Miracle-dz/UniFAR-FTUs huggingface dataset:
MicroRNAs' Involvement in Osteoarthritis and the Prospects for Treatments

Osteoarthritis (OA) is a chronic disease and its etiology is complex. With increasing OA incidence, more and more people are facing heavy financial and social burdens from the disease. Genetics-related aspects of OA pathogenesis are not well understood. Recent reports have examined the molecular mechanisms and genes related to OA. It has been realized that genetic changes in articular cartilage and bone may contribute to OA's development. Osteoclasts, osteoblasts, osteocytes, and chondrocytes in joints must express appropriate genes to achieve tissue homeostasis, and errors in this can cause OA. MicroRNAs (miRNAs) are small noncoding RNAs that have been discovered to be overarching regulators of gene expression. Their ability to repress many target genes and their target-binding specificity indicate a complex network of interactions, which is still being defined. Many studies have focused on the role of miRNAs in bone and cartilage and have identified numbers of miRNAs that play important roles in regulating bone and cartilage homeostasis. Those miRNAs may also be involved in the pathology of OA, which is the focus of this review. Future studies on the role of miRNAs in OA will provide important clues leading to a better understanding of the mechanism(s) of OA and, more particularly, to the development of therapeutic targets for OA.
Hardware implementation of programmable cellular automata encryption algorithm

In this paper, programmable cellular automata (PCAs) are used to design a symmetric key cryptosystem. Based on PCAs state transitions certain fundamental transformations are defined which represents block ciphering functions of the proposed enciphering scheme. In order to verify the proposed encryption algorithm, an experimental hardware platform based on a reconfigurable FPGA of type Spartan 3E XC3S500E was used. The enciphering scheme provides high speed, good security and it is ideally suit for hardware implementation in FPGA devices.
Using Rhodamine 123 Accumulation in CD8+ Cells as a Surrogate Indicator to Study the P-Glycoprotein Modulating Effect of Cepharanthine Hydrochloride In Vivo

The purpose of this study was the use of rhodamine 123 (Rho123) accumulation in peripheral blood CD8+cells as a surrogate indicator to evaluate the modulating effect of P-glycoprotein (P-gp) inhibitors in the multidrug resistance (MDR) tumor-bearing mouse model. Rho123 was administered to mice, and the fluorescence level in CD8+ cells was measured. Cepharanthine hydrochloride (CH) and verapamil (VER), two P-gp inhibitors, were administered to mice 1 hour prior to Rho123 administration in vivo or added to peripheral blood 1 hour prior to Rho123 addition ex vivo. The tumor inhibition effect of 5-fluorouracil/adriamycin/cisplatin (FAP) protocol plus CH was also investigated. A concentration- or dose-response relationship was shown between the concentration and dose of CH and Rho123 accumulation or the antitumor activity. In conclusion, the measurement of Rho123 accumulation in CD8+ cells provides a surrogate assay for the screening of candidate P-gp inhibitors in preclinical trials, and CH is effective in modulating P-gp-mediated MDR in vivo.
Identifying Repeated Patterns in Music Using Sparse Convolutive Non-negative Matrix Factorization

We describe an unsupervised, data-driven, method for auto- matically identifying repeated patterns in music by analyz- ing a feature matrix using a variant of sparse convolutive non-negative matrix factorization. We utilize sparsity con- straints to automatically identify the number of patterns and their lengths, parameters that would normally need to be fixed in advance. The proposed analysis is applied to beat- synchronous chromagrams in order to concurrently extract repeated harmonic motifs and their locations within a song. Finally, we show how this analysis can be used for long- term structure segmentation, resulting in an algorithm that is competitive with other state-of-the-art segmentation algo- rithms based on hidden Markov models and self similarity matrices. been on locating repetitions rather than on extracting of characteristic, repetitive patterns. Previous research on de- tecting motif occurrences across a collection (9) and cover- song retrieval based on short-snippets (3), illustrate the utility of extracting such patterns. In this paper we propose a novel approach for the auto- matic extraction and localization of repeated patterns in mu- sic audio. The approach is based on sparse shift-invariant probabilistic latent component analysis (14) (SI-PLCA), a probabilistic variant of convolutive non-negative matrix factorization (NMF). The algorithm treats a musical record- ing as a concatenation of a small subset of short, repeated patterns, and is able to simultaneously estimate both the patterns and their repetitions throughout the song. The anal- ysis naturally identifies the long-term harmonic structure within a song, while the short-term structure is encoded within the patterns themselves. Furthermore, we show how it is possible to utilize sparse prior distributions to learn the number of patterns and their respective lengths, min- imizing the number of parameters that must be specified exactly in advance. Finally, we explore the application of this approach to long-term segmentation of musical pieces. The remainder of this paper is organized as follows: Section 2 reviews the proposed analysis based on SI-PLCA and describes its relationship to NMF. Sections 3 and 4 describe prior distributions over the SI-PLCA parameters and the expectation maximization algorithm for parameter estimation. Sections 5 and 6 discuss how the proposed analysis can be used for structure segmentation and provide experimental results. Finally, we conclude in Section 7.
i-dentity: innominate movement representation as engaging game element

