Miracle-dz/UniFAR-FTUs huggingface dataset:
Cytokine Induction of VCAM-1 but Not IL13Rα2 on Glioma Cells: A Tale of Two Antibodies

The interleukin-13 receptor alpha2 (IL13Rα2) is a cell surface receptor that is over-expressed by a subset of high-grade gliomas, but not expressed at significant levels by normal brain tissue. For both malignant and non-malignant cells, IL13Rα2 surface expression is reported to be induced by various cytokines such as IL-4 or IL-13 and tumor necrosis factor (TNF). Our group has developed a therapeutic platform to target IL13Rα2-positive brain tumors by engineering human cytotoxic T lymphocytes (CTLs) to express the IL13-zetakine chimeric antigen receptor. We therefore sought to investigate the potential of cytokine stimulation to induce IL13Rα2 cell surface expression, and thereby increase susceptibility to IL13Rα2-specific T cell killing. In the course of these experiments, we unexpectedly found that the commercially available putative IL13Rα2-specific monoclonal antibody B-D13 recognizes cytokine-induced VCAM-1 on glioblastoma. We provide evidence that the induced receptor is not IL13Rα2, because its expression does not consistently correlate with IL13Rα2 mRNA levels, it does not bind IL-13, and it is not recognized by IL13-zetakine CTL. Instead we demonstrate by immunoprecipitation experiments and mass spectrometry that the antigen recognized by the B-D13 antibody following cytokine stimulation is VCAM-1, and that VCAM-1, but not IL13Rα2, is induced on glioma cells by TNF alone or in combination with IL-13 or IL-4. Further evaluation of several commercial B-D13 antibodies revealed that B-D13 is bi-specific, recognizing both IL13Rα2 and VCAM-1. This binding is non-overlapping based on soluble receptor competition experiments, and mass spectrometry identifies two distinct heavy and light chain species, providing evidence that the B-D13 reagent is di-clonal. PE-conjugation of the B-D13 antibody appears to disrupt IL13Rα2 recognition, while maintaining VCAM-1 specificity. While this work calls into question previous studies that have used the B-D13 antibody to assess IL13Rα2 expression, it also suggests that TNF may have significant effects on glioma biology by up-regulating VCAM-1.
Single-Carrier FDMA or Cyclic-Prefix CDMA With Optimized Spreading Sequences?

To meet the data rate and quality-of-service requirements of the future cellular systems, new air interfaces are currently under development. In this paper, we compare two air interfaces of particular interest for the uplink: (1) cyclic-prefix code-division multiple access (CP-CDMA), which has been proposed in the literature as an evolution of direct-sequence code-division multiple access (DS-CDMA) because it enables the low-complexity equalization of the multipath channel in the frequency domain, and (2) single-carrier frequency-division multiple access (SC-FDMA), which has recently been proposed in the long-term evolution of the Third-Generation Partnership Project (3GPP) standard because it enables the easy separation of users in the frequency domain. We analytically demonstrate that SC-FDMA is a special case of CP-CDMA, in which the CDMA codes have been optimized to minimize symbol estimation mean square error under a constraint of received power. Numerical results confirm that SC-FDMA significantly outperforms CP-CDMA at high user loads.
Log analysis in cloud computing environment with Hadoop and Spark

Log is the main source of the system operation status, user behavior analysis etc. Log analysis system needs not only the massive and stable data processing ability but also the adaptation to a variety of scenarios under the requirement of efficiency, which can't be achieved from standalone analysis tools or even single cloud computing framework. We present a unified cloud platform for batch log data analysis with the combination of Hadoop and Spark. Hadoop provides a distributed file system and off-line batch computing framework, while the computing pattern in Spark is based on distributed memory. The joint of Hadoop, Spark and the data warehouse and analysis tools of Hive and Shark makes it possible to provide a unified cloud platform with batch analysis and in-memory computing capacity in order to process log in a high available, stable and efficient way.
Dynamic Informative Advertising of New Experience Goods

This paper analyzes the optimal informative advertising and price policies of a monopolist who sells a new experience good over time to a population of heterogeneous buyers. Under certain conditions, the advertising rate first increases and then decreases over the marketing cycle with a peak occurring at the end of the introductory period when prices are low. Advertising lowers introductory prices but also shortens the period during which they are offered. Advertising raises the share of consumers who know their valuation in the long-run but not necessarily in the short-run
Path-Loss Prediction Models for Intervehicle Communication at 60 GHz

