Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms of Neuroblastoma Cell Growth Inhibition by CARP-1 Functional Mimetics

Neuroblastomas (NBs) are a clinically heterogeneous group of extra cranial pediatric tumors. Patients with high-risk, metastatic NBs have a long-term survival rate of below 40%, and are often resistant to current therapeutic modalities. Due to toxic side effects associated with radiation and chemotherapies, development of new agents is warranted to overcome resistance and effectively treat this disease in clinic. CARP-1 functional mimetics (CFMs) are an emerging class of small molecule compounds that inhibit growth of diverse cancer cell types. Here we investigated NB inhibitory potential of CFMs and the molecular mechanisms involved. CFM-1, -4, and -5 inhibited NB cell growth, in vitro, independent of their p53 and MYCN status. CFM-4 and -5 induced apoptosis in NB cells in part by activating pro-apoptotic stress-activated kinases (SAPKs) p38 and JNK, stimulating CARP-1 expression and cleavage of PARP1, while promoting loss of the oncogenes C and N-myc as well as mitotic cyclin B1. Treatments of NB cells with CFM-4 or -5 also resulted in loss of Inhibitory κB (IκB) α and β proteins. Micro-RNA profiling revealed upregulation of XIAP-targeting miR513a-3p in CFM-4-treated NB, mesothelioma, and breast cancer cells. Moreover, exposure of NB and breast cancer cells to CFM-4 or -5 resulted in diminished expression of anti-apoptotic XIAP1, cIAP1, and Survivin proteins. Expression of anti-miR513a-5p or miR513a-5p mimic, however, interfered with or enhanced, respectively, the breast cancer cell growth inhibition by CFM-4. CFMs also impacted biological properties of the NB cells by blocking their abilities to migrate, form colonies in suspension, and invade through the matrix-coated membranes. Our studies indicate anti-NB properties of CFM-4 and 5, and suggest that these CFMs and/or their future analogs have potential as anti-NB agents.
EATING DISORDERS IN INDIA

Data on the nature and extent of major eating disorders, anorexia nervosa and bulimia is lacking in non-white, native populations of the developing world, leaving a gap in understanding the determinants of these disorders. In a study on 210 medical students examined by a two-stage survey method, 31 subjects were found to have distress relating to their eating habits and body size not amounting to criterion-based diagnosis of eating disorders. The characteristics of this eating distress syndrome are described in relation to the major eating disorders.
Ideas and Enhancements Related to Mobile Applications to Support Type 1 Diabetes

Background Mobile devices have become increasingly important to young people who now use them to access a wide variety of health-related information. Research and policy related to the integration of health information and support with this technology do not effectively consider the viewpoint of a younger patient. Views of young people with type 1 diabetes are vital in developing quality services and improving their own health-related quality of life (HRQOL), yet research on their lifestyle and use of Web and mobile technology to support their condition and in non–health-related areas is sparse. Objective To develop insight into young people with type 1 diabetes and their current use of Web and mobile technology and its potential impact on HRQOL. This can be achieved by constructing an in-depth picture of their day-to-day experiences from qualitative interviewing and exploring how they make use of technology in their lives and in relation to their condition and treatment. The goal was then to build something to help them, using the researcher’s technical expertise and seeking users’ opinions during the design and build, utilizing sociotechnical design principles. Methods Data were collected by semistructured, in-depth qualitative interviews (N=9) of young people with type 1 diabetes aged 18-21. Interviews were transcribed and loaded onto NVivo for theme identification. Data analysis was undertaken during initial interviews (n=4) to locate potential ideas and enhancements for technical development. Latter interviews (n=5) assisted in the iterative sociotechnical design process of the development and provided additional developmental ideas. Results Six themes were identified providing an understanding of how participants lived with and experienced their condition and how they used technology. Four technological suggestions for improvement were taken forward for prototyping. One prototype was developed as a clinically approved app. A number of ideas for new mobile apps and enhancements to currently existing apps that did not satisfactorily cater to this age group’s requirements for use in terms of design and functionality were suggested by interviewees but were not prototyped. Conclusions This paper outlines the nonprototyped suggestions from interviewees and argues that young people with type 1 diabetes have a key role to play in the design and implementation of new technology to support them and improve HRQOL. It is vital to include and reflect on their suggestions as they have a radically different view of technology than either their parents or practitioners. We need to consider the relationship to technology that young people with type 1 diabetes have, and then reflect on how this might make a difference to them and when it might not be a suitable mechanism to use.
A new regularization method for bi-level image restoration

