Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Regulation of Corticotropin-Releasing Hormone and Vasopressin Gene Transcription in the Hypothalamus by Norepinephrine

All stress-related inputs are conveyed to the hypothalamus via several brain areas and integrated in the parvocellular division of the paraventricular nucleus (PVN) where corticotropin-releasing hormone (CRH) is synthesized. Arginine vasopressin (AVP) is present in both magnocellular and parvocellular divisions of the PVN, and the latter population of AVP is colocalized with CRH. CRH and AVP are co-secreted in the face of certain stressful stimuli, and synthesis of both peptides is suppressed by glucocorticoid. CRH and AVP stimulate corticotropin (ACTH) secretion synergistically, but the physiological relevance of the dual corticotroph regulation is not understood. Norepinephrine (NE) is a well known neurotransmitter that regulates CRH neurons in the PVN. We explored the mode of action of NE on CRH and AVP gene transcription in the PVN to examine the effect of the neurotransmitter on multiple genes that are responsible for a common physiological function. After NE injection into the PVN of conscious rats, CRH heteronuclear (hn) RNA increased rapidly and markedly in the parvocellular division of the PVN. AVP hnRNA did not change significantly in either the parvocellular or magnocellular division of the PVN after NE injection. The present results show that the transcription of CRH and AVP genes is differentially regulated by NE, indicating the complexity of neurotransmitter regulation of multiple releasing hormone genes in a discrete hypothalamic neuronal population.
SC&#xb3;: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles

In order to perform successful diagnosis and treatment of cancer, discovering, and classifying cancer types correctly is essential. One of the challenging properties of class discovery from cancer data sets is that cancer gene expression profiles not only include a large number of genes, but also contains a lot of noisy genes. In order to reduce the effect of noisy genes in cancer gene expression profiles, we propose two new consensus clustering frameworks, named as triple spectral clustering-based consensus clustering (SC3) and double spectral clustering-based consensus clustering (SC2 Ncut) in this paper, for cancer discovery from gene expression profiles. SC3 integrates the spectral clustering (SC) algorithm multiple times into the ensemble framework to process gene expression profiles. Specifically, spectral clustering is applied to perform clustering on the gene dimension and the cancer sample dimension, and also used as the consensus function to partition the consensus matrix constructed from multiple clustering solutions. Compared with SC3, SC2 Ncut adopts the normalized cut algorithm, instead of spectral clustering, as the consensus function. Experiments on both synthetic data sets and real cancer gene expression profiles illustrate that the proposed approaches not only achieve good performance on gene expression profiles, but also outperforms most of the existing approaches in the process of class discovery from these profiles.
Analysis of dynamic spectra in ferret primary auditory cortex. II. Prediction of unit responses to arbitrary dynamic spectra.

1. Responses of single units and multiunit clusters were recorded in the ferret primary auditory cortex (AI) with the use of broadband complex dynamic spectra. Previous work has demonstrated that simpler spectra consisting of single moving ripples (i.e., sinusoidally modulated spectral profiles that travel at a constant velocity along the logarithmic frequency axis) could be used effectively to characterize the response fields and transfer functions of AI cells. 2. A complex dynamic spectral profile can be thought of as being the sum of moving ripple spectra. Such a decomposition can be computed from a two-dimensional spectrotemporal Fourier transform of the dynamic spectral profile with moving ripples as the basis function. 3. Therefore, if AI units were essentially linear, satisfying the superposition principle, then their responses to arbitrary dynamic spectra could be predicted from the responses to single moving ripples, i.e., from the units' response fields and transfer functions (spectral and temporal impulse response functions, respectively). 4. This conjecture was tested and confirmed with data from 293 combinations of moving ripples, involving complex spectra composed of up to 15 moving ripples of different ripple frequencies and velocities. For each case, response predictions based on the unit transfer functions were compared with measured responses. The correlation between predicted and measured responses was found to be consistently high (84% with rho > 0.6). 5. The distribution of response parameters suggests that AI cells may encode the profile of a dynamic spectrum by performing a multiscale spectrotemporal decomposition of the dynamic spectral profile in a largely linear manner.
Hematocrit Predicts Long-Term Mortality in a Nonlinear and Sex-Specific Manner in Hypertensive Adults

