Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibition of Protein Kinase CK2 Prevents Adipogenic Differentiation of Mesenchymal Stem Cells Like C3H/10T1/2 Cells

Protein kinase CK2 as a holoenzyme is composed of two catalytic α- or α’-subunits and two non-catalytic β-subunits. Knock-out experiments revealed that CK2α and CK2β are required for embryonic development. Little is known about the role of CK2 during differentiation of stem cells. Mesenchymal stem cells (MSCs) are multipotent cells which can be differentiated into adipocytes in vitro. Thus, MSCs and in particular C3H/10T1/2 cells are excellent tools to study a possible role of CK2 in adipogenesis. We found downregulation of the CK2 catalytic subunits as well as a decrease in CK2 kinase activity with progression of differentiation. Inhibition of CK2 using the potent inhibitor CX-4945 impeded differentiation of C3H/10T1/2 cells into adipocytes. The inhibited cells lacked the observed decrease in CK2 expression, but showed a constant expression of all three CK2 subunits. Furthermore, inhibition of CK2 resulted in decreased cell proliferation in the early differentiation phase. Analysis of the main signaling cascade revealed an elevated expression of C/EBPβ and C/EBPδ and reduced expression of the adipogenic master regulators C/EBPα and PPARγ2. Thus, CK2 seems to be implicated in the regulation of different steps early in the adipogenic differentiation of MSC.
IMPROVING PACKET CLASSIFICATION: LEARNING FROM TRAFFIC

Some rules in a packet classifier are more likely to be matched than others, however, we cannot know which rules are most likely to be matched a priori so a classifier should be able to adapt itself dynamically. This paper presents an investigation of using the traffic a classifier is seeing to tune the classifier's search structure and improve its average response time. To test this idea, the HICuts algorithm has been modified to restructure itself dynamically. The effects of restructuring are described via a modified Interval Decision Diagram (IDD) notation. We show experimental results based on realworld traces and rulesets to establish improvements in average case performance. Compared to a static HICuts classifier, the dynamic classifier performed significantly better in terms of both search depth and amount of work performed during classification. We also show results based on synthetic traces and rulesets that establish that the worst-case behavior of the dynamic classifier can be explicitly controlled, and in particular that it can be controlled to be no worse than that of the static classifier.
A Recommendation System Combining LDA and Collaborative Filtering Method for Scenic Spot

Researchers have long sought to find an effective and straightforward method to bridge the gap between us and big data. Especially during the big data era, how to find the needed information with rapid speed and exact result has become the central concerns of the internet users. This paper focuses on exploring the valuable data in UGC (User Generated Content), and recommending useful information to specified users. To achieve this goal, we model the social network, and then the LDA (Linear Discriminant Analysis), PCA (Principal Component Analysis) and KNN (K-Nearest Neighbour) algorithms are adopted to calculate the recommendation items. Our algorithm avoids the disadvantages of the common collaborative filtering algorithm that only behaviors is considered but without considering the behaviour results, thus our method effectively improves the accuracy of the recommendation system. Experimental results show that our algorithm improves the accuracy comparing with the CF algorithms.
SOFTCARDINALITY: Hierarchical Text Overlap for Student Response Analysis

In this paper we describe our system used to participate in the Student-Response-Analysis task-7 at SemEval 2013. This system is based on text overlap through the soft cardinality and a new mechanism for weight propagation. Although there are several official performance measures, taking into account the overall accuracy throughout the two availabe data sets (Beetle and SciEntsBank), our system ranked first in the 2 way classification task and second in the others. Furthermore, our system performs particularly well with “unseendomains” instances, which was the more challenging test set. This paper also describes another system that integrates this method with the lexical-overlap baseline provided by the task organizers obtaining better results than the best official results. We concluded that the soft cardinality method is a very competitive baseline for the automatic evaluation of student responses.
Interferon‐γ: an overview of signals, mechanisms and functions

Interferon‐γ (IFN‐γ) coordinates a diverse array of cellular programs through transcriptional regulation of immunologically relevant genes. This article reviews the current understanding of IFN‐γ ligand, receptor, ignal transduction, and cellular effects with a focus on macrophage responses and to a lesser extent, responses from other cell types that influence macrophage function during infection. The current model for IFN‐γ signal transduction is discussed, as well as signal regulation and factors conferring signal specificity. Cellular effects of IFN‐γ are described, including up‐regulation of pathogen recognition, antigen processing and presentation, the antiviral state, inhibition of cellular proliferation and effects on apoptosis, activation of microbicidal effector functions, immunomodulation, and leukocyte trafficking. In addition, integration of signaling and response with other cytokines and pathogen‐associated molecular patterns, such as tumor necrosis factor‐α, interleukin‐4, type I IFNs, and lipopolysaccharide are discussed.
An Ensemble Classification Approach for Intrusion Detection