Image restoration is an ill-posed inverse problem, which has introduced the regularization method to suppress over-amplification. In this paper, by explicitly using the a priori knowledge for bi-level images, we propose a new regularization method for bi-level image restoration. Unlike the well known Tikhonov regularization method which eventually results in a linear system of equations, the new regularization method leads to a nonlinear optimization problem. This nonlinear optimization problem is solved by using the global Barzilai and Borwein gradient method. Simulation results show that the proposed regularization method is feasible and effective for bi-level image restoration.
An Interactive, Example-Based, Visual Clustering System

This work describes and evaluates a novel interactive visual clustering system. It combines a 2D projection with a clustering algorithm that operates on this projected data. Users can interact directly through the 2D representation, by providing examples according to their expert ground truth. Each interaction incrementally updates the 2D projection and the associated clustering. Experiments show the effectiveness of the method, with as few as one interaction leading to a tangible influence on the visualization.
Multi-feature extraction of ships from SAR images

Pose and structural features are widely used in automatic target recognition (ATR) systems. A procedure is proposed to extract such features of ships from synthetic aperture radar (SAR) images. The ship closed-contour image is first produced using a standard algorithm. A new two-step process is then presented for exact azimuth angle estimation. Modified criteria of centerline and minimum enclosing rectangle (MER) extraction are developed, and applied to calculate the ship dimension. Experimental results for numerically simulated SAR images demonstrate the effectiveness of the proposed technique.
Empirical Ontologies for Cohort Identification

The growth of patient data stored in Electronic Medical Records (EMR) has greatly expanded the potential for the evidence-based improvement of clinical practice. The proper re-use of this clinical information, however, does not replace basic research techniques — it augments them. The Text REtrieval Conference 2011 Medical Records Track explored how information retrieval may support clinical research by providing an efficient means to identify cohorts for clinical studies. Mayo Clinic NLP’s submission to the TREC Medical Records track attempts information retrieval at a semantic level, combining two disparate means of computing clinical semantics. Substantial effort has gone into the development of precise semantic specification of concepts in medical ontologies and terminologies[1, ? ]. But human clinicians do not generate clinical text by referring to such resources, and ontology creators do not base their terminology design on clinical text — so the distribution of ontology concepts in actual clinical texts may differ greatly. Therefore, in representing clinical reports for cohort identification, we advocate for a model that makes use of expert knowledge, is empirically validated, and considers context. This is accomplished through a new framework: empirical ontologies. Patient cohort identification is thus a practical use case for the techniques in our recent work on clinical concept frequency comparisons[2, 3]. The rest of this paper describes the TREC 2011 Medical Records task, describes Mayo Clinic’s run submissions, and reports evaluation results with subsequent discussion.
Sensor Performance Characterization for Use on a Micro Aerial Vehicle

This paper details the characterization of the Hokuyo UTM-30LX and the Xbox Kinect for use on a Micro Aerial Vehicle (MAV). The MAV is to be used for the exploration of unknown territories, and thus must be able to localize itself in various different conditions. To accomplish this, both the indoors and outdoors performance of the sensors are examined with different objects in their immediate surroundings.
SOCS : Socially intelligent computing for coding of qualitative data

This proposal will combine human and computational resources—in the form of human researchers and Natural Language Processing (NLP) and Machine Learning (ML) tools—to solve problems that currently challenge either working alone. The proposal has the goal of developing and evaluating an innovative NLP and ML-based research tool to support qualitative social science research, specifically content analysis. Content analysis is a qualitative research technique for finding evidence of concepts of theoretical interest using text as raw data rather than numbers (Myers, 1997). The process of identifying and labelling significant features in text is referred to as “coding” and the result of such an analysis is a text annotated with codes for the concepts exhibited (Miles and Huberman, 1994). This technique has become increasingly popular and more applicable as the volume of available “born-digital” text has exploded. However, the reliance on manual analysis of the text limits the scale and scope of content analysis research. In this proposal, the problem of coding qualitative data is conceptualized as an Information Extraction (IE) problem amenable to automation using NLP. However, rather than seeking to automate the process, the technologies will be used in a supporting role, creating a human-computer partnership. ML will be used to induce NLP rules from examples of coded text, avoiding the need to develop rules manually. To reduce the amount of training data needed from the human participants, an active learning process will be employed, in which a few hand-coded examples are used to create an initial model that can be further evolved through interaction with the user. These approaches will be combined in a prototype tool to support qualitative content analysis. As a demonstration and test of the tool, it will be applied to current and novel studies of cyber-infrastructure-supported distributed groups, specifically free/libre open source software development teams, and then to a broad range of social science research problems. This broad usage will also provide a test of the generalizability of a socio-computational approach to this problem.
Concurrent Programming in Education: Time for a Change

