Miracle-dz/UniFAR-FTUs huggingface dataset:
rRNA synthesis inhibitor, CX-5461, activates ATM/ATR pathway in acute lymphoblastic leukemia, arrests cells in G2 phase and induces apoptosis

Ribosome biogenesis is a fundamental cellular process and is elevated in cancer cells. As one of the most energy consuming cellular processes, it is highly regulated by signaling pathways in response to changing cellular conditions. Many of the regulators of this process are aberrantly activated in various cancers. Recently two novel rRNA synthesis inhibitors, CX-5461 and BMH-21, have been shown to selectively kill cancer cells while sparing normal cells. Here, we tested the effectiveness of pre-rRNA synthesis inhibitor CX-5461 on acute lymphoblastic leukemia cells with different cytogenetic abnormalities. Acute lymphoblastic leukemia cells are more sensitive to rRNA synthesis inhibition compared to normal bone marrow cells. CX-5461 treated cells undergo caspase-dependent apoptosis independent of their p53 status. More-over, CX5461, activates checkpoint kinases and arrests cells in G2 phase of cell cycle. Finally, overcoming this G2 arrest by inhibiting ATR kinase leads to robust cell killing. These results show that CX-5461 can be even more potent in combination with ATR inhibitors.
Design, Development, and Optimization of Dexibuprofen Microemulsion Based Transdermal Reservoir Patches for Controlled Drug Delivery

The aim of the study was to develop a reservoir-type transdermal patch for a controlled delivery of dexibuprofen and to evaluate its in vivo anti-inflammatory activity in Albino Wistar rats. In order to develop these patches, six formulations of dexibuprofen microemulsion comprising ethyl oleate, Tween 80: PG (2 : 1), and water were prepared by simplex lattice design and characterized. The reservoir compartment was filled with these microemulsions and in vitro release and skin permeation were assessed. The optimized patch was obtained on the basis of the responses: Q24 and flux. The impact of drug loading, surface area, membrane thickness, adhesive, and agitation speed on drug release and permeation was also studied. The skin sensitivity reaction and in vivo anti-inflammatory activity of optimized patch were evaluated. Stability study at three different temperatures for three months was carried out. The result suggests that a membrane based patch with zero-order release rate, Q24 of 79.13 ± 3.08%, and maximum flux of 331.17 µg/cm2h can be obtained exhibiting suitable anti-inflammatory activity with no visible skin sensitivity reaction. The outcomes of stability study recommend storage of patches at 4°C having shelf-life of 6.14 months. The study demonstrates that the reservoir-type transdermal patch of dexibuprofen microemulsion has a potential of delivering drug across skin in controlled manner with required anti-inflammatory activity.
The minimum biofuel requirement and the oil monopolist

The aim of the thesis is to describe how the minimum biofuel requirement affects the monopolistic oil markets. This is an increasingly important questions, because the governments are smoothing the biofuel’s way into the fuel markets for both the economical and environmental reasons. The focus in this thesis is mainly on the minimum biofuel requirement, but at times that policy is compared also to a subsidizing policy. To answer the research question of how the oil markets are affected by the minimum biofuel requirement, the interaction between the oil, fuel and biofuel markets was defined. From that the residual oil demand was derived, which was applied into two different cost structures, a static and a dynamic one. Solving these two cases both analytically and numerically reveals their price and quantity paths for oil, fuel and biofuel as well as the oil stock’s path. The final step of the analysis was to derive how changing the minimum biofuel requirement affects these paths. The resulting contributions from this thesis are theoretical and methodological. Numerical results are not given as no real, empirical demands, supplies and costs were used. The first contribution relates to defining the residual oil demand in case of the minimum biofuel requirement and taking time aspect into account, which forces the minimum biofuel requirement break at a certain production levels at a certain time instant. The second contribution relates to handling the production path when a monopolist faces a substitute, and that affects the price and quantity paths. Solving these paths with the static and the dynamic cost structures and analyzing the changing biofuel requirements on them is the third contribution. The final contribution of this thesis relates to presenting a subsidized biofuel case, and showing that contrary to some results in literature, the biofuel subsidy does not neccessarily postpone the oil exhaustion in linear demands.
Joint Design of Optimal Precoding and Cooperative Jamming for Multiuser Secure Broadcast Systems

A multiuser secure broadcast system is considered, where there are one multiantenna base station (BS), multiple single-antenna legitimate users, one multiantenna friendly jammer (FJ), and one multiantenna eavesdropper (Eve). We jointly design the optimal precoding matrix at the BS and the jamming covariance matrix at the FJ by minimizing the total transmit power of the BS and FJ under the signal-to-interference-plus-noise ratio constraints at the users and Eve. To solve this challenging problem, when the FJ has more antennas than the users and Eve, we first find the optimal structure of the jamming covariance matrix and, then, convert the problem into its equivalent convex form. Also, we propose an iterative algorithm to jointly design the precoding and jamming covariance matrices in all scenarios. The solution obtained by this algorithm is shown to be asymptotically optimal when the FJ has more antennas than the users and Eve. Numerical results show that the proposed schemes considerably outperform the existing schemes.
Sagan in TAC2010: A Machine Learning Approach to RTE within a Corpus

