Miracle-dz/UniFAR-FTUs huggingface dataset:
FF Domains of CA150 Bind Transcription and Splicing Factors through Multiple Weak Interactions

ABSTRACT The human transcription factor CA150 modulates human immunodeficiency virus type 1 gene transcription and contains numerous signaling elements, including six FF domains. Repeated FF domains are present in several transcription and splicing factors and can recognize phosphoserine motifs in the C-terminal domain (CTD) of RNA polymerase II (RNAPII). Using mass spectrometry, we identify a number of nuclear binding partners for the CA150 FF domains and demonstrate a direct interaction between CA150 and Tat-SF1, a protein involved in the coupling of splicing and transcription. CA150 FF domains recognize multiple sites within the Tat-SF1 protein conforming to the consensus motif (D/E)2/5-F/W/Y-(D/E)2/5. Individual FF domains are capable of interacting with Tat-SF1 peptide ligands in an equivalent and noncooperative manner, with affinities ranging from 150 to 500 μM. Repeated FF domains therefore appear to bind their targets through multiple weak interactions with motifs comprised of negatively charged residues flanking aromatic amino acids. The RNAPII CTD represents a consensus FF domain-binding site, contingent on generation of the requisite negative charges by phosphorylation of serines 2 and 5. We propose that CA150, through the dual recognition of acidic motifs in proteins such as Tat-SF1 and the phosphorylated CTD, could mediate the recruitment of transcription and splicing factors to actively transcribing RNAPII.
Phantom digit somatotopy: a functional magnetic resonance imaging study in forearm amputees

Forearm amputees often experience non‐painful sensations in their phantom when the amputation stump is touched. Cutaneous stimulation of specific stump areas may be perceived as stimulation of specific phantom fingers (stump hand map). The neuronal basis of referred phantom limb sensations is unknown. We used functional magnetic resonance imaging to demonstrate a somatotopic map of the phantom fingers in the hand region of the primary somatosensory cortex after tactile stump stimulation. The location and extent of phantom finger activation in the primary somatosensory cortex corresponded well to the location of normal fingers in a reference population. Stimulation of the stump hand map resulted in an increased bilateral activation of the primary somatosensory cortex compared with stimulation of forearm regions outside the stump hand map. Increased activation was also seen in contralateral posterior parietal cortex and premotor cortex. Ipsilateral primary somatosensory cortex activation might represent a compensatory mechanism and activation of the non‐primary fronto‐parietal areas might correspond to awareness of the phantom limb, which is enhanced when experiencing the referred sensations. It is concluded that phantom sensation elicited by stimulation of stump hand map areas is associated with activation of finger‐specific somatotopical representations in the primary somatosensory cortex. This suggests that the primary somatosensory cortex could be a neural substrate of non‐painful phantom sensations. The stump hand map phenomenon might be useful in the development of prosthetic hand devices.
Modeling of Dry Self-Assembly Process for Mass Rapid Assembling Microchips on Silicon Substrate

Dry self-assembly process has been proposed and demonstrated experimentally to be a potentially effective way for vast microchips assembly on a silicon substrate. In this method, each microchip is engraved on its bottom with a pattern of a higher rod peg and a lower uniquely orienting feature. On the silicon substrate, a matrix of counterpart pattern is grooved to capture the pattern features on microchips when the silicon substrate is subjected an orbital motion. In this paper, we proposed a model for the dry self-assembly process and theoretically investigated the effect of a few important parameters on the yield rate of production. Firstly, we derived out the motion of microchips before and after being trapped by the patterned features on the silicon substrate. When the silicon substrate is subjected an orbital motion, a microchip on the substrate is found to be maintaining a cycle motion with the same radius as the substrate but its rotational speed depends on the coefficient of friction between the chip and the substrate. After the rod peg of a chip is trapped on the substrate, the chip rotates around the hole on the substrate as well as rotates itself. The relative rotational speed is same as the substrate. But the self-rotational speed is upper bounded by the rotational speed of the substrate times the ratio of radius between the hole and the rod and varies with the coefficient of friction. Secondly, we numerically studied the effect of a few parameters on the yield rate. The result shows that yield rate increases with the radius of orbital motion of the substrate. Before the chip is trapped by a hole, yield rate depends on the region of the ratio between the rotational speed and a critical speed which is defined by the coefficient of friction and the radius of the orbital motion. In some regions, the matching rates are very high, while in other regions the matching rates are low. It indicates that increasing the rotational speed of motor is not a good way to improve the yield rate. After the chip is trapped by a hole, the yield rate depends on the region of the ratio of radius between the hole and the rod peg. Our model predicted the experimental observation very well. Our model helps to archive a rapid and reliable dry self-assembly process with high yield rate.
Manifold-Regularized Selectable Factor Extraction for Semi-supervised Image Classification