Hematocrit has been inconsistently reported to be a risk marker of cardiovascular morbidity and mortality. The Glasgow Blood Pressure Clinic Study cohort included 10951 hypertensive patients, who had hematocrit measured at their initial clinic visit and followed for ⩽35 years. Cox proportional hazards models were used to estimate hazard ratios for all-cause, cardiovascular, ischemic heart disease, stroke, and noncardiovascular mortality. There were 3484 deaths over a follow-up period of 173245 person-years. Hematocrit was higher in men (median, 0.44; interquartile range, 0.42–0.47) than in women (median, 0.41; interquartile range, 0.38–0.43). The lowest risk for all-cause mortality was seen in quartile 2 for men (range, 0.421–0.440) and women (range, 0.381–0.400). Compared with quartile 2, the adjusted hazard ratios for quartiles 1, 3, and 4 were, respectively, 1.11 (range, 0.97–1.28), 1.19 (range, 1.04–1.37), and 1.22 (range, 1.06–1.39) in men and 1.17 (range, 1.01–1.36), 0.97 (range, 0.83–1.13), and 1.19 (range, 1.04–1.37) in women. Men showed a J-shaped pattern for cardiovascular mortality and a linear pattern for noncardiovascular mortality in cause-specific analysis, whereas in women a U-shaped pattern was observed for noncardiovascular mortality only. Higher baseline hematocrit was associated with higher on-treatment blood pressure during follow-up. Baseline hematocrit did not affect the time to reach target blood pressure. The increased risk of death attributed to higher hematocrit was seen in men and women irrespective of their achievement of target blood pressure, indicating that the risk is independent of the effect of hematocrit on blood pressure. Hypertensive patients with hematocrit levels outside of the sex-specific reference ranges identified in this study should be targeted for more aggressive blood pressure and cardiovascular risk reduction treatment.
Automated recognition of facial expressions authenticity

Recognition of facial expressions authenticity is quite troublesome for humans. Therefore, it is an interesting topic for the computer vision community, as the developed algorithms for facial expressions authenticity estimation may be used as indicators of deception. This paper discusses the state-of-the art methods developed for smile veracity estimation and proposes a plan of development and validation of a novel approach to automated discrimination between genuine and posed facial expressions. The proposed fully automated technique is based on the extension of the high-dimensional Local Binary Patterns (LBP) to the spatio-temporal domain and combines them with the dynamics of facial landmarks movements. The proposed technique will be validated on several existing smile databases and a novel database created with the use of a high speed camera. Finally, the developed framework will be applied for the detection of deception in real life scenarios.
Diffuse Interface Models on Graphs for Classification of High Dimensional Data

There are currently several communities working on algorithms for classification of high dimensional data. This work develops a class of variational algorithms that combine recent ideas from spectral methods on graphs with nonlinear edge/region detection methods traditionally used in the PDE-based imaging community. The algorithms are based on the Ginzburg--Landau functional which has classical PDE connections to total variation minimization. Convex-splitting algorithms allow us to quickly find minimizers of the proposed model and take advantage of fast spectral solvers of linear graph-theoretic problems. We present diverse computational examples involving both basic clustering and semisupervised learning for different applications. Case studies include feature identification in images, segmentation in social networks, and segmentation of shapes in high dimensional datasets.
MicroRNA analysis: is it ready for prime time?

Since their discovery, microRNAs (miRNAs)20 have shown great promise in a wide array of clinical applications. In some cases, the use of miRNAs as new diagnostic markers might help answer diagnostic dilemmas that gene expression analyses or other types of analyses have not been able to satisfactorily address. In other instances, it is easy to perceive certain miRNAs as targets for novel therapies that downregulate an entire pathway via the targeting of a single miRNA. When and if this concept will come to fruition remain unclear. Scientists agree that this field of biology is exciting, offers much promise, and has numerous advantages, compared with experiences with other biomolecules. In this Q&A, 5 individuals with extensive miRNA experience share their vision for where the miRNA field is heading and whether we, in the clinical laboratory, will experience its implications soon. What are the most important characteristics of miRNAs that increase their potential as novel diagnostic or therapeutic targets? George Calin: One important characteristic of miRNAs that makes them an exciting potential biomarker for diagnosis and/or a target for therapy is the fact that specific miRNAs target multiple components from the same pathway. For example, the miR-15a/16–1 cluster targets genes from the apoptotic pathway: BCL2 21 (B-cell CLL/lymphoma 2) and MCL1 [myeloid cell leukemia sequence 1 ( BCL2 -related)]. Any given miRNA can regulate numerous genes, and each gene can be regulated by different miRNAs. Pierre Cordelier: In cancer research, miRNAs can differentiate normal from cancerous tissues and, more importantly, can discriminate different subtypes of cancer. The high stability of miRNAs in tissues and fluids is another key advantage that increases their potential as diagnostic markers over messenger RNA (mRNA). In addition, they can be quantified in very low amounts of material and in highly degraded samples. This is of prime importance …
Using Wikipedia to Translate OOV Term on MLIR

We deal with Chinese, Japanese and Korean multilingual information retrieval (MLIR) in NTCIR-6, and submit our results on the C-CJK-T and C-CJK-D subtask. In these runs, we adopt Dictionary-Based Approach to translate query terms. In addition to tradition dictionary, we incorporate the Wikipedia as a live dictionary.
Fast and effective kernels for relational learning from texts