This paper describes the Sagan system in the context of the Sixth Pascal Recognizing Textual Entailment (RTE6) and the RTE task within a Corpus. The system employs a Support Vector Machine classifier which uses semantic similarity metrics to sentence level using only WordNet as source of knowledge, and co-reference analysis. Additionally, we proposed a baseline to the Novelty Detection subtask.
Accountability in morphological borrowing: Analyzing a linguistic subsystem as a sociolinguistic variable

ABSTRACT Principles of structural borrowing have been proposed, relating to structures of the languages involved and sociodemographic circumstances of their respective societies. This article quantitatively evaluates the roles of both linguistic and social factors in structural borrowing via examination of language contact data from Aruba and Curaçao, where creole Papiamentu is in contact with Spanish, Dutch, and English. Variationist methods, rooted in Labov's Principle of Accountability, are applied in a novel way to the system of verbal morphology to flesh out factors promoting borrowing. Linguistic factors are found to be quantitatively stronger, and only one nonlinguistic factor was found to promote borrowing. Results are discussed in light of prevailing theories of language contact. Findings contribute to our understanding of the long-term consequences of language contact and the relationship of contact-induced change to a more general sociolinguistic theory of language variation and change.
Nucleosomal Fluctuations Govern the Transcription Dynamics of RNA Polymerase II

Gradual Unpacking Eukaryotic DNA is packaged onto nucleosomes, which form the main constituent of chromatin. This packaging material presents a barrier to accessing the genome by the various machineries that need to deal with the DNA: replication, recombination, repair, and transcription complexes, for example. Hodges et al. (p. 626; see the Perspective by Otterstrom and van Oijen) use single-molecule techniques to analyze how a yeast RNA polymerase II ternary elongation complex copes when it encounters a single nucleosome directly in its path. The polymerase does not actively peel the DNA from the nucleosome's surface but, instead, waits patiently until the DNA fluctuates off the nucleosome and then advances, increment by increment, until the nucleosome is destabilized. Under certain conditions the destabilized nucleosome, rather than being lost entirely from the DNA, can be passed back to the DNA behind the polymerase. RNA polymerase acts as a molecular ratchet to force its way through nucleosome-infested DNA. RNA polymerase II (Pol II) must overcome the barriers imposed by nucleosomes during transcription elongation. We have developed an optical tweezers assay to follow individual Pol II complexes as they transcribe nucleosomal DNA. Our results indicate that the nucleosome behaves as a fluctuating barrier that locally increases pause density, slows pause recovery, and reduces the apparent pause-free velocity of Pol II. The polymerase, rather than actively separating DNA from histones, functions instead as a ratchet that rectifies nucleosomal fluctuations. We also obtained direct evidence that transcription through a nucleosome involves transfer of the core histones behind the transcribing polymerase via a transient DNA loop. The interplay between polymerase dynamics and nucleosome fluctuations provides a physical basis for the regulation of eukaryotic transcription.
Universal Programmable Quantum Circuit Schemes to Emulate an Operator

Unlike fixed designs, programmable circuit designs support an infinite number of operators. The functionality of a programmable circuit can be altered by simply changing the angle values of the rotation gates in the circuit. Here, we present a new quantum circuit design technique resulting in two general programmable circuit schemes. The circuit schemes can be used to simulate any given operator by setting the angle values in the circuit. This provides a fixed circuit design whose angles are determined from the elements of the given matrix-which can be non-unitary-in an efficient way. We also give both the classical and quantum complexity analysis for these circuits and show that the circuits require a few classical computations. For the electronic structure simulation on a quantum computer, one has to perform the following steps: prepare the initial wave function of the system; present the evolution operator U = e(-iHt) for a given atomic and molecular Hamiltonian H in terms of quantum gates array and apply the phase estimation algorithm to find the energy eigenvalues. Thus, in the circuit model of quantum computing for quantum chemistry, a crucial step is presenting the evolution operator for the atomic and molecular Hamiltonians in terms of quantum gate arrays. Since the presented circuit designs are independent from the matrix decomposition techniques and the global optimization processes used to find quantum circuits for a given operator, high accuracy simulations can be done for the unitary propagators of molecular Hamiltonians on quantum computers. As an example, we show how to build the circuit design for the hydrogen molecule.
Methodological convergence of program evaluation designs.