In many vision analytics-based applications such as image classification, we confront explosive growth of high-dimensional data. Thus, many feature selection and extraction methods have been proposed to reduce the computational cost and avoid over-fitting. Recently, a novel selectable factor extraction (SFE) framework is proposed to simultaneously perform feature selection and extraction, and is theoretically and practically proved to be effective in handling high-dimensional data. The algorithm is also quite efficient and easy to implement. Although it is advantageous in several aspects, SFE is only designed for either supervised or unsupervised learning, and is not suitable when there are limited labeled samples and a large number of unlabeled samples, since the data distribution knowledge is likely to be poorly exploited. To tackle this problem, we propose a novel manifold regularized SFE (MRSFE) framework for semi-supervised image classification. In MRSFE, the local structures of the whole dataset are preserved, and the data distribution is well exploited. By integrating the label information, low rank property of the features and data distribution knowledge, the proposed MRSFE could select and extract reliable discriminative features when the labeled samples are scarce. An efficient and easy-to-implement algorithm is designed to find the solutions. Extensive experimental results on a real-world image dataset demonstrate the superiority of our method.
Tango Immigrants in New York City

The recent global renaissance of tango dancing has been accompanied by the emergence of a thriving tango economy in New York City (NYC) that has encouraged the arrival of Argentine tango dancers and amateurs artists (tango immigrants) in recent years. This article builds on social capital theory to examine the importance of the Manhattan tango world as a reservoir for social resources (e.g., health information, contact for jobs, referrals) to satisfy their members’ social and health needs. Tango immigrants seek informal access to health care through the assistance of health practitioners belonging to their tango networks (tango brokers), a relevant issue given the fact that many artists are uninsured and depend on their physical labor to perform. This article’s ultimate goal is aimed at providing a theoretical contribution to our understanding of contemporary entertainment forms as ethnic social niches for immigrants’ informal access to valued resources via interpersonal relationships.
p38 MAP kinase negatively regulates endothelial cell survival, proliferation, and differentiation in FGF-2–stimulated angiogenesis

The p38 mitogen–activated protein kinase (p38) is activated in response to environmental stress and inflammatory cytokines. Although several growth factors, including fibroblast growth factor (FGF)-2, mediate activation of p38, the consequences for growth factor–dependent cellular functions have not been well defined. We investigated the role of p38 activation in FGF-2–induced angiogenesis. In collagen gel cultures, bovine capillary endothelial cells formed tubular growth-arrested structures in response to FGF-2. In these collagen gel cultures, p38 activation was induced more potently by FGF-2 treatment compared with that in proliferating cultures. Treatment with the p38 inhibitor SB202190 enhanced FGF-2–induced tubular morphogenesis by decreasing apoptosis, increasing DNA synthesis and cell proliferation, and enhancing the kinetics of cell differentiation including increased expression of the Notch ligand Jagged1. Overexpression of dominant negative mutants of the p38-activating kinases MKK3 and MKK6 also supported FGF-2–induced tubular morphogenesis. Sustained activation of p38 by FGF-2 was identified in vascular endothelial cells in vivo in the chick chorioallantoic membrane (CAM). SB202190 treatment enhanced FGF-2–induced neovascularization in the CAM, but the vessels displayed abnormal features indicative of hyperplasia of endothelial cells. These results implicate p38 in organization of new vessels and suggest that p38 is an essential regulator of FGF-2–driven angiogenesis.
Identification of ayurvedic medicinal plants by image processing of leaf samples

Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front and back side of a green leaf along with morphological features to arrive at a unique optimum combination of features that maximizes the identification rate. A database of medicinal plant leaves is created from scanned images of front and back side of leaves of commonly used ayurvedic medicinal plants. The leaves are classified based on the unique feature combination. Identification rates up to 99% have been obtained when tested over a wide spectrum of classifiers. The above work has been extended to include identification by dry leaves and a combination of feature vectors is obtained, using which, identification rates exceeding 94% have been achieved.
Aptamers and Their Biological Applications

Recently, aptamers have attracted the attention of many scientists, because they not only have all of the advantages of antibodies, but also have unique merits, such as thermal stability, low cost, and unlimited applications. In this review, we present the reasons why aptamers are known as alternatives to antibodies. Furthermore, several types of in vitro selection processes, including nitrocellulose membrane filtration, affinity chromatography, magnetic bead, and capillary electrophoresis-based selection methods, are explained in detail. We also introduce various applications of aptamers for the diagnosis of diseases and detection of small molecules. Numerous analytical techniques, such as electrochemical, colorimetric, optical, and mass-sensitive methods, can be utilized to detect targets, due to convenient modifications and the stability of aptamers. Finally, several medical and analytical applications of aptamers are presented. In summary, aptamers are promising materials for diverse areas, not just as alternatives to antibodies, but as the core components of medical and analytical equipment.
A genetic algorithm for discovering process trees

