Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Heterogeneity in a Patient-Derived Glioblastoma Xenoline Is Regulated by Different Cancer Stem Cell Populations

Malignant glioblastoma (GBM) is a highly aggressive brain tumor with a dismal prognosis and limited therapeutic options. Genomic profiling of GBM samples has identified four molecular subtypes (Proneural, Neural, Classical and Mesenchymal), which may arise from different glioblastoma stem-like cell (GSC) populations. We previously showed that adherent cultures of GSCs grown on laminin-coated plates (Ad-GSCs) and spheroid cultures of GSCs (Sp-GSCs) had high expression of stem cell markers (CD133, Sox2 and Nestin), but low expression of differentiation markers (βIII-tubulin and glial fibrillary acid protein). In the present study, we characterized GBM tumors produced by subcutaneous and intracranial injection of Ad-GSCs and Sp-GSCs isolated from a patient-derived xenoline. Although they formed tumors with identical histological features, gene expression analysis revealed that xenografts of Sp-GSCs had a Classical molecular subtype similar to that of bulk tumor cells. In contrast xenografts of Ad-GSCs expressed a Mesenchymal gene signature. Adherent GSC-derived xenografts had high STAT3 and ANGPTL4 expression, and enrichment for stem cell markers, transcriptional networks and pro-angiogenic markers characteristic of the Mesenchymal subtype. Examination of clinical samples from GBM patients showed that STAT3 expression was directly correlated with ANGPTL4 expression, and that increased expression of these genes correlated with poor patient survival and performance. A pharmacological STAT3 inhibitor abrogated STAT3 binding to the ANGPTL4 promoter and exhibited anticancer activity in vivo. Therefore, Ad-GSCs and Sp-GSCs produced histologically identical tumors with different gene expression patterns, and a STAT3/ANGPTL4 pathway is identified in glioblastoma that may serve as a target for therapeutic intervention.
An imaging polarimeter (IMPOL) for multi-wavelength observations

Taking advantage of the advances in array detector technology, an imaging polarimeter (IMPOL) has been constructed for measuring linear polarization in the wavelength band from 400-800 nm. It makes use of a Wollaston prism as the analyzer to measure simultaneously the two orthogonal polarization components that define a Stoke's parameter. An achromatic half-wave plate is used to rotate the plane of polarization with respect to the axis of the analyzer so that the second Stoke's parameter also can be determined. With a field of view corresponding to about 30×30 mm 2 for a O20D 1.2 m, f/13 telescope, a sensitive, liquid-N 2 cooled CCD camera as the detector and a built-in acquisition and guidance unit, the instrument can be used for studying stellar fields or extended objects with an angular resolution of ~2''. The instrumental polarization is less than 0.05% and the accuracies of measurement are primarily limited by photon noise for typical observations.
A new video denoising method using texture metric and adaptive structure variance

In this paper, an innovated method is proposed for video denoising. The method consists of two major procedures. First, a new adaptive superpixel video texture metric is proposed. This video texture metric is calculated, which relates to different parts of a video stream. Then a new adaptive structure variance is estimated by adopting fine and coarse structures. Finally, a noise filter based on the estimated weights of different structures in a video stream is applied. By comparison, the proposed method outperforms traditional state-of-art methods, especially in block artifacts reduction.
ec(h)o: situated play in a tangible and audio museum guide

In this paper we discuss an adaptive museum guide prototype in which playfulness is a key design goal for the interaction experience. The interface for our prototype is a combined tangible user interface and audio display. We discuss how we determined the specific requirements for play through an ethnographic study and analysis based on ecological concepts of Bell and Nardi & O'Day. We found that we could consider play in two main forms in regard to the interface: content and physical play. We also found that play is highly contextual. Designers need to consider the situated nature of play for two reasons: 1) to best serve the overall design purpose; 2) in order to understand the nature and degree of play required. We augmented traditional user experience evaluation methods of questionnaires and interviews with observational analysis based on Djajadiningrat's descriptions of aesthetic interaction.
Design and Realization of Delay Mapping Receiver Based on GPS for Sea Surface Wind Measurement

The delay mapping receiver (DMR) is for receiving and processing the reflected GPS signal to get the information of wind of sea surface by recording the reflected GPS signal and matching it to the theoretical model. The hardware structure, software design and difference from normal GPS receiver are introduced in this paper. The test results at near sea of Tianjin of China are provided, which prove the design of DMR is successful and the collected data are useful for the sea surface wind measurement
A new algorithm for Arabic optical character recognition