BACKGROUND Nowadays, the confronting dichotomous view between experimental/quasi-experimental and non-experimental/ethnographic studies still exists but, despite the extensive use of non-experimental/ethnographic studies, the most systematic work on methodological quality has been developed based on experimental and quasi-experimental studies. This hinders evaluators and planners' practice of empirical program evaluation, a sphere in which the distinction between types of study is changing continually and is less clear. METHOD Based on the classical validity framework of experimental/quasi-experimental studies, we carry out a review of the literature in order to analyze the convergence of design elements in methodological quality in primary studies in systematic reviews and ethnographic research. RESULTS We specify the relevant design elements that should be taken into account in order to improve validity and generalization in program evaluation practice in different methodologies from a practical methodological and complementary view. CONCLUSIONS We recommend ways to improve design elements so as to enhance validity and generalization in program evaluation practice.
Constraint Reasoning in FocalTest

Property-based testing implies selecting test data satisfying coverage criteria on user-specified properties. However, current automatic test data generation techniques adopt direct generate-and-test approaches for this task. In FocalTest, a testing tool designed to generate test data for programs and properties written in the functionnal language Focal, test data are generated at random and rejected when they do not satisfy selected coverage criteria. In this paper, we improve FocalTest with a test-and-generate approach, through the usage of constraint reasoning. A particular difficulty is the generation of test data satisfying MC/DC on the precondition of a property, when it contains function calls with pattern matching and higher-order functions. Our experimental results show that a non-naive implementation of constraint reasoning on these constructions outperform traditional generation techniques when used to find test data for testing properties.
D-VisionDraughts: a draughts player neural network that learns by reinforcement in a high performance environment

This paper describes D-VisionDraughts, a distributed player agent for draughts which is based on Neural Networks trained by Temporal Differences. DVisionDraughs is trained in a high performance environment and achieves a high level of play without expert game analysis and with minimum human intervention. D-VisionDraughts corresponds to a distributed version of the efficient agent player VisionDraughts. In this way, the main contributions of this paper consist on substituting the distributed Young Brothers Wait Concept algorithm (YBWC) for the serial alpha-beta search algorithm used in VisionDraughts and on measuring the impact of a high performance environment into the non-supervised learning abilities of the player. Evaluative tests proved that even a modest distributed version counting just on ten processors is able to reduce from about 83% the search runtime and to increase from 15% its capacity of winning.
Early Neurodevelopment, adult human cognition and depressive psychopathology: analysis of neuroimaging brain correlates and epigenetic mediators

In the behavioral sciences, the concept of phenotypic plasticity can be roughly categorized into two classes: developmental and activational plasticity. Developmental plasticity denotes the capacity of an individual carrying a specific genetic background to adopt different developmental trajectories under distinct settings. Complementarily, activational plasticity refers to the differential activation of adaptation mechanisms: an individual with high activational plasticity would be able to detect a wide range of environments, and to respond to it using a psychobiological phenotype from a relatively large catalogue. In this context, it is feasible postulating that several etiopathogenic mechanisms of depression-related phenotypes can be clarified by expanding on processes of biobehavioral plasticity in response to the experience. This expansion can be elaborated on the basis of both neurodevelopmental phenomena (developmental plasticity) and novel biological mechanisms detectable through neuroimaging and epigenetics approaches (activational plasticity). The present work expands on two specific hypotheses. First, depression-related psychopathological phenotypes are induced by factors altering the early neurodevelopment, and these long-lasting changes can be assessed in adulthood (depression and developmental plasticity). Secondly, the clinical manifestation of depression-related psychopathological phenotypes can be understood as activational plasticity deficits; these deficits can be assessed as neurobiological disease traits using novel epigenetic and neuroimaging techniques (depression and activational plasticity). The results of this work provide support to the neuroplasticity hypothesis of depression, from both developmental and activational perspectives. Developmentally, they suggest putative etiopathogenic pathways leading from an altered early neurodevelopment to an increased risk for depression-related phenotypes. By exploring and combining genetic, environmental and psychopathologic concepts, the feasibility of these results has been explained by combining the popular genetic pleiotropy hypothesis in psychiatry with a notion of disease-specificity liability driven by the environment. With regards to activational plasticity, this work has proposed novel genetic and epigenetic signatures potentially underlying the clinical manifestation of neuropsychiatric and neurocognitive features of depression (i.e., the genetics of DNMT3B and the epigenetics of DEPDC7); additionally, it has proposed new putative neurobiological mechanisms to explain depressive traits (i.e., a combination of differential and variable methylation, a genetically-mediated hippocampal communication deficit, and a new amygdalar synchrony failure driven by the genes).
Multi-Armed Bandit with Budget Constraint and Variable Costs

