Miracle-dz/UniFAR-FTUs huggingface dataset:
Met Proto-Oncogene and Insulin-Like Growth Factor Binding Protein 3 Overexpression Correlates with Metastatic Ability in Well-Differentiated Pancreatic Endocrine Neoplasms

Pancreatic endocrine neoplasms are neoplastic proliferations of islet cells or islet cell precursors and are capable of secreting a variety of synthetic products, including insulin, glucagon, gastrin, and vasoactive intestinal peptide. The biological behavior of pancreatic endocrine neoplasms is often unpredictable, and there are few reliable histopathologic criteria reliably correlating with metastatic ability. We have used the Affymetrix U133 GeneChip set (HG_U133 A and B; Affymetrix; Santa Clara, CA) representing ∼33,000 characterized transcripts to examine global gene expression profiles from well-differentiated nonmetastatic (n = 5) and metastatic (n = 7) pancreatic endocrine neoplasms to determine molecular markers that predict disease progression. Microarray hybridization data were normalized using the GeneLogic GeneExpress Software System to identify differentially up- and down-regulated genes in metastatic versus nonmetastatic pancreatic endocrine neoplasms. Using a 3-fold change in gene expression as a threshold, we have identified 65 overexpressed and 57 underexpressed genes in metastatic pancreatic endocrine neoplasms as compared with nonmetastatic pancreatic endocrine neoplasms. Several classes of genes, including growth factors and growth factor-related molecules (IGFBP1, IGFBP3, and MET), developmental factors (TBX3 and MEIS2), cytoskeletal factors (β 1 tubulin and ACTN2), cholesterol homeostasis mediators (LRP5, SLC27A2, and RXRG), intracellular signaling pathway mediators (DYRK1A, PKIB, and AK2), methyltransferases (MGMT and GAMT), and DNA repair and regulatory molecules (CHEK1 and ZNF198), were identified as differentially over- or underexpressed via this method. Immunohistochemical validation of microarray data were performed for two overexpressed genes, namely, the met proto-oncogene (MET) and insulin-like growth factor binding protein 3 (IGFBP3) with tissue microarrays of nonmetastatic (n = 24) and metastatic (n = 15) pancreatic endocrine neoplasms. Increased expression of IGFBP3 was confirmed in metastatic versus nonmetastatic pancreatic endocrine neoplasms (12 of 15, 80% versus 10 of 24, 42%), as well as in lymph node (6 of 7, 86%) and liver (9 of 9, 100%) metastases. Similarly, overexpression of MET was confirmed in metastatic versus nonmetastatic pancreatic endocrine neoplasms (5 of 15, 33% versus 4 of 24, 17%), as well as in lymph node metastases (4 of 7, 57%) and liver metastases (5 of 9, 56%). The majority of genes that demonstrated altered expression has not been previously identified as differentially expressed in metastatic pancreatic endocrine neoplasm lesions and may therefore represent newly identified molecules in the progression of these lesions.
Why all this fuss about codified and tacit knowledge

This paper starts with a critical assessment of the recent paper by Cowan, Foray and David. It also provides the authors' own assessment of why the tacit-codified distinction is important in relation to economic analysis and knowledge management practice. The criticism of Cowan, Foray and David centres on three points. Firstly, it is argued that the discussion on codification must make the fundamental distinction between knowledge about the world (know-what) and knowledge in the form of skills and competence (know-how). Secondly, it is argued that the dichotomy between codifiable and non-codifiable knowledge is problematic since it is rare that a body of knowledge can be completely transformed into codified form without losing some of its original characteristics and that most forms of relevant knowledge are mixed in these respects. Thirdly, we contest their implicit assumption that codification always represents progress. We conclude that for these reasons their intellectual exercise of extending definitions of what is codified and possible to codify, while in principle addressing very important issues related to innovation policy and knowledge management, ends up having limited practical implications for these areas. Copyright 2002, Oxford University Press.
A parameter-dependent Lyapunov function based design approach for gain-scheduled H∞ filters of LPV systems

In this paper we study the problem of designing gain-scheduled Hinfin filters for a class of parameter-varying discrete-time systems. We utilize a parameter-dependent Lyapunov function to establish a new characterization of asymptotic stability with Hinfin norm bound for the linear parameter-varying (LPV) systems. On the basis of the stability with Hinfin performance characterization established, we provide sufficient conditions for the solvability of the filter design problem by means of a finite number of linear matrix inequalities (LMIs). Then we show that solving a set of LMIs paves the way for designing a desired gain-scheduled Hinfin filter. We also use a numerical example to demonstrate the application of the presented filter design method.
Navigating the massive world of reddit: Using backbone networks to map user interests in social media

In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities.
African American grandparents raising grandchildren: a national study using the Census 2000 American Community Survey.