cyber attacks in various forms compel everyone to implement effective intrusion detection systems for protecting their information wealth. From last two decades, there has been extensive research going on in intrusion detection system development using various techniques. But, designing detection systems producing maximum accuracy with minimum false positive is yet a challenging task for the research community. Ensemble method is one of the major developments in the field of machine learning. In this research work, new ensemble classification method is proposed from different classifiers. Support vector machine techniques, artificial neural network and random forest are used for classification. Ensemble model is formed for producing better result. The model shows promising result for all classes of attacks.
Networked texts: discourse, power and gender neutrality in Ugandan physics textbooks

ABSTRACT Research within science textbooks has dominantly focused on examining explicit representations of women and men using quantitative methodology. The assumption that gendered arrangements are necessarily explicit and therefore visible and countable, overlooks how power works explicitly and implicitly through discourse to produce specific gendered subjectivities. In taking up feminist post-structuralisms, this study contributes to textbook studies within sciences by illuminating both explicit and implicit representations of gender. Using discourse analysis, ‘gender-neutral’ and/or disembodied subjects and objects were ‘unmasked,’ revealing a generic male and/or masculine subject. Gender-neutrality, which is pervasive within the physics textbooks, was thus exposed as a mask for generic maleness/masculinity. I argue that this objectivist science, which remains compatible with a narrow range of student gendered identities, forecloses possibilities for a wide range of scientist subjectivities, to produce a more inclusive physics curriculum, with a greater possibility of developing physics using diverse subjectivities.
- 3-Design and Testing of a CPU Emulator

In this paper, we describe the design and testing process of an x86 CPU model for the Giano simulator. We used a hardware-oracle for test generation and as a reference behavioral specification. We developed an automatic tester that extracts the CPU specifications from the published CPU datasheets, and automatically generates test cases with complete test coverage. The number of required tests is reduced by many orders of magnitude by the separate testing of operand routing from computation. Debugging efficacy is such that the tester is now used concurrently with the development process, not afterwards. The hardware-oracle detects and documents several undocumented or erroneously-specified CPU behaviors which would be difficult to detect by conventional testing methods.
Bounded Planning in Passive POMDPs

In Passive POMDPs actions do not affect the world state, but still incur costs. When the agent is bounded by information-processing constraints, it can only keep an approximation of the belief. We present a variational principle for the problem of maintaining the information which is most useful for minimizing the cost, and introduce an efficient and simple algorithm for finding an optimum.
Nutritional adaptations of native plants of the cerrado biome in acid soils

Soils of the cerrado biome, mostly oxisols and deep sandy entisols, are acid, dystrophic and poor in available nutrients. These soils are not very different from soils that occur in the Amazon region. However, the open savanna physiognomies of cerrado with lower biomass of their different components are deficient in nutrients at the ecosystem level, unlike the Amazon forests which retain high nutrient reserves in their live biomass. Field crops are susceptible to aluminum and manganese toxicities, besides nutrient deficiencies, in cerrado soils and do not grow well in the absence of liming and fertilization. However, concepts of nutrient deficiencies and toxicities, well established for cultivated plants, should not be extended to native species in natural ecosystems, indiscriminately. Many native plants in the cerrado biome are resistant or tolerant to soil conditions deemed unfavorable for cultivated plants but their geographic distribution, frequency in native communities, growth and productivity are determined by water and nutrient availability and other edaphic conditions. Species growing on acid soils are aluminum tolerant or resistant, since their capacity to absorb essential nutrients, growth and reproduction is not affected by high aluminum levels in the soil. Many common species of the cerrado, instead of excluding aluminum, absorb and transport it to leaves and accumulate it in different tissues including leaves and seeds whereas others do not survive in the absence of exchangeable aluminum, even though no specific role of Al in plant metabolism is yet established.
Generation of executable test cases based on behavioral UML system models

The further automation of the test development process beyond the automatic execution of tests is an increasing challenge, because the development of new functionalities has a higher pace than the test development. The use of model based techniques combined with test generation methods enable a fast test definition while the test oracle is calculated automatically. The presented approach was implemented in a research prototype, which was used to generate test cases out of an UML state chart model describing the behavior of the system under test. The resulting test sequences were executed on a hardware in the loop (HiL) and showed its applicability in an industrial setting.
A Dynamic Trading Strategy Approach to Deviations from Uncovered Interest Parity