We study the multi-armed bandit problems with budget constraint and variable costs (MAB-BV). In this setting, pulling an arm will receive a random reward together with a random cost, and the objective of an algorithm is to pull a sequence of arms in order to maximize the expected total reward with the costs of pulling those arms complying with a budget constraint. This new setting models many Internet applications (e.g., ad exchange, sponsored search, and cloud computing) in a more accurate manner than previous settings where the pulling of arms is either costless or with a fixed cost. We propose two UCB based algorithms for the new setting. The first algorithm needs prior knowledge about the lower bound of the expected costs when computing the exploration term. The second algorithm eliminates this need by estimating the minimal expected costs from empirical observations, and therefore can be applied to more real-world applications where prior knowledge is not available. We prove that both algorithms have nice learning abilities, with regret bounds of O(ln B). Furthermore, we show that when applying our proposed algorithms to a previous setting with fixed costs (which can be regarded as our special case), one can improve the previously obtained regret bound. Our simulation results on real-time bidding in ad exchange verify the effectiveness of the algorithms and are consistent with our theoretical analysis.
Tile size selection using cache organization and data layout

When dense matrix computations are too large to fit in cache, previous research proposes tiling to reduce or eliminate capacity misses. This paper presents a new algorithm for choosing problem-size dependent tile sizes based on the cache size and cache line size for a direct-mapped cache. The algorithm eliminates both capacity and self-interference misses and reduces cross-interference misses. We measured simulated miss rates and execution times for our algorithm and two others on a variety of problem sizes and cache organizations. At higher set associativity, our algorithm does not always achieve the best performance. However on direct-mapped caches, our algorithm improves simulated miss rates and measured execution times when compared with previous work.
Do Birds of a Feather Flock Together? Speculator Herding in the World Oil Market

This paper looks at speculative behavior in the international oil market. Much of the blame for oil-market turbulence has been placed on speculators, particularly hedge funds. Speculative capital has been characterized as “hot money,” with capital flows driven by “herding,” “flocking,” and “contagion. ” Policies to deal with volatility by weakening, or even disabling speculation, have been based largely on anecdote, convenience (speculators have long served as scapegoats for various problems), and ideology, rather than careful analysis. Part of the problem arises from the secrecy with which speculators operate. Because speculative trading cannot easily be observed, it is difficult to assess speculators’ contribution, if any, to volatility. The paper utilizes a large, detailed database on individual trader positions in crude-oil and heating-oil futures markets. The paper is exploratory, with focus on measuring and assessing the tendency of speculators to herd (trade in the same direction as a group) and flock (trade in the same direction by subgroups of speculators).
Multi-Label Classification with Feature-Aware Non-Linear Label Space Transformation

Multi-label classification with many classes has recently drawn a lot of attention. Existing methods address this problem by performing linear label space transformation to reduce the dimension of label space, and then conducting independent regression for each reduced label dimension. These methods however do not capture nonlinear correlations of the multiple labels and may lead to significant information loss in the process of label space reduction. In this paper, we first propose to exploit kernel canonical correlation analysis (KCCA) to capture nonlinear label correlation information and perform nonlinear label space reduction. Then we develop a novel label space reduction method that explicitly combines linear and nonlinear label space transformations based on CCA and KCCA respectively to address multi-label classification with many classes. The proposed method is a feature-aware label transformation method that promotes the label predictability in the transformed label space from the input features. We conduct experiments on a number of multi-label classification datasets. The proposed approach demonstrates good performance, comparing to a number of state-of-the-art label dimension reduction methods.
Influence of Service Quality, Corporate Image and Perceived Value on Customer Behavioral Responses: CFA and Measurement Model

The paper aims at validating the instrument of the study by conducting Confirmatory Factor Analysis (CFA) and the measurement model and also the overall goodness of model fit indices. Similarly, the measurement model of both the combined exogenous and endogenous variables was performed in order to assess the psychometric properties of the measures in the study. Structural Equation Modeling with AMOS software was employed in the analysis. It was found that the measurement model fitted the data after checking modification indices and deleting items that have weak loadings and/or high correlation errors. It was therefore, concluded that the model fit the empirical data and is set for conducting construct validity and subsequently structural model.
Simulation of Specular Surface Imaging Based on Computer Graphics: Application on a Vision Inspection System

This work aims at detecting surface defects on reflecting industrial parts. A machine vision system, performing the detection of geometric aspect surface defects, is completely described. The revealing of defects is realized by a particular lighting device. It has been carefully designed to ensure the imaging of defects. The lighting system simplifies a lot the image processing for defect segmentation and so a real-time inspection of reflective products is possible. To bring help in the conception of imaging conditions, a complete simulation is proposed. The simulation, based on computer graphics, enables the rendering of realistic images. Simulation provides here a very efficient way to perform tests compared to the numerous attempts of manual experiments.