OBJECTIVE The objective of this work was to determine the prevalence, sociodemographic characteristics, and service utilization patterns of African American grandparents raising grandchildren compared with noncaregiving peers. METHODS Data were obtained from the Census 2000 Supplementary Survey/American Community Survey, a nationally representative survey of 890,000 households. Analysis was based upon comparison of 2,362 African American grandparent caregivers aged 45+ with 40,148 noncaregiving peers. Gender-specific analyses also were conducted. RESULT Over 500,000 African Americans aged 45+ were estimated to be raising grandchildren in 2000. They were disproportionately female, younger, and less educated than noncaregivers and more likely to be living in poverty and receiving public assistance. Grandmother caregivers had significantly higher rates of functional limitations and poverty than either grandfather caregivers or other African American women aged 45+. DISCUSSION African American grandparent caregivers, particularly grandmothers, represent a highly vulnerable population economically. The finding that four-fifths of African American grandmother caregivers below the poverty line were not receiving public assistance underscores the need for improving program outreach and substance to better serve this population. The use of theories of intersectionality and multiracial feminism in future studies of grandparent caregiving should enhance our understanding of how the impact of race on gender varies by social class.
THE ATACAMA COSMOLOGY TELESCOPE: A MEASUREMENT OF THE 600 < ℓ < 8000 COSMIC MICROWAVE BACKGROUND POWER SPECTRUM AT 148 GHz

We present a measurement of the angular power spectrum of the cosmic microwave background (CMB) radiation observed at 148 GHz. The measurement uses maps with 1. ′4 angular resolution made with data from the Atacama Cosmology Telescope (ACT). The observations cover 228 deg2 of the southern sky, in a 4. °2 wide strip centered on declination 53° south. The CMB at arcminute angular scales is particularly sensitive to the Silk damping scale, to the Sunyaev–Zel'dovich (SZ) effect from galaxy clusters, and to emission by radio sources and dusty galaxies. After masking the 108 brightest point sources in our maps, we estimate the power spectrum between 600 < ℓ < 8000 using the adaptive multi-taper method to minimize spectral leakage and maximize use of the full data set. Our absolute calibration is based on observations of Uranus. To verify the calibration and test the fidelity of our map at large angular scales, we cross-correlate the ACT map to the WMAP map and recover the WMAP power spectrum from 250 < ℓ < 1150. The power beyond the Silk damping tail of the CMB (ℓ ∼ 5000) is consistent with models of the emission from point sources. We quantify the contribution of SZ clusters to the power spectrum by fitting to a model normalized to σ8 = 0.8. We constrain the model's amplitude ASZ < 1.63 (95% CL). If interpreted as a measurement of σ8, this implies σSZ8 < 0.86 (95% CL) given our SZ model. A fit of ACT and WMAP five-year data jointly to a six-parameter ΛCDM model plus point sources and the SZ effect is consistent with these results.
Nanophotonics Sensor Based on Microcantilever for Chemical Analysis

A Si-based cantilever sensor with photonic crystal (PC) resonator as readout for chemical sensing and analysis has been developed. The resonant wavelength shift of PC resonator is resulted from PC deformation induced by cantilever bending, in which this optical readout scheme facilitates cantilever deflection measurements in liquid. Through numerical simulation, we demonstrate that the detection capability of this micromechanical sensor operated in water is better than that of sensor operated in air. The minimum detectable Z-displacement and strain of Si/SiO2 cantilever sensor are derived as 0.6 mum and 0.0098% in water and 0.812 mum and 0.0144% in air, respectively. This novel micromechanical sensor shows its promising future in applications such as detection of proteins and DNA in solution.
CREEC: Chain routing with even energy consumption

