression, and other nonaffective psychoses. Risk of schizophrenia was increased in subjects with average IQ compared with those with high scores, indicating that risk is spread across the whole IQ range. CONCLUSIONS Lower IQ score was associated with increased risk for schizophrenia, severe depression, and other nonaffective psychoses, but not bipolar disorder. This finding indicates that at least some aspects of the neurodevelopmental etiology of bipolar disorder may differ from these other disorders.
Techies as nontechnological factors in software engineering?

The author discusses human issues in software engineering and considers two technological factors that offered dramatic productivity and quality growth in the last decade. The individual differences in the performance range among software engineers are reviewed. Steps to reduce the wide variation in performance among individuals are considered. The first technological factor is having bigger machines with more memory that allowed software engineers more time to work on the task rather than wrestling with machine limitations that inhibited the task. The second technological factor was Lisp machines and the powerful programming environments that accompanied them. The author observes that hardware will still play a dramatic role in productivity growth, but that nontechnological market factors will limit the extent to which the best software ideas will be translated into industry-wide advances. <<ETX>>
Classifier Based Machine Comprehension

In this report we implement a machine comprehension system, and train and test it on the MCTest dataset (Richardson et al., 2013). We treat it as a classification problem. We use baseline features and syntactic features to compute the score for each candidate answers. We also used a set of NLP techniques, including word embedding, coreference resolution and lemmatization to improve the performance of our features. We train our system using a max-margin loss function with a latent variable. Our result shows significant improvement over the original baseline.
Accuracy of i-Scan for Optical Diagnosis of Colonic Polyps: A Meta-Analysis

Background i-Scan is a novel virtual chromoendoscopy system designed to enhance surface and vascular patterns to improve optical diagnostic performance. Numerous prospective studies have been done to evaluate the accuracy of i-Scan in differentiating colonic neoplasms from non-neoplasms. i-Scan could be an effective endoscopic technique for optical diagnosis of colonic polyps. Objective Our aim of this study was to perform a meta-analysis of published data to establish the diagnostic accuracy of i-Scan for optical diagnosis of colonic polyps. Methods We searched PubMed, Medline, Elsevier ScienceDirect and Cochrane Library databases. We used a bivariate meta-analysis following a random effects model to summarize the data and plotted hierarchical summary receiver-operating characteristic (HSROC) curves. The area under the HSROC curve (AUC) serves as an indicator of the diagnostic accuracy. Results The meta-analysis included a total of 925 patients and 2312 polyps. For the overall studies, the area under the HSROC curve was 0.96. The summary sensitivity was 90.4% (95%CI 85%-94.1%) and specificity was 90.9% (95%CI 84.3%-94.9%). In 11 studies predicting polyps histology in real-time, the summary sensitivity and specificity was 91.5% (95%CI 85.7%-95.1%) and 92.1% (95%CI 84.5%-96.1%), respectively, with the AUC of 0.97. For three different diagnostic criteria (Kudo, NICE, others), the sensitivity was 86.3%, 93.0%, 85.0%, respectively and specificity was 84.8%, 94.4%, 91.8%, respectively. Conclusions Endoscopic diagnosis with i-Scan has accurate optical diagnostic performance to differentiate neoplastic from non-neoplastic polyps with an area under the HSROC curve exceeding 0.90. Both the sensitivity and specificity for diagnosing colonic polyps are over 90%.
Learning Business Process Management through Serious Games: Feedbacks on the Usage of INNOV8

Business processes play a major role in the Information System (IS) domain. Business Process Management (BPM) approaches offer languages, methods and tools for modeling, execution and optimizing business processes. However, these approaches are often difficult to understand by the learners. In particular, the modeling phase often remains abstract, not consensual and complex to implement in real settings. In order ato better motivate BPM learners, several BPM's Serious Games (SG) has been developed in recent years. But few studies have evaluated game based educational methods for the BPM learning. This paper presents a feedback on the SG INNOV8 use in BPM teaching. INNOV8's evaluation was conducted within the course: "Information System Design," proposed to Master students. The feedback from the students and teachers were quite positive. Nevertheless, the study shows a low potential of the game for novice students in this field.
New Records of Xylaria Species in Korea: X. ripicola sp. nov. and X. tentaculata

