Miracle-dz/UniFAR-FTUs huggingface dataset:
Progressive loss of epidermal growth factor receptor in a subpopulation of breast cancers: implications in target-directed therapeutics

Understanding the molecular etiology and heterogeneity of disease has a direct effect on cancer therapeutics. To identify novel molecular changes associated with breast cancer progression, we conducted phosphoproteomics of the MCF10AT model comprising isogenic, ErbB2- and ErbB3-positive, xenograft-derived cell lines that mimic different stages of breast cancer. Using in vitro animal model and clinical breast samples, our study revealed a marked reduction of epidermal growth factor receptor (EGFR) expression with breast cancer progression. Such diminution of EGFR expression was associated with increased resistance to Gefitinib/Iressa in vitro. Fluorescence in situ hybridization showed that loss of EGFR gene copy number was one of the key mechanisms behind the low/null expression of EGFR in clinical breast tumors. Statistical analysis on the immunohistochemistry data of EGFR expression from 93 matched normal and breast tumor samples showed that (a) diminished EGFR expression could be detected as early as in the preneoplastic lesion (ductal carcinoma in situ) and this culminated in invasive carcinomas; (b) EGFR expression levels could distinguish between normal tissue versus carcinoma in situ and invasive carcinoma with high statistical significance (P < 0.001, n = 81). However, no significant correlation of EGFR expression with disease-free survival and overall survival was observed. This is the first time EGFR expression has been tracked meaningfully and developmentally from the normal condition through disease progression using in vitro, xenograft, and matched normal and tumor samples. Thus, our study provides a new insight into the role of EGFR in breast cancer development. Although no value of EGFR expression in prognosis was found, our findings are likely to have implications in the design of clinical trials targeting the EGFR family of proteins in breast cancer. [Mol Cancer Ther 2007;6(11):2828–42]
An information processing analysis of mental multiplication

The specific model of central or conscious processing system (CPS) is presented. It consists of two part: (1) a number of information carrying registers, and (2) a functional unit controlling the processes performed on the contents of the registers. The capacity limit is explained as a combination of storage capacity and processing capacity. The capacity limit was estimated by an experiment using mental multiplication performed according to a particular algorithm. The results suggested that the capacity was around eight to nine registers when the algorithm was strictly followed; this limit could, however, be exceeded because some problems automatically were coded and remembered by rules rather then by rehearsal. The results further suggested that information processing, i.e. the multiplication, had to take place on a time-sharing basis in competition with rehearsal, thus suggesting the existence of higher-order control system.
Land Management Controls on Hydraulic Conductivity of an Urban Farm in Atlanta, GA

Increasing urbanization is often accompanied by problematic changes in watershed hydrology. Decreasing surface permeability can lead to increased overland flow volumes, which may spread surficial contaminants and increase the strain on municipal stormwater infrastructure. This study examines a mixed-use property in the Proctor Creek watershed in Atlanta, Georgia, to better understand how land-management practices influence soil overland flow potential. Field saturated hydraulic conductivity (Kfs) measurements were collected from soils 1) subjected to compaction, 2) in urban agricultural use, and 3) under common lawn maintenance. Mean values were 9.1E-7 cm/s, 2.2E-4 cm/s, and 9.0E-6 cm/s respectively. Measurements were collected insitu with the use of the Aardvark constant-head permeameter. Statistical analyses indicated a substantial difference in Kfs based on land-management practices and that urban farming can increase soil Kfs and limit overland flow. Additional analysis revealed no significant difference in grain-size distributions suggesting land-management practices controlled Kfs, not soil texture. INDEX WORDS: Saturated hydraulic conductivity, Urban farm, Hydrology, Overland flow, Constant head permeameter, Aardvark permeameter, Land management practices LAND MANAGEMENT CONTROLS ON HYDRAULIC CONDUCTIVITY OF AN URBAN FARM IN ATLANTA, GA
Critical review – Outsourcing: a paradigm shift

Outsourcing of services has been receiving increasing attention in management literature and praxis. It is considered that greatest attention has been given to the enhanced efficiency of transaction costs through outsourcing. In contrast, this paper explores what is being outsourced, the drivers for outsourcing and the IT commodification influence on outsourcing. Particular attention is given to examining supplier‐client relationships and the consequently new emerging outsourcing arrangements and organisational forms. The benefits and costs of outsourcing and client satisfaction are discussed as well as outsourcing in the public sector. The paper highlights that a fundamental paradigm shift is underway from strictly provider/supplier relationships to an emerging array of partner based relationships comparable with the Japanese kieretsu relationship model. The paper concludes by identifying areas for further research for increasing understanding of the paradigm shift that is highlighted.
Widget based automated rigging of bipedal character with custom manipulators