Movement-based digital games typically make it clear whose movement representation belongs to which player. In contrast, we argue that selectively concealing whose movement controls which representation can facilitate engaging play experiences. We call this "innominate movement representation" and explore this opportunity through our game "i-dentity", where players have to guess who makes everyone's controller light up based on his/her movements. Our work reveals five dimensions for the design of innominate movement representation: concealing the association between movement and representation; number of represented movements; number of players with representations; location of representation in relation to the body and technical attributes of representation. We also present five strategies for how innominate representation can be embedded into a play experience. With our work we hope to expand the range of digital movement games.
Fast CG-Based Methods for Tikhonov-Phillips Regularization

Tikhonov--Phillips regularization is one of the best-known regularization methods for inverse problems. A posteriori criteria for determining the regularization parameter $\alpha$ require solving $$(*) (A^*A+\alpha I) x =A^* y^{\delta}$$ for different values of $\alpha$. We investigate two methods for accelerating the standard cg-algorithm for solving the family of systems (*). The first one utilizes a stopping criterion for the cg-iterations which depends on $\alpha$ and $\delta$. The second method exploits the shifted structure of the linear systems (*), which allows us to solve (*) simultaneously for different values of $\alpha$. We present numerical experiments for three test problems which illustrate the practical efficiency of the new methods. The experiments as well as theoretical considerations show that run times are accelerated by a factor of at least 3.
Pushdown Abstractions of JavaScript

We design a family of program analyses for JavaScript that make no approximation in matching calls with returns, exceptions with handlers, and breaks with labels. We do so by starting from an established reduction semantics for JavaScript and systematically deriving its intensional abstract interpretation. Our first step is to transform the semantics into an equivalent low-level abstract machine: the JavaScript Abstract Machine (JAM). We then give an infinite-state yet decidable pushdown machine whose stack precisely models the structure of the concrete program stack. The precise model of stack structure in turn confers precise control-flow analysis even in the presence of control effects, such as exceptions and finally blocks. We give pushdown generalizations of traditional forms of analysis such as k-CFA, and prove the pushdown framework for abstract interpretation is sound and computable.
Allies for Life? Lessons From White Scholars of Multicultural Psychology

This article reports on a qualitative investigation conducted to assess how White multicultural scholars have responded to critical questions raised in the 1990s about their roles and responsibilities in multicultural psychology. We used the consensual qualitative research method to analyze semistructured interview data from 12 leading White scholars of multicultural psychology. Findings revealed 10 domains with nuanced categories within each. Key themes addressed: (a) antecedents of participants’ interest and commitments to multicultural theory and research, (b) how participants understood Whiteness broadly, and how it influenced their multicultural scholarship specifically, (c) rewards and challenges of conducting multicultural research, and (d) recommendations for future White counseling psychologists. Implications for training and future research are provided.
Experimental Study of Bidding Strategies for Scientific Workflows using AWS Spot Instances

Spot instance is an auction based Amazon Elastic Compute Cloud (EC2) instance provided by Amazon Web Service (AWS). It aims to help users to reduce their resource renting cost. The price for spot instances sometimes can be as low as one tenth of the price of the same type on demand instances. However, while gaining signicantly cost savings on renting resources, users take risks on running instances without any availability guarantees, i.e. running spot instances can be preempted by Amazon at anytime. Spot instances that get pre-empted are not charged for their last hour and some users utilize that feature to run very short jobs. Dierent bidding strategies have been proposed to ensure the execution performance of tasks submitted to spot instances. In this paper, we present a full EC2 spot instance simulator that uses real EC2 spot pricing history to emulate the spot instance life cycle and expected charges. We review eight of the most popular bidding strategies in both literature and practice and compare them in terms of cost, deadline miss rate and task’s execution length for scientic workows. Our evaluation provides users a guidance on how dierent bidding strategies may impact the execution of scientic workows.
A Self-regulated Learning approach for programming language using cloud-based Learning Management System

Indian students lack the skill of Self-regulated Learning (SRL) that is indispensable for lifelong learning. Current E-Learning systems provide content to the students as a whole, needing effort from the students to identify, discriminate and classify contents to be covered for particular topics. In this paper, a Learning Management System (LMS) is presented that provides students with electronic materials and supporting tools to inculcate Self-regulated Learning by the way of providing an environment to learn a programming language. The course contents of language are dynamically provided to the students appropriate to the level of knowledge acquired using Adaptive Learning Objects (ALOB). Students learn the contents to achieve the goal set by the teacher at the start of each level. Students monitor their learning process using a set of tools provided by the system and manage their time, effort and reading being spent in learning the provided content. Students assess their knowledge and take suitable corrective actions when required. The system is implemented as web services and made accessible anywhere, anytime by deploying it over a cloud. The results show that the students who underwent the course show increase in the self-regulation components and the interest to learn in-depth.
Evolutionary GMDH-based Identification of Building Blocks for Binary-Coded Systems