Existing process discovery approaches have problems dealing with competing quality dimensions (fitness, simplicity, generalization, and precision) and may produce anomalous process models (e.g., deadlocking models). In this paper we propose a new genetic process mining algorithm that discovers process models from event logs. The tree representation ensures the soundness of the model. Moreover, as experiments show, it is possible to balance the different quality dimensions. Our genetic process mining algorithm is the first algorithm where the search process can be guided by preferences of the user while ensuring correctness.
Evaluation in Europe 2000

The fourth conference of the European Evaluation Society, which took place in Lausanne, was on the topic ‘Taking Evaluation to the People’. One of the reasons to hold the conference in Switzerland is that, perhaps more than any other European country, decentralized Switzerland highlights the importance of citizens and civil society in so many ways. Studies by Swiss researchers have shown that the efficiency and effectiveness of the public sector are strongly influenced by the importance that society attaches to the role of civil society in decision making and implementation, and to the institutional arrangements that shape this role (Frey, 1990; Knoepfel, 2000; Pommerehne, 1991; World Bank, 1997). Evaluation in Europe is growing and becoming more institutionalized.1 The formation of evaluation societies illustrates this and it is also documented in a comparative study of the development of evaluation in 30 countries (Rist et al., 2001). Similar evidence can be deduced from a survey that I and colleagues undertook a few years ago (Leeuw et al., 1999). Against this background, my focus is on four challenges that face both evaluation in practice and evaluation research in Europe now and in the coming years.
Adaptive Critic Neuro-fuzzy Control of Two-wheel Vehicle

This paper presents an adaptive critic neuro-fuzzy control design, which enables learning from scratch to achieve the control objective. The learning algorithm is derived from the Dual Heuristic Programming (DHP) method to approximate optimal control. The learning structure contains the action, critic and verification neuro-fuzzy networks each corresponding to the first-order Sugeno fuzzy model. The critic and verification networks together forms a reinforcement learning scheme to estimate the future derivative Bellman function. The transparency of the network sensitivity function makes the update rule of the action network simple. Simulation results show that the adaptive critic neuro-fuzzy control system enables the motorized vehicle with simply two coaxial wheels learning from scratch to achieve velocity control while keeps the intermediate body in balance.
Nation-building and common values in Russia

Chapter 1 Nation-Building in Russia: A Value-Oriented Strategy Chapter 2 Value Consensus and Social Cohesion in Russia Chapter 3 The Peculiarities of Nation-Building in the Republic of Komi Chapter 4 Bashkortostan: Preconditions and Prospects of Civic Integration in a Multiethnic Region Chapter 5 The Republic of Dagestan: Nation-Building inside Russia's Womb Chapter 6 The Lonely Heart of the World: Nation-Building and Common Values in Novosibirsk Oblast Chapter 7 Nation-Building and Common Values in St. Petersburg Chapter 8 Moscow's Values: The Masses and the Elite Chapter 9 Russian Regionalism Redefined? Nation-Building, Values and Federal Discourse Chapter 10 Nation-Building and Values in Russian Textbooks Chapter 11 Orthodoxy and Islam in Post-Soviet Russia: Opposing Confessional Cultures or Unifying Force? Chapter 12 Epilogue: Values and State Ideology in Post-Communist Russia Part 13 Appendix: Technical Specifications and Sample Design
A Probabilistic Chunker

This paper proposes a probabilistic partial parser, which we call chunker. The chunker partitions the input sentence into segments. This idea is motivated by the fact that when we read a sentence, we read it chunk by chunk. We train the chunker from Susanne Corpus, which is a modified but shrunk version of Brown Corpus, underlying bi-gram language model. The experiment is evaluated by outside test and inside test. The preliminary results show the chunker has more than 98% chunk correct rate and 94% sentence correct rate in outside test, and 99% chunk correct rate and 97% sentence correct rate in inside test. The simple but effective chunker design has shown to be promising and can be extended to complete parsing and many applications.
Interpersonal Rejection Sensitivity and Regulatory Focus Theory to Explain College Students' Class Engagement