Abstract During a Korean mushroom diversity survey from 2011 to 2014, we found one new Xylaria species (X. ripicola sp. nov.) and one Xylaria species that had not been previously observed in Korea (X. tentaculata). To confirm the phylogenetic placement of the new species, we conducted a phylogenetic investigation based on internal transcribed spacer regions of ribosomal DNA sequences. Additionally, the new species, X. ripicola, was subsequently analyzed for RNA polymerase II subunit sequences. We also evaluated the macroscopic and microscopic features of this species. Herein, X. ripicola is described as a new species that was collected from a natural beach habitat and X. tentaculata is formally reported as newly found in Korea.
A Hybrid Resonant Converter Utilizing a Bidirectional GaN AC Switch for High-Efficiency PV Applications

This paper introduces a novel isolated hybrid resonant converter with smooth transition between multiple operating modes. With the simple addition of a bidirectional ac switch, the highly efficient series resonant converter operating in the discontinuous conduction mode is combined with a phase-shifted full-bridge buck converter and a pulsewidth-modulated boost converter in order to provide high power conversion efficiency over a wide input-voltage operating range utilizing a simple topology and simple control techniques. First, the topology is introduced, and the converter's operating modes are discussed. Next, closed-loop input-voltage controllers are designed for the different operating modes, and a smooth transition technique is introduced using a two-carrier modulation scheme. Experimental results are provided to verify the proposed system using a 300-W prototype that achieved a 97.5% California Energy Commission weighted efficiency with a 30-V input including all auxiliary and control losses.
K-maximin clustering: a maximin correlation approach to partition-based clustering

We propose a new clustering algorithm based upon the maximin correlation analysis (MCA), a learning technique that can minimize the maximum misclassification risk. The proposed algorithm resembles conventional partition clustering algorithms such as k-means in that data objects are partitioned into k disjoint partitions. On the other hand, the proposed approach is unique in that an MCA-based approach is used to decide the location of the representative point for each partition. We test the proposed technique with typography data and show our approach outperforms the popular k-means and k-medoids clustering in terms of retrieving the inherent cluster membership.
An avian live attenuated master backbone for potential use in epidemic and pandemic influenza vaccines

The unprecedented emergence in Asia of multiple avian influenza virus (AIV) subtypes with a broad host range poses a major challenge in the design of vaccination strategies that are both effective and available in a timely manner. The present study focused on the protective effects of a genetically modified AIV as a source for the preparation of vaccines for epidemic and pandemic influenza. It has previously been demonstrated that a live attenuated AIV based on the internal backbone of influenza A/Guinea fowl/Hong Kong/WF10/99 (H9N2), called WF10att, is effective at protecting poultry species against low- and high-pathogenicity influenza strains. More importantly, this live attenuated virus provided effective protection when administered in ovo. In order to characterize the WF10att backbone further for use in epidemic and pandemic influenza vaccines, this study evaluated its protective effects in mice. Intranasal inoculation of modified attenuated viruses in mice provided adequate protective immunity against homologous lethal challenges with both the wild-type influenza A/WSN/33 (H1N1) and A/Vietnam/1203/04 (H5N1) viruses. Adequate heterotypic immunity was also observed in mice vaccinated with modified attenuated viruses carrying H7N2 surface proteins. The results presented in this report suggest that the internal genes of a genetically modified AIV confer similar protection in a mouse model and thus could be used as a master donor strain for the generation of live attenuated vaccines for epidemic and pandemic influenza.
TLR Ligand-Induced Type I IFNs Affect Thymopoiesis1