In this research paper we present an automated rigging system for a humanoid (biped) character with complete customization and control options according to the need of the character rigger and the animator. The rigging requirements check list has been proposed for each body part by analyzing various live natural human motions and then summarizing them. The system initially provides widgets that represent human skeletal joints, which can be easily manipulated and modified according to the character type and body height. The system takes the widgets positions from the 3D space and creates the joint based skeletal structure automatically using procedural programming and then followed by creating a complete bipedal rig with custom manipulators to satisfy the animators need in a production environment.
Utilizing the Analytic Hierarchy Process to Establish Weighted Values for Evaluating the Stability of Slope Revegetation based on Hydroseeding Applications in South Korea

The aim of this study was to identify the major variables identified as important for considering the stabilization of slope revegetation based on hydroseeding applications and evaluate weights of each variable using the analytic hierarchy process (AHP) with both environmental experts and civil engineers. Twenty-five variables were selected by the experts’ survey from a total of 65 from the existing literature, with each variable considered as an important factor for slope stabilization in South Korea. The final results from the AHP method showed that variables associated with the driving force of water resources showed higher values in all expert groups such as rain intensity, seepage water and drainage condition. Other important variables were related to plant growth such as vegetation community, vegetation coverage and quality of soil ameliorant produced in an artificial factory such as tensile strength, permeability coefficient, soil texture and organic matter. The five highest-ranked variables that satisfied both environmental experts and civil engineers were rain intensity, seepage water, slope angle, drainage condition and ground layer. The findings of this research could be helpful for developing a more accurate rating system to evaluate the stability of slope revegetation.
EigenGP: Sparse Gaussian process models with data-dependent eigenfunctions

Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost and it is difficult to design nonstationary GP priors in practice. In this paper, we propose a sparse Gaussian process model, EigenGP, based on the Karhunen-Loeve (KL) expansion of a GP prior. We use the Nystrom approximation to obtain data dependent eigenfunctions and select these eigenfunctions by evidence maximization. This selection reduces the number of eigenfunctions in our model and provides a nonstationary covariance function. To handle nonlinear likelihoods, we develop an efficient expectation propagation (EP) inference algorithm, and couple it with expectation maximization for eigenfunction selection. Because the eigenfunctions of a Gaussian kernel are associated with clusters of samples - including both the labeled and unlabeled - selecting relevant eigenfunctions enables EigenGP to conduct semi-supervised learning. Our experimental results demonstrate improved predictive performance of EigenGP over alternative state-of-the-art sparse GP and semisupervised learning methods for regression, classification, and semisupervised classification.
A natural language processing approach to generate SBVR and OCL

The Object Constraint Language (OCL) is a declarative language and is used to make the Unified Modeling Language (UML) models well-defined through defining a set of constraints. However, the syntactic complexity of OCL makes the writing of OCL code difficult. A natural language based interface can be useful in making the process of writing OCL expressions easy and simple. However, the translation of natural language (NL) text to object constraint language (OCL) code is a challenging task on account of the informal nature of natural languages as various syntactic and semantic ambiguities make the process of NL translation to formal languages more complex. However, in our approach the usage of SBVR not only provides natural languages a formal abstract syntax representation but it is also close to OCL syntax. In this thesis, a framework is presented to facilitate the users of the UML tools so that they can write invariants and pre/post conditions in English. The results of the case studies manifest that a natural language based approach to generate OCL constraints can not only help in significantly improving usability of OCL but also outperforms the most closely related techniques in terms of effectiveness and effort required in generating OCL
Highly stable data SRAM-PUF in 65nm CMOS process

Physical Unclonable Functions (PUF) are innovative circuit extract key relying upon the intrinsic process variations in interconnects and transistors of integrated circuits. It can be used in many modern cryptographic protocols as keys or unique digital ID. This paper proposes a highly stable SRAM-PUF cell with isolate nMOS to improve the robustness of the circuit. This design is implemented in SMIC 65nm LP CMOS technology and the layout area of the cell occupies 1. 9μm×1. 15μm. The simulation results show that the SNM of the proposed solution improve 45% performance during a read operation compared with a traditional SRMA-PUF cell architecture. Meanwhile, it has a high level of stability under different corners.
1.5D Egocentric Dynamic Network Visualization