Propagation path losses affecting intervehicle communication (IVC) at 60 GHz are presented. In order to examine the radio-propagation characteristics in the IVC system, in the first stage of our investigation, we have carried out propagation tests between two vehicles communicating in a line-of-sight (LOS) situation at fixed positions on a smooth surface paved with asphalt, which is the normal communication condition. Furthermore, the non-LOS (NLOS) situation with up to three intermediate vehicles, which can cause an obstruction, has been conducted. To construct path-loss models in NLOS cases, the uniform theory of diffraction technique was applied to the calculation of waves propagating through the intermediate vehicles. The propagation path models were derived from the measured results. In the second stage, a path-loss prediction formula was derived by statistically processing the data that had been calculated using the propagation path model. The propagation tests between the vehicles in motion confirm that the path-loss prediction formula is very useful when the communicating vehicles are moving, and it is therefore suitable for designing the cells that form the IVC.
Adoption of the Internet and the Information Superhighway for electronic commerce

The Internet has made a major impact on electronic commerce and market structures by facilitating information exchange between organizations. For researchers and business professionals, the economic and social ramifications of the Internet have generated much interest. Inspired by the many studies done on the information technology (IT) adoption, particularly Everett Roger's research in diffusion of innovation, we develop an Internet adoption model using economic theories such as asset specificity, installed base effect, network externality, and transaction cost theory. This paper will show that the adoption of the Internet for electronic commerce involves several issues. The model can serve as a guideline for evaluating an organization's readiness to adopt the Internet.
Sialylation of the Host Receptor May Modulate Entry of Demyelinating Persistent Theiler's Virus

ABSTRACT Theiler's murine encephalomyelitis virus (TMEV) is a picornavirus of the Cardiovirus genus. Certain strains of TMEV may cause a chronic demyelinating disease, which is very similar to multiple sclerosis in humans, associated with a persistent viral infection in the mouse central nervous system (CNS). Other strains of TMEV only cause an acute infection without persistence in the CNS. It has been shown that sialic acid is a receptor moiety only for the persistent TMEV strains and not for the nonpersistent strains. We report the effect of sialylation on cell surface on entry and the complex structure of DA virus, a persistent TMEV, and the receptor moiety mimic, sialyllactose, refined to a resolution of 3.0 Å. The ligand binds to a pocket on the viral surface, composed mainly of the amino acid residues from capsid protein VP2 puff B, in the vicinity of the VP1 loop and VP3 C terminus. The interaction of the receptor moiety with the persistent DA strain provides new understanding for the demyelinating persistent infection in the mouse CNS by TMEV.
Genetic Evidence for the Phylogenetic Relationship between Na-Dene and Yeniseian Speakers

Ruhlen's hypothesis, based on linguistic evidence, for a common phylogenetic origin of Na-Dene and Yeniseian speakers is tested using genetic data. Gene frequency data for the Kets, the only surviving Yeniseian speakers, were collected during a field study in 1993. Data for several Na-Dene groups, as well as other New World and Siberian populations, were compiled from the literature. These data were analyzed using R-matrix, principal components analysis, and Mantel tests. In a comparison of 10 New World and Siberian populations using eight alleles, 55.8% of the variation was accounted for by the first principal component, and 22.1% of the variation was subsumed by the second principal component. Contrary to Ruhlen's interpretation of the linguistic data, analysis of the genetic data shows that the Na-Dene cluster with other Native American populations, while the Kets genetically resemble the surrounding Siberian groups. This conclusion is further supported by correlations that are higher when the Kets are considered unrelated to Na-Dene speakers, and an insignificant partial correlation between genes and language when geography is held constant, indicating that spatial patterning accounts for most of the variation present in these populations.
Classifying Lexical-semantic Relationships by Exploiting Sense/Concept Representations

This paper proposes a method for classifying the type of lexical-semantic relation between a given pair of words. Given an inventory of target relationships, this task can be seen as a multi-class classification problem. We train a supervised classifier by assuming: (1) a specific type of lexical-semantic relation between a pair of words would be indicated by a carefully designed set of relation-specific similarities associated with the words; and (2) the similarities could be effectively computed by “sense representations” (sense/concept embeddings). The experimental results show that the proposed method clearly outperforms an existing state-of-the-art method that does not utilize sense/concept embeddings, thereby demonstrating the effectiveness of the sense representations.
Building Trust in a Human-Robot Team with Automatically Generated Explanations