Writing concurrent programs using shared memory causes many programmers much trouble, due primarily to unsafe semantics. Memory corruption, race conditions, deadlocks, and even livelocks are trivially easy to introduce into a program and painful to hunt down due the nearly infinite possible interleavings of instructions between the threads. Undergraduate curricula traditionally introduce students to the idea of shared memory multithreading in a systems programming or operating systems class, but rarely expose them to any alternate models of concurrent programming. This leaves them with the idea that shared memory is the only way to do concurrent programming. After students were exposed to alternate models, they came to prefer them to the standard shared memory model. This happened despite their distaste of the programming language that was utilized in performing the study. The students also expressed interest in alternate models of concurrency being taught in the computer science curricula at WPI.
PSO FOR EXTREME LEARNING MACHINE

Magnetic Resonance Imaging (MRI) technique is one of the most useful diagnostic tools for human soft tissue analysis. Moreover, the brain anatomy features and internal tissue architecture of brain tumor are a complex task in case of 3-D anatomy. The additional spatial relationship in transverse, longitudinal planes and the coronal plane information has been proved to be helpful for clinical applications. This study extends the computation of gray level cooccurrence matrix (GLCM) and Run length matrix (RLM) to a threedimensional form for feature extraction. The sub-selection of rich optimal bank of features to model a classifier is achieved with custom Genetic Algorithm design. An improved Extreme Learning Machine (ELM) classifier algorithm is explored, for training single hidden layer artificial neural network, integrating an enhanced swarm-based method in optimization of the best parameters (input-weights, bias, norm and hidden neurons), enhancing generalization and conditioning of the algorithm. The method is modeled for automatic brain tissue and pathological tumor classification and segmentation of 3D MRI tumor images. The method proposed demonstrates good generalization capability from the best individuals obtained in the learning phase to handle sparse image data on publically available benchmark dataset and real time data sets. Received 2 January 2013, Accepted 12 February 2013, Scheduled 14 February 2013 * Corresponding author: Baladhandapani Arunadevi (arunaamurthy@gmail.com). 32 Arunadevi and Deepa
Using structural information and citation evidence to detect significant plagiarism cases in scientific publications

In plagiarism detection (PD) systems, two important problems should be considered: the problem of retrieving candidate documents that are globally similar to a document q under investigation, and the problem of side-by-side comparison of q and its candidates to pinpoint plagiarized fragments in detail. In this article, the authors investigate the usage of structural information of scientific publications in both problems, and the consideration of citation evidence in the second problem. Three statistical measures namely Inverse Generic Class Frequency, Spread, and Depth are introduced to assign a degree of importance (i.e., weight) to structural components in scientific articles. A term-weighting scheme is adjusted to incorporate component-weight factors, which is used to improve the retrieval of potential sources of plagiarism. A plagiarism screening process is applied based on a measure of resemblance, in which component-weight factors are exploited to ignore less or nonsignificant plagiarism cases. Using the notion of citation evidence, parts with proper citation evidence are excluded, and remaining cases are suspected and used to calculate the similarity index. The authors compare their approach to two flat-based baselines, TF-IDF weighting with a Cosine coefficient, and shingling with a Jaccard coefficient. In both baselines, they use different comparison units with overlapping measures for plagiarism screening. They conducted extensive experiments using a dataset of 15,412 documents divided into 8,657 source publications and 6,755 suspicious queries, which included 18,147 plagiarism cases inserted automatically. Component-weight factors are assessed using precision, recall, and F-measure averaged over a 10-fold cross-validation and compared using the ANOVA statistical test. Results from structural-based candidate retrieval and plagiarism detection are evaluated statistically against the flat baselines using paired-t tests on 10-fold cross-validation runs, which demonstrate the efficacy achieved by the proposed framework. An empirical study on the system's response shows that structural information, unlike existing plagiarism detectors, helps to flag significant plagiarism cases, improve the similarity index, and provide human-like plagiarism screening results. © 2012 Wiley Periodicals, Inc.
Unequal packet loss protection for layered video transmission

A general model for the transmission of layered video is introduced. The effect of packet dropping in different layers on the reconstruction of pictures is analysed. The application of unequal packet loss protection (UPLP) for layered video is proposed and the performance is investigated. An algorithm for the design of the UPLP parameters is described. The numerical analysis shows that the unequal packet loss protection can significantly improve the communication quality, in the sense of packet loss rate on reconstructing pictures, without any additional complexity in the network infrastructure. It is also shown that application of UPLP can achieve a more efficient bandwidth utilization than the application of ARQ protocols. This technique is suitable for multimedia multicasting in heterogeneous networks, as well as for digital video broadcasting systems which support a variety of receivers with different resolutions and signal processing powers.
Lighting and material of Halo 3