Dynamic network visualization has been a challenging research topic due to the visual and computational complexity introduced by the extra time dimension. Existing solutions are usually good for overview and presentation tasks, but not for the interactive analysis of a large dynamic network. We introduce in this paper a new approach which considers only the dynamic network central to a focus node, also known as the egocentric dynamic network. Our major contribution is a novel 1.5D visualization design which greatly reduces the visual complexity of the dynamic network without sacrificing the topological and temporal context central to the focus node. In our design, the egocentric dynamic network is presented in a single static view, supporting rich analysis through user interactions on both time and network. We propose a general framework for the 1.5D visualization approach, including the data processing pipeline, the visualization algorithm design, and customized interaction methods. Finally, we demonstrate the effectiveness of our approach on egocentric dynamic network analysis tasks, through case studies and a controlled user experiment comparing with three baseline dynamic network visualization methods.
A Pilot Study of Compositional Analysis of the Breast and Estimation of Breast Mammographic Density Using Three-Dimensional T1-Weighted Magnetic Resonance Imaging

Purpose: A method and computer tool to estimate percentage magnetic resonance (MR) imaging (MRI) breast density using three-dimensional T1-weighted MRI is introduced, and compared with mammographic percentage density [X-ray mammography (XRM)]. Materials and Methods: Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by water-containing tissue on three-dimensional T1-weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the Magnetic Resonance Imaging in Breast Screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21-point score and measured by applying a two-dimensional interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and interreader consistency. Results: Percent MRI density correlated strongly (r = 0.78; P < 0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2. Discussion: MRI provides direct three-dimensional measurement of the proportion of water-based tissue in the breast. It correlates well with visual and computerized percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk. (Cancer Epidemiol Biomarkers Prev 2008;17(9):2268–74)
Cytosolic Entry of Bisphosphonate Drugs Requires Acidification of Vesicles after Fluid-Phase Endocytosis

Bisphosphonates such as alendronate and zoledronate are blockbuster drugs used to inhibit osteoclast-mediated bone resorption. Although the molecular mechanisms by which bisphosphonates affect osteoclasts are now evident, the exact route by which they are internalized by cells is not known. To clarify this, we synthesized a novel, fluorescently labeled analog of alendronate (AF-ALN). AF-ALN was rapidly internalized into intracellular vesicles in J774 macrophages and rabbit osteoclasts; uptake of AF-ALN or [14C]zoledronate was stimulated by the presence of Ca2+ and Sr2+ and could be inhibited by addition of EGTA or clodronate, both of which chelate calcium ions. Both EGTA and clodronate also prevented the bisphosphonate-induced inhibition of Rap1A prenylation, an effect that was reversed by addition of Ca2+. In J774 cells and osteoclasts, vesicular AF-ALN colocalized with dextran (but not wheat germ agglutinin or transferrin), and uptake of AF-ALN or [14C]zoledronate was inhibited by dansylcadaverine, indicating that fluid-phase endocytosis is involved in the initial internalization of bisphosphonate into vesicles. Endosomal acidification then seems to be absolutely required for exit of bisphosphonate from vesicles and entry into the cytosol, because monensin and bafilomycin A1, both inhibitors of endosomal acidification, did not inhibit vesicular uptake of AF-ALN or internalization of [14C]zoledronate but prevented the inhibitory effect of alendronate or zoledronate on Rap1A prenylation. Taken together, these results demonstrate that cellular uptake of bisphosphonate drugs requires fluid-phase endocytosis and is enhanced by Ca2+ ions, whereas transfer from endocytic vesicles into the cytosol requires endosomal acidification.
Automatic pronunciation prediction for text-to-speech synthesis of dialectal arabic in a speech-to-speech translation system

Text-to-speech synthesis (TTS) is the final stage in the speech-tospeech (S2S) translation pipeline, producing an audible rendition of translated text in the target language. TTS systems typically rely on a lexicon to look up pronunciations for each word in the input text. This is problematic when the target language is dialectal Arabic, because the statistical machine translation (SMT) system usually produces undiacritized text output. Many words in the latter possess multiple pronunciations; the correct choice must be inferred from context. In this paper, we present a weakly supervised pronunciation prediction approach for undiacritized dialectal Arabic in S2S systems that leverages automatic speech recognition (ASR) to obtain parallel training data for pronunciation prediction. Additionally, we show that incorporating source language features derived from SMT-generated automatic word alignment further improves automatic pronunciation prediction accuracy.
Choice Bias in Multiple Choice Questions From Organizational Behavior Test Banks

Many instructors of organizational behavior use author-provided test banks for testing and evaluating student performance. There are numerous advantages to using these test banks. A principal advantage is that their use ensures a direct relationship between terms and methods used in the textbook and those used on examinations. Other advantages are that questions taken from these test banks may be (a) reasonably well written, (b) comprehensive in coverage, (c) time saving, and (d) reliable for the evaluation of the students’ knowledge of the text material. The characteristics of non-bias and reliability are of paramount importance because the results of examinations formulated from these test banks are used in assigning grades. These grades, in turn, have further impacts on membership in honorary societies, selection of graduate schools, scholarships, job placement, and in some cases even whether or not a degree is granted. Clearly, the presence of any inherent bias or unreliability in these test banks is a matter of critical importance to students and instructors
Whom You Know Matters: Venture Capital Networks and Investment Performance

Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot market transactions. We examine the performance consequences of this organizational structure in the context of relationships established when VCs syndicate portfolio company investments. We find that better-networked VC firms experience significantly better fund performance, as measured by the proportion of investments that are successfully exited through an IPO or a sale to another company. Similarly, the portfolio companies of better-networked VCs are significantly more likely to survive to subsequent financing and eventual exit. We also provide initial evidence on the evolution of VC networks. Copyright 2007 by The American Finance Association.
DeVANET: Decentralized Software-Defined VANET Architecture

Vehicular adhoc networks come out to be a promising solution for ensuring traffic and road safety on highways. However this area induces communication challenges such as topology dynamics, and connectivity losses. Today's Vanet demand a sound planning to make architectural level decisions. Integrating Vanet with emerging Software defined Networking brings ground-breaking networking innovation. Recent researches in SDN based Vanet are useful but their performance goals drops in large scale VANETs. In this research we are going to exploit SDN planes by partitioning Vanet to work in distributed manner. The proposed architecture is tested on VEINS testbed which provides interactive environment to perform road traffic simulations. Simulation results show an increase in performance gains as compared to traditional Vanet architectures.
In the Name of the Practical: Unearthing the Hegemony of Pragmatics in the Discourse of Environmental Management

A prominent feature of the rapidly growing field of Environmental Management (EM) is its strong emphasis on pragmatic considerations. Much of EM's legitimacy stems from its own identity as having practical relevance in resolving contemporary environmental problems. With the help of Critical Discourse Analysis, our paper engages closely with the language of practicality in Environmental Management. We show how the message of practicality emerged through three core messages in the discourse, viz. economic utilitarianism, compromise and inter-organizational collaboration. We further contend that in the actual material context of the complex biospheric environment, these messages may have less than pragmatic implications. We conclude with a discussion of ecological rationality in redefining the discourse of pragmatics in Environmental Management.
Testing Social Cognitive Models of Mental Illness Stigma: The Prairie State Stigma Studies

Abstract Social cognitive models of stigma define the relationship among: signals that suggest a person is mentally ill, stigmatizing attitudes about the person with mental illness, affective reactions to the stigmatizing attitude, and behavior responses to these attitudes and emotions. The Prairie State Stigma Studies were a set of investigations completed over the past five years examining stigma from the perspective of the general public. Several of the studies examined path models that explain the relationship between stigmatizing attitudes and discriminatory behavior. Among the many results was the finding that stigmatizing attitudes about dangerousness were especially problematic, leading to fear and avoidance of persons with mental illness. The studies also examined ways to change stigmatizing attitudes and corresponding behaviors. Results suggested that contact with persons who are challenged by psychiatric disabilities has a broad and positive effect on public stigma. Future directions for research on social cognitive models of stigma are discussed.
Environmental/Economic Transaction Planning Using Multiobjective Particle Swarm Optimization and Non-Stationary Multi-Stage Assignment Penalty Function

This paper presents a new approach for environmental/economic transaction planning model of the electricity market. The environmental/economic transaction planning problem is formulated as a multi-objective optimal power flow (MOPF) problem. A novel algorithm using multiobjective particle swarm optimization (MOPSO) and non-stationary multi-stage assignment penalty function is proposed to solve this problem. PSO is modified by using dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Incorporating of non-stationary multistage assignment penalty function in solving OPF problem can improve the convergence. The proposed method is demonstrated on the IEEE 30-bus system. The results show that the proposed approach can efficiently gain multiple pareto optimal transaction planning that match with the sustainable development strategy
High density nanostructure transfer in soft molding using polyurethane acrylate molds and polyelectrolyte multilayers

Here we present an alternative, new, unconventional lithographic technique developed to create dense and multilevel nanostructure pattern transfer using a highly accurate polyurethane acrylate (PU, MINS101m, Minuta Tech.) mold and a polyelectrolyte multilayer as the adhesion promotion layer. Specifically, we demonstrate the pattern transfer of periodic 80 nm lines with 400 nm height and complex and multilevel nanostructures to a polymer layer on various substrates, such as Si or SiO2 wafers, glass and flexible polymer films. This new, unconventional lithographic technique presented here would open the door to a variety of applications in the fields of electronic, optical and biological devices.