The interactions between TLRs and their ligands have profound immune modulation properties. Attention has focused mostly on the impact of TLR ligands on peripheral innate and adaptive immunity during viral infections, whereas little impact of TLR activation has been shown on thymic development. Here we show that treatment of murine fetal thymic organ cultures (FTOCs) with TLR3 or TLR7 ligands induced rapid expression of IFN-α and -β mRNA, hallmarks of acute and chronic viral infections. This resulted in an early developmental blockade, increased frequencies of apoptotic cells, and decreased proliferation of thymocytes, which led to an immediate decrease in cellularity. FTOCs infected with vesicular stomatitis virus, known to act through TLR7, were similarly affected. Down-regulation of IL-7R α-chain expression, together with an increased expression of suppressor of cytokine signaling-1 and a concomitant decreased expression of the transcriptional regulator growth factor independence 1 were observed in TLR ligands or IFN-treated FTOCs. This indicates a role for these pathways in the observed changes in thymocyte development. Taken together, our data demonstrate that TLR activation and ensuing type I IFN production exert a deleterious effect on T cell development. Because TLR ligands are widely used as vaccine adjuvants, their immunomodulatory actions mediated mainly by IFN-α suggested by our results should be taken in consideration.
Pycnogenol Attenuates the Release of Proinflammatory Cytokines and Expression of Perilipin 2 in Lipopolysaccharide-Stimulated Microglia in Part via Inhibition of NF-κB and AP-1 Activation

Over activation of microglia results in the production of proinflammatory agents that have been implicated in various brain diseases. Pycnogenol is a patented extract from French maritime pine bark (Pinus pinaster Aiton) with strong antioxidant and anti-inflammatory potency. The present study investigated whether pycnogenol may be associated with the production of proinflammatory mediators in lipopolysaccharide-stimulated BV2 (mouse-derived) microglia. It was found that pycnogenol treatment was dose-dependently associated with significantly less release of nitricoxide (NO), TNF-α, IL-6 and IL-1β, and lower levels of intercellular adhesion molecule1 (ICAM-1) and perilipin 2 (PLIN2). Furthermore, this effect was replicated in primary brain microglia. Levels of inducible NO synthase mRNA and protein were attenuated, whereas there was no change in the production of the anti-inflammatory cytokine IL-10. Further evidence indicated that pycnogenol treatment led to the suppression of NF-κB activation through inhibition of p65 translocation into the nucleus and inhibited DNA binding of AP-1, suggesting that these proinflammatory factors are associated with NF-κB and AP-1. We conclude that pycnogenol exerts anti-inflammatory effects through inhibition of the NF-κB and AP-1pathway, and may be useful as a therapeutic agent in the prevention of diseases caused by over activation of microglia.
Flexible Object Based Filesystems for Scientific Computing Flexible Object Based Filesystems for Scientific Computing Abstract

by Christopher M. Moretti Object storage has traditionally been seen only in low level interfaces, visible only to kernels and filesystem code. However, if storage objects are made visible across a distributed system, it dramatically simplifies the construction of large storage systems that are flexible, expandable, and migrateable. The FOBS filesystem, consisting of a simple metadata layer containing pointers to storage objects on remote filesystems, demonstrates this concept. The layer of indirection allows for filesystems larger than any single disk, permits multiple filesystems to share common objects, and enables users to create and manage private namespaces. Experimentally, this work demonstrates that the overhead of indirection is low, a single client can write faster than disk speed, and multiple clients can harness aggregate disk throughput. FOBS scientific application is examined with case study of the filesystem as employed by a high energy physics experiment on a cluster of 32 nodes for over one year.
Eliciting Structured Knowledge from Situated Crowd Markets

We present a crowdsourcing methodology to elicit highly structured knowledge for arbitrary questions. The method elicits potential answers (“options”), criteria against which those options should be evaluated, and a ranking of the top “options. ” Our study shows that situated crowdsourcing markets can reliably elicit/moderate knowledge to generate a ranking of options based on different criteria that correlate with established online platforms. Our evaluation also shows that local crowds can generate knowledge that is missing from online platforms and on how a local crowd perceives a certain issue. Finally, we discuss the benefits and challenges of eliciting structured knowledge from local crowds.
Stimulant Medication and Prefrontal Functional Connectivity During Working Memory in ADHD

Objective: Recent theoretical and empirical work suggests that while unmedicated, children with ADHD have a deficit in subcortical processing that leads to greater and more varied prefrontal cortical (PFC) activation, compared to (a) age-matched control participants and (b) their own brain activity while on stimulant medication. This pattern has been described elsewhere as inefficient. Method: Functional magnetic resonance imaging (fMRI) and functional connectivity analyses were used during a working memory task for five female adolescents with ADHD, aged 11 to 17 years, both on and off their usual dose of stimulant medication. Results: On medication, adolescents with ADHD demonstrated less PFC activation and less functional connectivity between frontal and subcortical regions compared to off medication. Conclusions: Because of the small sample size, results are presented as preliminary findings which await replication in a larger sample. However, these findings lend support to the idea that remediation of inefficiencies in PFC function for individuals with ADHD by stimulant medication may be related, in part, to frontal-subcortical connectivity. (J. of Att. Dis. 2010; 14(1) 69-78)
Formative assessment: the effect on academic achievement and attitudes toward science