Lighting and material are very important aspects of the visual appearances of games and they present some of the hardest challenges in real time graphics today. For Halo and indeed many other games, keeping the players immersed in the virtual environment for long periods of time is a top priority of the graphics system, and good quality lighting and realistic materials are the fundamental building blocks for achieving the level of realism necessary to accomplish this goal.
Gene discovery from a pilot study of the transcriptomes from three diverse microbial eukaryotes: Corallomyxa tenera, Chilodonella uncinata, and Subulatomonas tetraspora

Background: Characterizing genome-scale data from diverse eukaryotes is essential for gene discovery and for inferring major transitions across the eukaryotic tree of life. Yet, the bulk of eukaryotic diversity remains undersampled, particularly for free-living microbial lineages. Analysis of transcriptome data generated from high throughput (e.g. 454) sequencing of mRNAs provides an efficient way to characterize genes from diverse eukaryotes. Results: Here we report analyses of RNA-Seq data from the rhizarian net-like amoeba Corallomyxa tenera, the ciliate Chilodonella uncinata and a recently-described genus representing a novel major clade of eukaryotes, Subulatomonas tetraspora. We generated 16,983, 11,529 and 10,630 contigs plus single reads for these taxa respectively. Given that these organisms cannot be cultured axenically, we developed custom scripts to remove bacterial contaminants through an iterative BLAST based protocol and we then identified expressed genes using BLAST2GO (1;2). This approach yielded a large number of genes with eukaryotic homologs, as well as numerous novel genes. To assess our approach and to explore the resulting sequences, we searched for genes involved in anaerobic metabolism, RNAi and meiosis. Further, we report the results of a preliminary phylogenomic analysis including these organisms. Conclusions: We characterized the transcriptomes of three phylogenetically diverse eukaryotes. After applying several filters to ensure the retention of only high-quality, non-contaminant data, we identified numerous sequences that can be used for gene discovery and phylogenomics. We found candidate genes involved in RNAi, meiosis, and anaerobic metabolism, and generated phylogenies that place the target taxa in positions predicted by previous analyses. This work supports the use of high throughput approaches for assessing features of non-model organisms, even in instances when species cannot be cultured axenically or grown to large numbers.
Task-set switching and long-term memory retrieval.

The authors tested the hypothesis of a close relationship between the intentional component of task-set switching ("advance reconfiguration;" R. D. Rogers & S. Monsell, 1995) and long-term memory (LTM) retrieval. Consistent with this hypothesis, switch costs are reported to be larger when the switched-to task involves high retrieval demands (i.e., retrieval of episodic information) than when it involves low retrieval demands (i.e., retrieval of semantic information). In contrast, switch costs were not affected by a primary-task difficulty manipulation unrelated to intentional retrieval demands (Experiment 2). Also, the retrieval-demand effect on switch costs was eliminated when time for advanced preparation or task cues explicitly specifying the task rules were provided (Experiment 3). Overall, results were consistent with the hypothesis that the intentional switch-cost component reflects the time demands of retrieving appropriate task rules from LTM.
Chikungunya Viral Fitness Measures within the Vector and Subsequent Transmission Potential

Given the recent emergence of chikungunya in the Americas, the accuracy of forecasting and prediction of chikungunya transmission potential in the U.S. requires urgent assessment. The La Reunion-associated sub-lineage of chikungunya (with a valine substitution in the envelope protein) was shown to increase viral fitness in the secondary vector, Ae. albopictus. Subsequently, a majority of experimental and modeling efforts focused on this combination of a sub-lineage of the East-Central-South African genotype (ECSA-V) – Ae. albopictus, despite the Asian genotype being the etiologic agent of recent chikungunya outbreaks world-wide. We explore a collection of data to investigate relative transmission efficiencies of the three major genotypes/sub-lineages of chikungunya and found difference in the extrinsic incubation periods to be largely overstated. However, there is strong evidence supporting the role of Ae. albopictus in the expansion of chikungunya that our R0 calculations cannot attribute to fitness increases in one vector over another. This suggests other ecological factors associated with the Ae. albopictus-ECSA-V cycle may drive transmission intensity differences. With the apparent bias in literature, however, we are less prepared to evaluate transmission where Ae. aegypti plays a significant role. Holistic investigations of CHIKV transmission cycle(s) will allow for more complete assessment of transmission risk in areas affected by either or both competent vectors.
Self-synchronization of Nonidentical Machines in Machine-to-Machine Systems

The notion of common time is very important information in distributed systems (e.g., for maintaining the consistency of distributed data). Namely, due to the lack of global time and imperfections (e.g., skew) of physical clocks, in order to agree on common time, distributed nodes have to synchronize themselves. Imperfections of physical clocks are even more emphasized in heterogeneous distributed systems called Machine-to-Machine (M2M) systems in which communication refers to the communication among no identical machines that communicate using different types of communication technologies without, or with limited human intervention.