In this paper we present a new approach for designing an Optical Character Recognition (OCR) system for Arabic Alphabet. Our approach addresses mere text images; using customized techniques. We have divided the problem at hand into three phases: preprocessing and line extraction, segmentation, and character recognition. Extensive histogram and thresh-holding were considered in line extraction, image filtering and noise reduction. Main line algorithm was proposed for segmentation, and a neural network was used for character recognition. The segmentation algorithm is based on a new and novel idea that exploits the nature of the Arabic language. It introduces the concept of the main line to tokenize the text, and it generates a set of 33 different tokens that represent the 28 Arabic characters and their different shapes and variation. The system generates tokens from the text and compares them with the reference tokens. Experimental results illustrate the effectiveness of the system using different font types and sizes with an average recognition rate of 87%.
Recovery in schizophrenia: related factors in a group of patients followed up for 5 years in a private practice

ABSTRACT OBJECTIVE: The aim of this study was to determine recovery rates and related factors in schizophrenia outpatients treated by the same physician in a private practice. METHODS: Seventy patients with schizophrenia and schizoaffective disorder who followed up for 5 years were examined retrospectively. Psychopathological status was evaluated by means of standardized symptomatic remission criteria, and the Clinical Global Impression-Severity. Functionality of the patients was evaluated using the Global Assessment of Functioning. Recovery required remission of positive and negative symptoms and adequate social/vocational functioning for at least 24 months. RESULTS: In a 5-year period, 35.7% of the patients achieved recovery criteria. There were no demographic and clinical differences between patients with schizophrenia and schizoaffective disorder. Recovery was predicted by the onset of illness (OR 1.14, 95% CI 1.01–1.30, p = .044) and type of illness – schizoaffective disorder (OR 6.84, 95% CI 1.54–30.34, p = .011). More than half of the patients were taking intramuscular injection mostly depot antipsychotic drugs at any time of the treatment period. CONCLUSIONS: These favourable findings might be interpreted as results of the treatment of the same physician, intramuscular injection applications, and regular or when needed meetings. Illness type of schizoaffective disorder and late onset of illness are predictive factors for recovery. Increased therapeutic alliance and treatment adherence might foster the recovery of schizophrenia.
Forecasting of Indian Stock Market by Effective Macro- Economic Factors and Stochastic Model

The stock market patterns are non-linear in nature therefore it is difficult to forecast the future trends of the market. In this paper we have used different macro-economic factors of Indian stock market. Macro-economic factors include technical indicators. These technical indicators help to decide the patterns of the market at a particular time. There are hundreds of technical indicators are available, but all technical indicators are not useful. So we have obtained most effective technical indicators by applying Principal Component Analysis (PCA). Selected technical indicators are taken as input variable. Future prices are found through Hidden Markov Model (HMM). Hidden Markov Model is a very powerful stochastic model. In literature survey it was found that HMM gives better accuracy than other models. On the basis of experiment it was found that HMM with PCA performed well and gives Mean Absolute Percentage Error (MAPE) 1.77%.
An in-vehicle tracking method using vehicular ad-hoc networks with a vision-based system

Vehicle tracking is an important issue in intelligent vehicle automation systems since it can be used to increase safety, convenience and efficiency in driving. Many of the methods for vehicle tracking use a vision-based system to recognize the neighboring vehicles and provide a real time map of nearby vehicles. In other methods, wireless communication between vehicles has been used to locate the vehicles within range and provide tracking information for driving assistance applications. In this paper we present a combination of both a vision-based system and a wireless based system to provide more accurate real-time information about neighboring vehicles. We assume that some of the vehicles are equipped with GPS receivers, a Dedicated Short Range Communication (DSRC) transceiver and one or more cameras mounted on the vehicle. This tracking method has been implemented and evaluated in urban, highway and intersection scenarios under different adoption rates. The results show that a combined approach can be more effective.
Study on Fiber Orientation of Short-Fiber-Reinforced Rubber Matrix Sealing Composites Based on Digital Image Processing

The short fiber orientation has significant influence on the behaviors of short fiber reinforced rubber matrix composite (SFRC). By using slicing method in deep cooling environment, the smooth and flat cross section of SFRC sample was obtained at-150¿ and the micrograph of the cross section was taken by using a high magnified microscope. The digital image processing software was developed to recognize the elliptical profile of the short fiber cross section from its background. Then the oriented angle of the short fiber can be determined according to the major axis and minor axis of the elliptical profile. The oriented angle distribution of short fibers was statistically analyzed by using histogram method and characterized by modified coefficient. The investigation results indicate that the oriented angle of short fiber in SFRC distributes symmetrically about 0° approximately, and it tends to centralized distribution with the increasing short fiber content and the initial fiber length.
Handwritten Character Recognition Using Competitive Neural Trees