A convergecast is a popular routing scheme in wireless sensor networks (WSNs) in which every sensor node periodically forwards measured data along configured routing paths to a base station (BS). Prolonging lifetimes in energy-limited WSNs is an important issue because the lifetime of a WSN influences on its quality and price. Low-energy adaptive clustering hierarchy (LEACH) was the first attempt at solving this lifetime problem in convergecast WSNs, and it was followed by other solutions including power efficient gathering in sensor information systems (PEGASIS) and power efficient data gathering and aggregation protocol (PEDAP). Our solution-chain routing with even energy consumption (CREEC)-solves this problem by achieving longer average life-times using two strategies: i) Maximizing the fairness of energy distribution at every sensor node and ii) running a feedback mechanism that utilizes a preliminary simulation of energy consumption to save energy for depleted sensor nodes. Simulation results confirm that CREEC outperforms all previous solutions such as LEACH, PEGASIS, PEDAP, and PEDAP-power aware (PA) with respect to the first node death and the aver- age lifetime. CREEC performs very well at all WSN sizes, BS distances and battery capacities with an increased convergecast delay.
Places of Their Own: African American Suburbanization in the Twentieth Century

ly, modem utopian communities, of which there were at least seven hundred in 2000 (such as Drop City, Stephen Gaskin’s The Farm, or Twin Oaks). Although Sutton undoubtedly is right in concluding that “secular communities, in contrast to religious ones, often lacked a charismatic leader,” he provides vivid portraits of many visionaries whose names are indelibly associated with some of the communities mentioned above that they inspired. When Sutton’s introduction labels utopian communalism as “an unbroken motif in American history,” readers may expect a celebration of this tradition. However, his tone throughout is even handed, and, in fact, he concludes by pessimistically noting the “eventual disillusionment” experienced by most members of communal societies when they realized that their experiments were simply microcosms of the larger society, sharing the same problems and shortcomings. Even readers familiar with the subject will most likely encounter new information; however, because Sutton was constrained by both the book’s brevity and breadth of coverage, they may also wish that he had devoted more attention to certain communities, such as Frances Wright’s radical Nashoba Community. I was surprised that the selected bibliography omitted such works as John Egerton’s Visions of Utopia: Nashoba, Rugby, Ruskin, and the “New Communities ” in Tennessee’s Past (1977), Richard Fairfield’s Communes USA: A Personal Tour (1972), Robert Houriet’s Getting Back Together (1971), and Ron Roberts’s The New Communes: Coming Together in America (1971). Nonetheless, this brief overview, along with his companion volume on religious utopian communities, belongs in all major library collections.
Locating Sensors in Concave Areas

In sensor network localization, multihop based approaches were proposed to approximate the shortest paths to Euclidean distances between pairwise sensors. A good approximation can be achieved when sensors are densely deployed in a convex area, where the shortest paths are close to straight lines connecting pairwise sensors. However, in a concave network, the shortest paths may deviate far away from straight lines, which leads to erroneous distance estimation and inaccurate localization results. In this paper, we propose an improved multihop algorithm which can recognize and filter out the erroneous distance estimation, and therefore achieve accurate localization results even in a concave network.
Product Placements in Movies: A Cross-Cultural Analysis of Austrian, French and American Consumers' Attitudes toward This Emerging, International Promotional Medium

Abstract This paper reports on a cross-cultural study which tests the robustness of the approach developed by Gupta and Gould (1997) concerning use of product placements in movies. Using their American data as a comparison point, additional data using the same questionnaire were collected in Austria and France. As an international medium in which movies freely cross borders, product placement is also a less adaptable one, relative to commercials since it remains in the movie regardless of the nation where it is shown. Applying a three-pronged framework which considered country, product and individual differences and their interactive effects, the results of this study indicate the ways in which all three have an impact on the acceptability of product placements and on potential purchase behavior. Finally, implications for managing and further researching product placements based on this framework are drawn.
An Economic Analysis of Online Advertising Using Behavioral Targeting

Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and other available information (e.g., hobbies registered on a web site). This technique, known as behavioral targeting, has been hailed as the new "Holy Grail" in online advertising because of its potential effectiveness. In this paper, we study the economic implications when an online publisher engages in behavioral targeting. The publisher auctions off an advertising slot and is paid on a cost-per-click basis. Using a horizontal differentiation model to capture the fit between a user and an advertisement being displayed, we identify the factors that affect the publisher's revenue, the advertisers' payoffs, and social welfare. We show that revenue for the online publisher in some circumstances can double when behavioral targeting is used. However, increased revenue for the publisher is not guaranteed: in some cases, the prices of advertising and hence the publisher's revenue can be lower, depending on the degree of competition and the advertisers' valuations. We identify two effects associated with behavioral targeting: a competitive effect and a propensity effect. The relative strength of the two effects determines whether the publisher's revenue is positively or negatively affected. We also demonstrate that, although social welfare is increased and small advertisers are better off under behavioral targeting, the dominant advertiser might be worse off and reluctant to switch from traditional advertising.
Mathematical model of falling of a viscous jet onto a moving surface