Foreign exchange predictability is explored from the standpoint of generating explicit dynamic strategies. These are used to assess the economic signicance and economic magnitude of the predictability present in exchange rates. It is found that considering the true out of sample behavior of certain dynamic foreign exchange strategies greatly reduces the magnitude of previously reported puzzles.
Performance driven data cache prefetching in a dynamic software optimization system

Software or hardware data cache prefetching is an efficient way to hide cache miss latency. However effectiveness of the issued prefetches have to be monitored in order to maximize their positive impact while minimizing their negative impact on performance. In previous proposed dynamic frameworks, the monitoring scheme is either achieved using processor performance counters or using specific hardware. In this work, we propose a prefetching strategy which does not use any specific hardware component or processor performance counter. Our dynamic framework wants to be portable on any modern processor architecture providing at least a prefetch instruction. Opportunity and effectiveness of prefetching loads is simply guided by the time spent to effectively obtain the data. Every load of a program is monitored periodically and can be either associated to a dynamically inserted prefetch instruction or not. It can be associated to a prefetch instruction at some disjoint periods of the whole program run as soon as it is efficient. Our framework has been implemented for Itanium-2 machines. It involves several dynamic instrumentations of the binary code whose overhead is limited to only 4% on average. On a large set of benchmarks, our system is able to speed up some programs by 2%--143%.
Sampling Decisions in Optimum Experimental Design in the Light of Pontryagin's Maximum Principle

Optimum experimental design (OED) problems are optimization problems in which an experimental setting and decisions on when to measure---the so- called sampling design---are to be determined such that a follow-up parameter estimation yields accurate results for model parameters. In this paper we use the interpretation of OED as optimal control problems with a very particular structure for the analysis of optimal sampling decisions. We introduce the information gain function, motivated by an analysis of necessary conditions of optimality. We highlight differences between problem formulations and propose to use a linear penalization of sampling decisions to overcome the intrinsic ill-conditioning of OED. The results of this paper are independent from the actual numerical method to compute the solution to the OED problem and of the question of local and global optima.
Generation of signals activating neutrophil functions by leukocyte integrins: LFA-1 and gp150/95, but not CR3, are able to stimulate the respiratory burst of human neutrophils

To address the question whether leukocyte integrins are able to generate signals activating neutrophil functions, we investigated the capability of mAbs against the common beta chain (CD18), or the distinct alpha chains of CR3, LFA-1, or gp150/95, to activate neutrophil respiratory burst. These investigations were performed with mAbs bound to protein A immobilized to tissue culture polystyrene. Neutrophils plated in wells coated with the anti-CD18 mAbs IB4 and 60.3 released H2O2; H2O2 release did not occur when neutrophils were plated in wells coated with an irrelevant, isotype-matched mAb (OKDR), or with mAbs against other molecules (CD16, beta 2-microglobulin) expressed on the neutrophil surface at the same density of CD18. Four different mAbs, OKM1, OKM9, OKM10, 60.1, which recognize distinct epitopes of CR3 were unable to trigger H2O2 or O2- release from neutrophils. However, mAbs against LFA-1 or gp150/95 triggered both H2O2 and O2- release from neutrophils. Stimulation of neutrophils respiratory burst by both anti- CD18, and anti-LFA-1 or gp150/95 mAbs was totally inhibited by the microfilaments disrupting agent, cytochalasin B, and by a permeable cAMP analogue. While the capability to activate neutrophil respiratory burst was restricted to anti-LFA-1 and gp150/95 mAbs, we observed that mAbs against all members of leukocyte integrins, including CR3, were able to trigger neutrophil spreading. These findings indicate that, in neutrophils, all three leukocyte integrins can generate signals activating spreading, but only LFA-1 and gp150/95 can generate signals involved in activation of the respiratory burst. This observation can be relevant to understand the mechanisms responsible for the activation of neutrophil respiratory burst by tumor necrosis factor-alpha, which has been shown to be strictly dependent on expression of leukocyte integrins (Nathan, C. , S. Srimal, C. Farber, E. Sanchez, L. Kabbash, A. Asch, J. Gailit, and S. Wright. 1989. J. Cell Biol. 109:13411349.
Transparent conductive glass for biopotential recordings

Transparent conductive oxides are investigated for use as noninvasive, surface biopotential electrode. Performance of the ITO electrode is compared against conventional Ag/AgCl electrodes. Results show that the ITO electrodes have an electrode-skin impedance that is approximately an order of magnitude higher than Ag/AgCl electrodes. The electrocardiogram is similar in the time and frequency domain. Results suggest that the ITO electrodes do not perform as well as Ag/AgCl electrodes but could be comparable to other dry electrodes (e.g., stainless steel). While ITO electrodes are probably not suited for clinical diagnostic ECG applications, their transparent feature could be useful in other applications, such as medical biometrics.
Cycle-based Model to Evaluate Consistency Protocols within a Multi-protocol Compilation Tool-chain