Technological advances offer the promise of robotic systems that work with people to form human-robot teams that are more capable than their individual members. Unfortunately, the increasing capability of such autonomous systems has often failed to increase the capability of the human-robot team. Studies have identified many causes underlying these failures, but one critical aspect of a successful human-machine interaction is trust. When robots are more suited than humans for a certain task, we want the humans to trust the robots to perform that task. When the robots are less suited, we want the humans to appropriately gauge the robots’ ability and have people perform the task manually. Failure to do so results in disuse of robots in the former case and misuse in the latter. Real-world case studies and laboratory experiments show that failures in both cases are common. Researchers have theorized that people will more accurately trust an autonomous system, such as a robot, if they have a more accurate understanding of its decision-making process. Studies show that explanations offered by an automated system can help maintain trust with the humans in case the system makes an error, indicating that the robot’s communication transparency can be an important factor in earning an appropriate level of trust. To study how robots can communicate their decision-making process to humans, we have designed an agent-based online test-bed that supports virtual simulation of domain-independent human-robot interaction. In the simulation, humans work together with virtual robots as a team. The test-bed allows researchers to conduct online human-subject studies and gain better understanding of how robot communication can improve human-robot team performance by fostering better trust relationships between humans and their robot teammates. In this paper, we describe the details of our design, and illustrate its operation with an example human-robot team reconnaissance task.
Sudoku puzzles and how to solve them

A Sudoku puzzle (of ‘classical type’) consists of a 9-by-9 matrix partitioned into nine 3-by-3 submatrices (‘boxes’). Some of the entries are given, and the puzzle is to find the remaining entries, under the condition that the nine rows, the nine columns, and the nine boxes all contain a permutation of the symbols of some given alphabet of size 9, usually the digits 1-9, or the letters A-I. Some mathematicians will claim that since this is a finite problem, it is trivial. The time needed to solve a Sudoku puzzle is O(1) indeed, one can always try the 9 possible ways of filling the grid. But one can still ask for efficient ways of finding a solution. Or, if one knows the solution already, one can ask for a sequence of logical arguments one can use to convince someone else of the fact that this really is the unique solution.
Earnings within Education Groups and Overall Productivity Growth

To offer a possible interpretation for recent empirical findings on earnings growth, this paper constructs a simple model with endogenous human capital investment, a distribution of natural abilities, and unbiased technological progress. The model predicts that in the long run, average earnings within any education group will grow more slowly than average wages overall. It also predicts that average earnings in high‐education groups ultimately will rise relative to average earnings in low‐education groups. In the model, these processes do not imply secular increases in the degree of inequality in the overall cross‐sectional distribution of earnings.
Identification of rat hepatocyte plasma membrane proteins using monoclonal antibodies

We have localized and identified five rat hepatocyte plasma membrane proteins using hybridoma technology in combination with morphological and biochemical methods. Three different membrane preparations were used as immunogens: isolated hepatocytes, a preparation of plasma membrane sheets that contained all three recognizable surface domains of the intact hepatocyte (sinusoidal, lateral, and bile canalicular), and a glycoprotein subfraction of that plasma membrane preparation. We selected monoclonal IgGs that were hepatocyte specific and localized them using both immunofluorescence on 0.5-micron sections of frozen liver and immunoperoxidase at the ultrastructural level. One antigen (HA 4) was localized predominantly to the bile canalicular surface, whereas three (CE 9, HA 21, and HA 116) were localized predominantly to the lateral and sinusoidal surfaces. One antigen (HA 16) was present in all three domains. Only one antigen (HA 116) could be detected in intracellular structures both in the periphery of the cell and in the Golgi region. The antigens were all integral membrane proteins as judged by their stability to alkaline extraction and solubility in detergents. The apparent molecular weights of the antigens were established by immunoprecipitation and/or immunoblotting. In a related study (Bartles, J.R., L.T. Braiterman, and A.L. Hubbard, 1985, J. Cell. Biol., 100:1126-1138), we present biochemical confirmation of the domain-specific localizations for two of the antigens, HA 4 and CE 9, and demonstrate their suitability as endogenous domain markers for monitoring the separation of bile canalicular and sinusoidal lateral membrane on sucrose density gradients.
Six Steps to Overcome Bias in the Forecast Process

A technically sound forecast goes nowhere if it is not accepted by those in power, whether they are politicians or upper management. Elaine notes that often the forecast gets manipulated to satisfy political ends or meet targets and plans. Politics is also about biases, motivations, and interests, and comes into play when various incentives and reward structures influence the forecast. She maintains that we need to examine some common problems that organizations face and provide solutions that allow the technically sound forecast to prevail, free of manipulation or bias. She describes a case study in the politics of budget forecasting and offers six strategies to overcoming organizational politics. Copyright International Institute of Forecasters, 2005
Recent Advances in Antimicrobial Polymers: A Mini-Review

Human safety and well-being is threatened by microbes causing numerous infectious diseases resulting in a large number of deaths every year. Despite substantial progress in antimicrobial drugs, many infectious diseases remain difficult to treat. Antimicrobial polymers offer a promising antimicrobial strategy for fighting pathogens and have received considerable attention in both academic and industrial research. This mini-review presents the advances made in antimicrobial polymers since 2013. Antimicrobial mechanisms exhibiting either passive or active action and polymer material types containing bound or leaching antimicrobials are introduced. This article also addresses the applications of these antimicrobial polymers in the medical, food, and textile industries.
"Popularity Effect" in User-Generated Content: Evidence from Online Product Reviews