In this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering.
A New Thresholding Algorithm for Hierarchical Text Classification

This paper describes a method for the automatic classification of a HTML document into a hierarchy of categories. The method, implemented in the system WebClassIII extends and improves the previous version WebClassII in various directions. First, it presents a modified Naive Bayes algorithm in order to avoid problems related to the different document length. Second, it implements a probabilistic SVM-based classifier. Third, it implements a new thresholding algorithm that operates in a bottom-up fashion and takes advantage of the decision taken at lower levels of the hierarchy. Two experiments have been conducted. The first experiment aims to compare WebClassIII with WebClassII. The second experiment aims to show performances of WebClassIII with a deeper hierarchy.
Time and the event: The semantics of Russian prefixes

In this paper, I draw on data from prefixation in Russian to argue for a basic distinction between event structure and temporal struc- ture. I present a linguistic semantics of verb and argument structure interpretation on the one hand, and a formal semantic implementa- tion of 'telicity' on the other, which makes sense of the generalisations apparently common to both domains. I will claim that the temporal domain embeds the event structure domain, and that the latter con- strains the former. At the same time, the different formal primitives that operate at the levels proposed form the basis for a principled linguistic distinction between the two tiers of composition: the event structure level encodes subevental relations and predicational rela- tions within those subevents; the temporal structure level introduces a t variable explicitly and relates it to the structure built up by the event level. Whether the event structure is homogenous or not will have an impact on whether the temporal variable chosen will be 'def- inite' or 'indefinite. ' This latter claim then forms the basis for a new conception of the difference between perfective and imperfective verb forms in Russian.
Urban-Rural Educational Inequalities and Human Capital Polarization in Romania

Equal access to education is among the basic human rights. However, numerous studies show that educational gaps between various groups in many countries persist (OECD, 2012). Educational inequalities are on the agenda of policy makers in many countries as they affect life chances of individuals and contribute to perpetuation of social disadvantages. In many cases, children coming from disadvantaged communities register lower school outcomes and grow up without acquiring the skills needed on the labour market. The present paper aims to analyse the level and evolution of urban-rural educational inequalities in Romania. We analyse data on educational attainment in urban and rural areas, showing how participation to education is influenced by the area of residence. It is argued that rural-urban educational inequalities determine human capital polarization, producing labour market segmentation in long term. How to cite: Zamfir, A-M. (2017). Urban-Rural Educational Inequalities and Human Capital Polarization in Romania. Revista Romaneasca pentru Educatie Multidimensionala, 9 (3), 156-164. https://doi.org/10.18662/rrem/2017.0903. 10
AN EFFICIENT ALGORITHM TO ACCESS WEB LOG PATTERN USING WAP TREE-MINE

World Wide Web is a huge data repository and is growing with the explosive rate of about 1 million pages a day, web log records each access of the web page and number of entries in the web logs is increasing rapidly. These web logs, when mined properly can provide useful information for decision-making. Sequential pattern mining discovers frequent user access patterns from web logs. Since Apriori-like sequential pattern mining techniques requires expensive multiple scans of database. But, recently a novel data structure, known as Web Access Pattern Tree (or WAP-tree), was developed. This proposed method an efficient WAP-tree mining algorithm, known as DLT-mine (Doubly Linked Tree algorithm). Proposed recursive algorithm uses this doubly Linked tree to efficiently find all access patterns that satisfy user specified criteria. This mining algorithm is faster than the other Apriori-based mining algorithms.
Counter example for Q-bucket-brigade under prediction problem

Aiming to clarify the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of LCS diverges; and (2) methods to avoid such divergence. Based on our previous work that showed equivalence between LCS's reinforcement process and Reinforcement Learning (RL) with Function approximation (FA) method, we present a counter-example for LCS with Q-bucket-brigade based on the 11-state star problem, a counter-example originally proposed to show the divergence of Q-learning with linear FA. Furthermore, the empirical results applying the counter-example to LCS verified the results predicted from the theory: (1) LCS with Q-bucket-brigade diverged under the prediction problem, where the action selection policy was fixed; and (2) such divergence was avoided by using implicit-bucket-brigade or applying residual gradient algorithm to Q-bucket-brigade.
Blending defined on difference, and an implementation using dynamical approach

The human cognitive ability to blend mental objects (blending) has been a subject of interest in cognitive linguistics. Some researchers are motivated to give formal descriptions to concepts developed in cognitive linguistics. We have formulated conditions of blending that provide testable conditions to classify whether the final product of blending could be uniquely produced as the result of processing the whole of the word combinations. Our formulation allows us to theoretically test the conditions of blending up to an indefinitely large series of word combinations. Such a formal description of the conditions of blending has not appeared in previous studies. We demonstrated our formulation by implementing a neural network model, as a dynamical system. As the results of computer simulations showed, we found that the model exhibits “context-dependency” and “ambiguity” (probabilistic behavior) in the process of blending. This can be seen as a model of communication, more generally, demonstrating a process that involves a series of making of meanings.
Broadcasting on Meshes with Worm-hole Routing