The stationary flow of a jet of a Newtonian fluid that is drawn by gravity onto a moving surface is analyzed. It is assumed that the jet has a convex shape and hits the moving surface tangentially. The flow is modelled by a third-order ODE on a domain of unknown length and with an additional integral condition. By solving part of the equation explicitly, the problem is reformulated as a first-order ODE with an integral constraint. The corresponding existence region in the three-dimensional parameter space is characterized in terms of an easily calculable quantity. In a qualitative sense, the results from the model are found to correspond with experimental observations.
Detecting stock market manipulation using supervised learning algorithms

Market manipulation remains the biggest concern of investors in today's securities market, despite fast and strict responses from regulators and exchanges to market participants that pursue such practices. The existing methods in the industry for detecting fraudulent activities in securities market rely heavily on a set of rules based on expert knowledge. The securities market has deviated from its traditional form due to new technologies and changing investment strategies in the past few years. The current securities market demands scalable machine learning algorithms supporting identification of market manipulation activities. In this paper we use supervised learning algorithms to identify suspicious transactions in relation to market manipulation in stock market. We use a case study of manipulated stocks during 2003. We adopt CART, conditional inference trees, C5.0, Random Forest, Naïve Bayes, Neural Networks, SVM and kNN for classification of manipulated samples. Empirical results show that Naïve Bayes outperform other learning methods achieving F2 measure of 53% (sensitivity and specificity are 89% and 83% respectively).
Efficacy of Different Precooling Agents and Topical Anesthetics on the Pain Perception during Intraoral Injection: A Comparative Clinical Study

ABSTRACT Topical anesthesia is widely advocated in pediatric dentistry practice to reduce pain and anxiety produced by administration of local anesthesia. Cryoanesthesia to lessen the injection pain has also been reported to be promising. However, sparse literature reports exist regarding clinical efficacy of these agents. Aim: The purpose of this study was to compare the efficacy of the refrigerant (1,1,1,3,3-pentafluoropropane/1,1,1,2-tetrafluo-roethane), benzocaine and ice on the pain perception during intraoral injection using visual analog scale (VAS) and sound, eye, motor (SEM) scale. Study design: In this Spit-mouth design study, a total of 160 patients between the age group of 5 and 8 years were selected and were randomly divided into two equal groups having 80 patients in each group. Results: Ice cone has shown lower mean scores (p < 0.001) as compared to benzocaine and refrigerant whereas no significant difference was observed between refrigerant and benzocaine (p > 0.05) on both the scales. Conclusion: Ice cone had shown significantly higher efficacy as compared to benzocaine and refrigerant. How to cite this article: Lathwal G, Pandit IK, Gugnani N, Gupta M. Efficacy of Different Precooling Agents and Topical Anesthetics on the Pain Perception during Intraoral Injection: A Comparative Clinical Study. Int J Clin Pediatr Dent 2015;8(2):119-122.
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive task. As it has to compute million of parameters, it results to huge memory consumption. Moreover, extracting finer features and conducting supervised training tends to increase the complexity. With the introduction of Fully Convolutional Neural Network, which uses finer strides and utilizes deconvolutional layers for upsampling, it has been a go to for any image segmentation task. In this paper, we propose two segmentation architecture which not only needs one-third the parameters to compute but also gives better accuracy than the similar architectures. The model weights were transferred from the popular neural net like VGG19 and VGG16 which were trained on Imagenet classification data-set. Then we transform all the fully connected layers to convolutional layers and use dilated convolution for decreasing the parameters. Lastly, we add finer strides and attach four skip architectures which are element-wise summed with the deconvolutional layers in steps. We train and test on different sparse and fine data-sets like Pascal VOC2012, Pascal-Context and NYUDv2 and show how better our model performs in this tasks. On the other hand our model has a faster inference time and consumes less memory for training and testing on NVIDIA Pascal GPUs, making it more efficient and less memory consuming architecture for pixel-wise segmentation.
Perspectives on theory at the interface of physics and biology

Theoretical physics is the search for simple and universal mathematical descriptions of the natural world. In contrast, much of modern biology is an exploration of the complexity and diversity of life. For many, this contrast is prima facie evidence that theory, in the sense that physicists use the word, is impossible in a biological context. For others, this contrast serves to highlight a grand challenge. I am an optimist, and believe (along with many colleagues) that the time is ripe for the emergence of a more unified theoretical physics of biological systems, building on successes in thinking about particular phenomena. In this essay I try to explain the reasons for my optimism, through a combination of historical and modern examples.