Online product reviews are increasingly important for consumer decisions, yet we still know little about how reviews are generated in the first place. In an effort to gather more reviews, many websites encourage user interactions such as allowing one user to subscribe to another. Do these interactions actually facilitate the generation of product reviews? More importantly, what kind of reviews do such interactions induce? We study these questions using data from one of the largest product review websites where users can subscribe to one another. By applying both panel data and a flexible matching method, we find that as users become more popular, they produce more reviews and more objective reviews; however, their numeric ratings also systematically change and become more negative and more varied. Such trade-off has not been previously documented and has important implications for both product review and other user-generated content websites.
Developmental Trajectories of Internally and Externally Driven Temporal Prediction

The ability to generate temporal prediction (TP) is fundamental to our survival since it allows us to selectively orient our attention in time in order to prioritize relevant environmental information. Studies on adult participants showed that externally and internally driven mechanisms can be engaged to establish TP, both resulting in better behavioural performance. However, few studies on children have investigated the ability to engage internally and externally driven TP, especially in relation to how these mechanisms change across development. In this study, 111 participants (88 children between six and eleven years of age, and 23 adults) were tested by means of a simple reaction time paradigm, in which temporal cueing and neutral conditions were orthogonally manipulated to induce externally and internally driven TP mechanisms, as well as an interaction between the two. Sequential effects (SEs) relative to both tasks were also investigated. Results showed that all children participating in the study were able to implement both external and internal TP in an independent fashion. However, children younger than eight years were not able to combine both strategies. Furthermore, in the temporal cueing blocks they did not show the typically-observed asymmetric SE pattern. These results suggest that children can flexibly use both external and internal TP mechanisms to optimise their behaviour, although their successful combined use develops only after eight years of age.
Learning to Match and Cluster Entity Names

Information retrieval is, in large part, the study of methods for assessing the similarity of pairs of documents. Document similarity metrics have been used for many tasks including ad hoc document retrieval, text classification [YC1994], and summarization [GC1998,SSMB1997]. Another problem area in which similarity metrics are central is record linkage (e.g., [KA1985]), where one wishes to determine if two database records taken from different source databases refer to the same entity. For instance, one might wish to determine if two database records from two different hospitals, each containing a patient’s name, address, and insurance information, refer to the same person; as another example, one might wish to determine if two bibliography records, each containing a paper title, list of authors, and journal name, refer to the same publication.
Efficient Implementation of Error Detection Functions

As semiconductor technology advances, the size of t he transistors is shrinking. This technology has reach ed a point where the potential of transient errors is becoming significant even for combinational logic. Previous efforts to detect such errors have used a general approach applied to the entire circuit. In this project, the error detection efforts are tailored t o the specifics of the targeted circuit. A new measure of rror susceptibility is defined which provides for simple r tradeoff analysis. The critical path of the circuit , with low threshold transistors, is analyzed first as the most likely starting point, followed by other parts of t he circuit with high error susceptibility. This approa ch is shown to be efficient for error detection and provi des valuable tradeoff input to the designer of error detection circuits. The error handling is detection ly, not correction or prevention, placing the responsib ility for correction on the system software.
Microarchitectural synthesis of performance-constrained, low-power VLSI designs

New portable signal-processing applications such as mobile telephony, wireless computing, and personal digital assistants place stringent power consumption limits on their constituent components. Substantial power servings can be realized if 5 V designs are translated to use the new lower supply voltage standards. This conversion, however is not achieved easily: a design originally targeted for implementation in a 5 V technology will typically require significant re-work to meet timing and throughput requirements at the lower operating voltage. We describe a high-level synthesis system which assists the designer in performing this task, minimizing the need for manual re-design. Techniques employed in this work include pipelining and a new approach to module selection which minimizes power consumption subject to timing constraints. Using these and other high-level synthesis techniques to target designs to 3.3 V libraries, we show that it is possible to reduce power consumption by as much as 56% as compared to the original 5 V implementation, while meeting specified minimum throughput and maximum latency constraints. <<ETX>>
Super-resolution using DCT based learning with LBP as feature model

In this paper, we propose a novel learning based technique for feature preserving super-resolution of a low resolution observation. The local geometry of an image is conveyed by image features such as edges, corners and curves. We encode these features with local binary pattern operator. The missing high resolution features of the low resolution observation are learnt in the form of discrete cosine transform coefficients from high resolution images in the training database. Experiments are conducted on real world natural images and results are compared with the standard interpolation techniques. Both the qualitative and quantitative comparisons show the effectiveness of the proposed approach.