We address the problem of broadcasting on mesh architectures with arbitrary (non-power-two) dimensions. It is assumed that such mesh architectures employ cut-through or worm-hole routing. The primary focus is on avoiding network connicts in the various proposed algorithms. We give algorithms for performing a connict-free minimum-spanning tree broadcast, a pipelined algorithm that is similar to Ho and Johnsson's EDST algorithm for hypercubes, and a novel scatter-collect approach that is a natural choice for communication libraries due to its simplicity. Results obtained on the Intel Paragon system are included.
Discretization of the Multiscale Semiconductor Boltzmann Equation by Diffusive Relaxation Schemes

In this paper we derive diffusive relaxation schemes for the linear semiconductor Boltzmann equation that work in both the kinetic and diffusive regimes. Similar to our earlier approach for multiscale transport equations, we use the even- and odd-parity formulation of the kinetic equation, and then reformulate it into the diffusive relaxation system (DRS). In order to handle the implicit anisotropic collision term efficiently, we utilize a suitable power series expansion based on the Wild sum, which yields a time discretization uniformly stable with any desired order of accuracy, yet is explicitly solvable with the correct drift-diffusion limit. The velocity discretization is done with the Gauss? Hermite quadrature rule equivalent to a moment expansion method. Asymptotic analysis and numerical experiments show that the schemes have the usual advantages of a diffusive relaxation scheme for multiscale transport equations and are asymptotic-preserving.
Formation of the initial C3 convertase of the alternative complement pathway. Acquisition of C3b-like activities by spontaneous hydrolysis of the putative thioester in native C3

Activation of the alternative pathway of complement commences with the formation of an initial fluid-phase C3 convertase. Treatment of C3 with the nucleophilic reagent methylamine has previously been shown to result in the cleavage of an intramolecular thioester bond and to induce C3b-like properties, including the ability to form a fluid-phase C3 convertase. This report examines the hypothesis that spontaneous hydrolysis of the thioester generates a derivative of C3 that is responsible for the formation of the initial C3 convertase of the alternative pathway. The rate of spontaneous decay of C3 hemolytic activity in buffer was found to be between 0.2 and 0.4%/h. In the presence of other alternative pathway proteins, the rate of inactivation was 1%/h. The rate of spontaneous inactivation was greatly accelerated by low concentrations of chaotrophic agents such as KSCN or guanidine. Liberation of a sulfhydryl group, not present in native C3, correlated with loss of hemolytic activity, indicating that exposure to chaotropic agents resulted in thioester hydrolysis. Unlike native C3, C3 bearing a single reactive sulfhydryl group was capable of generating fluid-phase C3 convertase with Factors B, D, and P and was cleaved by Factor I (C3b inactivator) in the presence of Factor H (beta 1H). The fragmentation patterns indicated that the C3a domain was covalently associated with the functionally C3b-like C3. Organomercurial agarose was employed for the rapid removal of sulfhydryl-bearing, hemolytically inactive forms of C3 and C3b from native hemolytically active C3.
Interactive Information Visualization to Explore Temporal Data

Information Visualization is mainly concerned with the representation of non-physical data like medical patient records or business data. Time is a very important data dimension in many domains that is different from others. A number of novel Information Visualization methods have been developed in order to facilitate the exploration of data across temporal contexts, but most of these methods are domain specific and only take application specific time aspects into account. My thesis work aims for providing a general framework for supporting the interactive visual exploration of temporal data over a broad range of domains and applications. The approach I chose for achieving this goal is based on the visualization reference model. This model will be extended by the denotation of temporal attributes via meta data along a versatile time model. Furthermore, interactive overview visualizations of temporal data sets will be provided as starting point for the visual exploration process. The design of user interfaces and visualization methods will be performed using Interaction Design and Usability Engineering methods.
A generic approach to searching for Jacobians

We consider the problem of finding cryptographically suitable Jacobians. By applying a probabilistic generic algorithm to compute the zeta functions of low genus curves drawn from an arbitrary family, we can search for Jacobians containing a large subgroup of prime order. For a suitable distribution of curves, the complexity is subexponential in genus 2, and O(N^{1/12}) in genus 3. We give examples of genus 2 and genus 3 hyperelliptic curves over prime fields with group orders over 180 bits in size, improving previous results. Our approach is particularly effective over low-degree extension fields, where in genus 2 we find Jacobians over F_{p^2) and trace zero varieties over F_{p^3} with near-prime orders up to 372 bits in size. For p = 2^{61}-1, the average time to find a group with 244-bit near-prime order is under an hour on a PC.