Neural network classifier methods and decision trees are widely used in various pattern recognition research areas. Among them, handwritten character recognition still faces some issues in all languages. Myanmar handwritten character recognition based on Competitive Neural Trees (CNeT) is proposed in this paper. CNeT performs hierarchical classification and apply competitive unsupervised learning at node label. The goals of Myanmar handwritten character recognition are to obtain better recognition accuracy rate and robust in geometric character shapes of different writing styles. Three main steps such as preprocessing, shape feature descriptors extraction and recognition are implemented in our experiment. Shape feature descriptors are extracted from preprocessed images which are used in Competitive Neural Trees (CNeT) for recognition. This paper discusses a global search method for the CNeT, which is utilized for training.
A low–phase–noise 5GHz quadrature CMOS VCO using common–mode inductive coupling

A new concept for quadrature coupling of LC oscillators is introduced and demonstrated on a 5GHz CMOS VCO. It uses injection-locking through common-mode inductive coupling to enforce quadrature. The technique provides quadrature over a wide tuning range without introducing any phase noise- or power consumption increase. The realized VCO is tunable between 4.6GHz and 5.2GHz and measures a phase noise lower than -124dBc/Hz at 1MHz offset over the entire tuning range. The circuit draws 8.75mA from a 2.5V supply.
LDA Based Related Word Detection in Advertising

In this paper, we propose a new method for related word detection in Advertising by combining LDA topic model and word co-occurrence. We use a corpus of BaiduBaike, which is a Chinese Encyclopedia, to calculate the word co-occurrence. Words allocation on topics driven by LDA is used to sort the related words glossary which is obtained by the traditional co-occurrence procedure. We evaluate our method on advertising related word recognition, and the experiments result shows that the method is feasible.
Subject-Based Evaluation Measures for Interactive Spoken Language Systems

The DARPA Spoken Language effort has profited greatly from its emphasis on tasks and common evaluation metrics. Common, standardized evaluation procedures have helped the community to focus research effort, to measure progress, and to encourage communication among participating sites. The task and the evaluation metrics, however, must be consistent with the goals of the Spoken Language program, namely interactive problem solving. Our evaluation methods have evolved with the technology, moving from evaluation of read speech from a fixed corpus through evaluation of isolated canned sentences to evaluation of spontaneous speech in context in a canned corpus. A key component missed in current evaluations is the role of subject interaction with the system. Because of the great variability across subjects, however, it is necessary to use either a large number of subjects or a within-subject design. This paper proposes a within-subject design comparing the results of a software-sharing exercise carried out jointly by MIT and SRI.
An evolutionary model with myopic learning

We examine a dynamical system consisting of two distinct, but interactive, subsystems, namely population dynamics and learning dynamics. The population dynamics formalize that the population shares of fitter groups increase relatively to those of less fit groups. The learning dynamics describe that each subgroup adapts its strategy, by placing more weight on activities contributing more than average to its fitness, meanwhile decreasing weights on activities contributing less than average.
Accurate and scalable nearest neighbors in large networks based on effective importance

Nearest neighbor proximity search in large graphs is an important analysis primitive with a variety of applications in graph data from different domains. We propose a novel proximity measure for weighted graphs called Effective Importance which incorporates multiple paths between nodes and captures the inherent structural clusters within a network. We develop effective bounds on the EI value using a modified small subnetwork around a query node, enabling scalable exact nearest neighbor (NN) search at query time. Our NN search does not require heavy offline analysis or holistic knowledge of the graph, making our method suitable for very large dynamically changing networks or composite network overlays. We employ our NN search algorithm on social, information and biological networks and demonstrate the effectiveness and scalability of the approach. For million-node networks, our method retrieves the exact top 20 neighbors using less than $0.2%$ of the network edges in a fraction of a second on a conventional desktop machine. We also evaluate the effectiveness of our proximity measure and NN search for three applications, namely (i) finding good local clusters, (ii) network sparsification and (iii) prediction of node attributes in information networks. The EI measure and NN search method outperform recent counterparts from the literature in all applications.
Mutation of a putative S-nitrosylation site of TRPV4 protein facilitates the channel activates

The transient receptor potential vanilloid 4 (TRPV4) cation channel, a member of the TRP vanilloid subfamily, is expressed in a broad range of tissues. Nitric oxide (NO) as a gaseous signal mediator shows a variety of important biological effects. In many instances, NO has been shown to exhibit its activities via a protein S-nitrosylation mechanism in order to regulate its protein functions. With functional assays via site-directed mutagenesis, we demonstrate herein that NO induces the S-nitrosylation of TRPV4 Ca2+channel on the Cys853 residue, and the S-nitrosylation of Cys853 reduced its channel sensitivity to 4-α phorbol 12,13-didecanoate and the interaction between TRPV4 and calmodulin. A patch clamp experiment and Ca2+ image analysis show that the S-nitrosylation of Cys853 modulates the TRPV4 channel as an inhibitor. Thus, our data suggest a novel regulatory mechanism of TRPV4 via NO-mediated S-nitrosylation on its Cys853 residue.
General Features in Knowledge Tracing: Applications to Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge

Knowledge Tracing is the de-facto standard for inferring student knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specic feature of interest. For example, variants have studied the eect of students’ individual characteristics, the eect of help in a tutor, and subskills. These ad hoc models are successful for their own specic purpose, but are specied to only model a single specic feature. We present FAST (Feature Aware Student knowledge Tracing), an ecient, novel method that allows integrating general features into Knowledge Tracing. We demonstrate FAST’s exibility with three examples of feature sets that are relevant to a wide audience. We use features in FAST to model (i) multiple subskill tracing, (ii) a temporal Item Response Model implementation, and (iii) expert knowledge. We present empirical results using data collected from an Intelligent Tutoring System. We report that using features can improve up to 25% in classication performance of the task of predicting student performance. Moreover, for tting
Unveiling a socio-demographic portrait of Canadian men's mental health : exploring the intersectional impact of social hierarchies on depression and suicidal ideation among Canadian men

The intersectional impact of multiple social gradients on major depression (MD) and lifetime suicidal ideation (LSI) among Canadian men was explored through statistical analysis of four years of Canadian Community Health Survey data spanning 2009-2012 (n=124 000). 4.1% of Canadian men demonstrated MD, with significant variation across sub-groups of men. Significant predictors of MD included low self-esteem, current unemployment, low social support, being a sexual minority, and younger age. The effect of resource inequalities on MD were fully mediated by self-esteem, with the exception of social support, which exerted both a direct and indirect effect on depression predicted probability (DPP), while also moderating the effect of self-esteem on DPP. Significant predictors of LSI included low self-esteem, low social support, and visible minority status. Low standing on a single social gradient only predicted of MD or LSI, in the presence of high covariance contribution related to poor standing on multiple gradients.
Fictionalism about musical works

Abstract The debate concerning the ontological status of musical works is perhaps the most animated debate in contemporary analytic philosophy of music. In my view, progress requires a piecemeal approach. So in this article I hone in on one particular musical work concept – that of the classical Western art musical work; that is, the work concept that regulates classical art-musical practice. I defend a fictionalist analysis – a strategy recently suggested by Andrew Kania as potentially fruitful – and I develop a version of such an analysis in line with a broad commitment to philosophical naturalism.
Quantum Computing and the Hunt for Hidden Symmetry

In 1994, Peter Shor gave e cient quantum algorithms for factoring integers and extracting discrete logarithms [20]. If we believe that nature will permit us to faithfully implement our current model of quantum computation, then these algorithms dramatically contradict the Strong Church-Turing thesis. The e ect is heightened by the fact that these algorithms solve computational problems with long histories of attention by the computational and mathematical communities alike. In this article we discuss the branch of quantum algorithms research arising from attempts to generalize the core quantum algorithmic aspects of Shor's algorithms. Roughly, this can be viewed as the problem of generalizing algorithms of Simon [21] and Shor [20], which work over abelian groups, to general nonabelian groups. The article is meant to be self-contained, assuming no knowledge of quantum computing or the representation theory of nite groups. We begin in earnest in Section 2, describing the problem of symmetry nding : given a function f : G → S on a group G, this is the problem of determining {g ∈ G | ∀x, f(x) = f(gx)}, the set of symmetries of f . We switch gears in Section 3, giving a short introduction to the circuit model of quantum computation. The connection between these two sections is eventually established in Section 4, where we discuss the representation theory of nite groups and the quantum Fourier transform a unitary transformation speci cally tuned to the symmetries of the underlying group. Section 4.2 is devoted to Fourier
Updating high average-utility itemsets in dynamic databases

In this paper, a maintenance algorithm for average-utility mining is proposed to update derived high average-utility itemsets in dynamic databases. It first calculates the count difference of modified itemsets and then partitions them into four parts according to whether they are high upper-bound average-utility itemsets in the original database and whether their count difference is positive or negative. Each part is then processed in its own way. Experimental results show the proposed maintenance algorithm runs faster than the two-phase approach for mining high average-utility itemsets in dynamic databases.
Commonality in Various Design Science Methodologies

Based on reviewing foremost literature, the paper discusses various design science research methodologies along with their case studies. It concentrates on activities (tools, methods, actions) that are used while constructing an artefact. We have identified common activities occurring across ‘design’ steps, which were not indicated in their methodologies. Combining them and drawing on that finding, we propose a concept of reference model, which gives more insights and additionally dissipates design science high level of abstraction.