Many-core processors are made by hundreds to thousands cores, distributed memories and a dedicated network on a single chip. In this context, and because of the scale of the processor, providing a shared memory system has to rely on efficient hardware mechanisms and/or data consistency protocols. Some works explored several consistency mechanisms designed for many-core processors. They lead to the conclusion that there won't exist one protocol that fits to all applications and hardware contexts. Therefore, it sounds relevant to use a multi-protocol platform, in which shared data of the application can be managed by different protocols. Protocols are chosen and configured at compile time, following a static analysis of the application and the profiling of memory accesses. In this work, we propose a high-level timed model that we use to evaluate, at compile time, the consistency protocol which has been assigned to a given application and a given Network-on-Chip (NoC). This model allows to calculate the number of NoC cycles needed for each data access, that can be turned into mean access cycles for each core or each shared data. The model is not as accurate as a cycle-based NoC simulator or an instruction set simulator. However, it is accurate enough to evaluate the impact of choosing and configuring a protocol, and its lightweight implementation allows to run within an operational research optimization loop. To validate our approach, we apply the model to compare three consistency protocols, on a 2D mesh network, compiling a parallel convolution application.
Metastability of R-Charged Black Holes

The global stability of R-charged AdS black holes in a grand canonical ensemble is examined by eliminating the constraints from the action, but without solving the equations of motion, thereby constructing the reduced action of the system. The metastability of the system is found to set in at a critical value of the chemical potential which is conjugate to the R-charge. The relation among the small black hole, large black hole and the instability is discussed. The result is consistent with the metastability found in the AdS/CFT-conjectured dual field theory. The “renormalized” temperature of AdS black holes, which has been rather ad hoc, is suggested to be the boundary temperature in the sense of AdS/CFT correspondence. As a byproduct of the analysis, we find a more general solution of the theory and its properties are briefly discussed.
Fast Heuristics for Near-Optimal Task Allocation in Data Stream Processing over Clusters

We study provisioning and job reconfiguration techniques for adapting to execution environment changes when processing data streams on cluster-based deployments. By monitoring the performance of an executing job, we identify computation and communication bottlenecks. In such cases we reconfigure the job by reallocating its tasks to minimize the communication cost. Our work targets data-intensive applications where the inter-node transfer latency is significant. We aim to minimize the transfer latency while keeping the nodes below some computational load threshold. We propose a scalable centralized scheme that employs fast allocation heuristics. Our techniques are based on a general group-based job representation that is commonly found in many distributed data stream processing frameworks. Using this representation we devise linear-time task allocation algorithms that improve existing quadratic-time solutions in practical cases. We have implemented and evaluated our proposals using both synthetic and real-world scenarios. Our results show that our algorithms: (a) exhibit significant allocation throughput while producing near-optimal allocations, and (b) significantly improve existing task-level approaches.
A Low-Cost Cache Coherence Verification Method for Snooping Systems

Due to modern technology trends such as decreasing feature sizes and lower voltage levels, fault tolerance is becoming increasingly important in computing systems. Shared memory in modern multiprocessor systems is supported by cache coherence mechanisms. The correctness of cache coherence of the system is crucial for the data integrity. This work proposes an error detection scheme for snooping-based cache coherence protocols. For the widely used MESI coherence protocol, the proposed method does not introduce any performance overhead. Only a limited amount of additional hardware is required. Existing systems can be easily extended to support the proposed technique. Almost all single faults that are able to affect data integrity in the system are covered, with the exception of a few very rare cases. Experimental results involving fault injection do not encounter any undetected faults leading to corrupted application output.
Noun Classification From Predicate-Argument Structures

A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification.
Spread spectrum for chaotic compressed sensing techniques in parallel magnetic resonance imaging

We consider the fast acquisition problem in magnetic resonance imaging (MRI). Often, fast acquisition is achieved using parallel imaging (pMRI) techniques. It has been shown recently that compressed sensing (CS), which enables exact reconstruction of sparse or compressible signals from a small number of random measurements, can accelerate the speed of MRI acquisition because the number of measurements was small. Recently, the spread spectrum (SS) has been utilized to enhance the quality of reconstructed CS image. Also recently, chaotic CS approach potentially offers simpler hardware implementation. In this paper, we combine chaotic CS and spread spectrum technique in order to obtain a fast acquisition in MRI with enhanced quality of reconstructed image. The performance of the proposed method is analyzed using numerical simulation.