Susanne Casey FORMATIVE ASSESSMENT: THE EFFECT ON ACADEMIC ACHIEVEMENT AND ATTITUDES TOWARD SCIENCE 2004/05 Dr. Catherine Yang Master of Arts-Subject Matter Teaching Physical Science The purposes of this study were to (a) determine if the use of formative assessment in the science classroom improved academic achievement in a group of ninth grade honors level students; and (b) determine the effect of formative assessment on the attitudes these students have toward science. Data was collected from two control groups (N=24 and N=26) and the experimental, comparison group (N=28). Students were administered two Likert-type surveys, an attitude survey and a causal attribution survey. Each survey was given at the start of the experiment and again at the end of the experiment. Summative test scores were also collected over the course of the investigation. The mean and standard deviation for summative test scores were compared. Analysis of the attitude surveys was done using an independent-sample t-test and a Bonferroni One-way Analysis of Variance (ANOVA). While the analysis of summative test scores did not show an overall increase in scores, there was a narrowing of the gap between high and low achievers which may demonstrate a relationship between achievement and formative assessment. Most results of the attitude and causal attribution surveys were not statistically significant. The attitude survey showed a possible relationship between the use of formative assessment and improved attitudes toward the usefulness of science. Results of the causal attribution survey showed a possible relationship between the use of formative assessment and the attribution of success in science to ability and effort. These attributions are associated with facilitated learning experiences. TABLE OF CONTENTS Page Chapter
Differentially Private Frequent Itemset Mining via Transaction Splitting

Recently, there has been a growing interest in designing differentially private data mining algorithms. Frequent itemset mining (FIM) is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database. For a given database, the preprocessing phase needs to be performed only once. In the mining phase, to offset the information loss caused by transaction splitting, we devise a run-time estimation method to estimate the actual support of itemsets in the original database. In addition, by leveraging the downward closure property, we put forward a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the mining process. Through formal privacy analysis, we show that our PFP-growth algorithm is ε-differentially private. Extensive experiments on real datasets illustrate that our PFP-growth algorithm substantially outperforms the state-of-the-art techniques.
Ashli - Advanced Shading Language Interface

The support for IEEE floating point computation and the exposure of shading functionality in a standardize API form have made graphics hardware a viable workflow solution to an artist involved in digital content creation (DCC). Nevertheless, there still remains a significant interface void between the abstract shading description an artist is used to and the low level shading constructs the hardware expects. We have developed Ashli, an advanced shading language interface tool, with the primary motivation to bridge the interface gap fore mentioned. Ashli is developed in the form of a case study. It takes in high level shading languages and descriptions and at the end emits standard graphics hardware shading API (e.g. Microsoft DirectX and OpenGL). Ashli‘s main contribution is in its seamless cooperation with the DCC graphics application. The RenderMan® Shading Language, Maya® Shading Network and 3ds Max® Standard Materials are the subset of input abstraction we have experimented with to validate our approach. Shading path computation complexity and conformance to hardware resource constraints are owned by Ashli and for the most part made transparent to the artist. A tool like Ashli essentially retains rendered image appearance quality comparable to the level produced by a software renderer, but at a significant higher rate of interaction efficiency. We present Ashli in the context of a stand-alone viewing application framework, depicting shading path computation bound to scene rendering. We demonstrate the application design and results for using Ashli as a shading coprocessor assist.
Recent Developments in Cloud Based Systems: State of Art

Cloud computing is the new buzzword in the head of the techies round the clock these days. The importance and the different applications of cloud computing are overwhelming and thus, it is a topic of huge significance. It provides several astounding features like Multitenancy, on demand service, pay per use etc. This manuscript presents an exhaustive survey on cloud computing technology and potential research issues in cloud computing that needs to be addressed.
Flattening an object algebra to provide performance

Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent results.