The current study investigated the relationship between undergraduate students' sensitivity to rejection and their level of participation in positive classroom engagement. One hundred and thirty-five undergraduate students from an upper-Midwest university volunteered to participate in a survey as part of their Psychology 101 course. As hypothesized, there was a negative correlation between students' sensitivity to rejection and their level of participation in positive class behaviors. We also found that this relationship was moderated by the regulatory focus, “prevention pride”. The greater a student's prevention pride was, the more likely the student was to have an average amount of participation in positive class behaviors, regardless of their sensitivity to rejection. These findings suggest that students who do not become engaged in positive classroom behaviors may be afraid to do so for fear of rejection by peers and/or instructors, and that this relationship is moderated to a large degree by the students' prevention pride.
The nature of publishing and assessment in Geography and Environmental Studies: evidence from the Research Assessment Exercise 2008

We present a summary of the kinds of outputs submitted to the Geography and Environmental Studies sub-panel (H-32) for the 2008 Research Assessment Exercise (RAE), and examine the relationships between the peer assessment of research quality that the RAE process has typified, and alternative modes of assessment based on bibliometrics. This comparison is effected using (in aggregate form) some of the results from the RAE, together with citation data gathered after completion of the RAE assessment, specifically for the purpose of this paper. We conclude that, if it continues to be necessary and desirable to assess, in some measure and however imprecisely, research quality, then peer assessment cannot be replaced by bibliometrics. Bibliometrics permit measurement of something that may be linked to quality but is essentially a different phenomenon – a measure of ‘impact’, for example.
Molecular Basis for the Antiproliferative Effect of Agmatine in Tumor Cells of Colonic, Hepatic, and Neuronal Origin

The aim of the present study was to challenge potential mechanisms of action underlying the inhibition of tumor cell proliferation by agmatine. Agmatine inhibited proliferation of the human hepatoma cells HepG2, the human adenocarcinoma cells HT29, the rat hepatoma cells McRH7777, and the rat pheochromocytoma cells PC-12. Inhibition of proliferation of HepG2 cells was associated with an abolition of expression of ornithine decarboxylase (ODC) protein and a doubling of mRNA content encoding ODC. In HepG2 cells, silencing of ODC-antizyme-1, but not of antizyme inhibitor, by RNA interference resulted in an increase of agmatine's antiproliferative effect. Thus, the distinct decrease in intracellular polyamine content by agmatine was due to a reduced translation of the synthesizing protein ODC but was not essentially mediated by induction of ODC-antizyme or blockade of antizyme inhibitor. In interaction experiments 1 mM l-arginine, 1 mM d-arginine, 1 mM citrulline, 100 μM Nω-nitro-l-arginine methyl ester, 1 and 10 μM sodium nitroprusside, and 1 μM N1-guanyl-1,7-diaminoheptane failed to alter agmatine's antiproliferative effect. Hence, the antiproliferative effect of agmatine in HT29 and HepG2 cells is due to an interaction with neither the NO synthases, the hypusination of eIF5A, nor an agmatine-induced reduction in availability of intracellular l-arginine. l-Arginine and citrulline, but not d-arginine, inhibited tumor cell proliferation by themselves. Their inhibitory effect was abolished after silencing of arginine decarboxylase (ADC) expression by RNA interference indicating the conversion to agmatine by ADC. Finally, in the four cell lines under study, agmatine-induced inhibition of cell proliferation was paralleled by an increase in intracellular caspase-3 activity, indicating a promotion of apoptosis.
MicroRNA-directed program of cytotoxic CD8+ T-cell differentiation

Significance Development of cytotoxic T lymphocytes (CTLs) from activated CD8+ T cells is a key step of the antiviral immune response and is marked by the up-regulation of lytic molecules (perforin, granzymes). How this process is regulated at the posttranscriptional level is still largely unknown. Here we report that Dicer and microRNAs (miRNAs) restrict the expression of lytic molecules in mouse and human CTLs, and describe a unique signaling network that controls the expression of perforin, eomesodermin, and the IL-2Rα chain (CD25) downstream of IL-2 and inflammatory signals through miR-139 and miR-150 in differentiating CTLs. Acquisition of effector properties is a key step in the generation of cytotoxic T lymphocytes (CTLs). Here we show that inflammatory signals regulate Dicer expression in CTLs, and that deletion or depletion of Dicer in mouse or human activated CD8+ T cells causes up-regulation of perforin, granzymes, and effector cytokines. Genome-wide analysis of microRNA (miR, miRNA) changes induced by exposure of differentiating CTLs to IL-2 and inflammatory signals identifies miR-139 and miR-150 as components of an miRNA network that controls perforin, eomesodermin, and IL-2Rα expression in differentiating CTLs and whose activity is modulated by IL-2, inflammation, and antigenic stimulation. Overall, our data show that strong IL-2R and inflammatory signals act through Dicer and miRNAs to control the cytolytic program and other aspects of effector CTL differentiation.
Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information

We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.