This paper proposes an approach to the problem of building block extraction in the context of evolutionary algorithms (with binary strings). The method is based upon the construction of a GMDH neural network model of a population of promising solutions with the aim of extracting building blocks from the resultant network. The operation of the proposed method is regardless of the order by which building blocks are positioned in strings representing the solutions. The experiments are carried out on some well-known benchmark functions including DeJong's
Learning Latent Variable Gaussian Graphical Models

Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.
An Axiomatic Approach to Systemic Risk

Systemic risk refers to the risk of collapse of an entire complex system as a result of the actions taken by the individual component entities or agents that comprise the system. Systemic risk is an issue of great concern in modern financial markets as well as, more broadly, in the management of complex business and engineering systems. We propose an axiomatic framework for the measurement and management of systemic risk based on the simultaneous analysis of outcomes across agents in the system and over scenarios of nature. Our framework defines a broad class of systemic risk measures that accomodate a rich set of regulatory preferences. This general class of systemic risk measures captures many specific measures of systemic risk that have recently been proposed as special cases and highlights their implicit assumptions. Moreover, the systemic risk measures that satisfy our conditions yield decentralized decompositions; i.e., the systemic risk can be decomposed into risk due to individual agents. Furthermore, one can associate a shadow price for systemic risk to each agent that correctly accounts for the externalities of the agent's individual decision making on the entire system. This paper was accepted by Gerard P. Cachon, stochastic models and simulation.
Optimization of IEEE 802 . 11 parameters for wide area coverage

Wireless mesh networks are quickly emerging as a solution for broadband wireless connectivity. The physic al layer technology is making remarkable progress allowing hi gher bandwidth and longer distance coverage than before. While standardization efforts are going on to define new standardsand paradigms for Wireless Mesh Networks, many vendors sell mes h solutions based on the 802.11b technology. This standard ha s been originally designed for Wireless Local Area Networks i n indoor scenarios with a maximum distance between nodes up to few hundred meters. As a consequence, there are many technic al challenges for adapting 802.11 to multihop networks and out door environments. This paper shows some guidelines to set the 802.11b MAC parameters in order to extend the coverage of 802.11 to long distance networks. Simulation results show that a fine tuning of MAC parameters can allow a satisfactory multi-hop wireless communication in mesh network scenario using 802.11.
Triply Stochastic Variational Inference for Non-linear Beta Process Factor Analysis

We propose a non-linear extension to factor analysis with beta process priors for improved data representation ability. This non-linear Beta Process Factor Analysis (nBPFA) allows data to be represented as a non-linear transformation of a standard sparse factor decomposition. We develop a scalable variational inference framework, which builds upon the ideas of the variational auto-encoder, by allowing latent variables of the model to be sparse. Our framework can be readily used for real-valued, binary and count data. We show theoretically and with experiments that our training scheme, with additive or multiplicative noise on observations, improves performance and prevents overfitting. We benchmark our algorithms on image, text and collaborative filtering datasets. We demonstrate faster convergence rates and competitive performance compared to standard gradient-based approaches.
Curvature, Torsion, and Force Sensing in Continuum Robots Using Helically Wrapped FBG Sensors

Due to their small size and flexibility, fiber Bragg grating (FBG) sensors have been integrated into needle-sized continuum robots for shape estimation and force measurement. The challenge in extending previous shape and force sensing technologies to pre-curved continuum robots, such as concentric-tube robots, is that torsion information is essential for accurate shape estimation, and the force-strain relationship is nonlinear. In this letter, a novel helically wrapped FBG sensor design and corresponding force-curvature-strain model are developed to provide simultaneous curvature, torsion, and force measurement. To validate this design and modeling technique, two sensorized Nitinol tubes were fabricated and tested in an experimental setup. The results showed that accurate and sensitive curvature, torsion, and force measurements can be obtained at a 100 Hz sampling rate.
Music alphabet for low-resolution touch displays

In this paper we present a novel approach to writing music on handheld pen-based devices which has been developed during the implementation of MusicMan, our pen-based music score editor. A new alphabet of music symbols has been designed especially for use on devices with small screens by musicians skilled in writing the music by hand. Our alphabet is based on simplification of the common Western music notation symbols, making them easier to write on PDA while retaining their overall feel. A user study described in this paper revealed that the alphabet is significantly faster, easier-to-use and more accepted by the firsttime users of PDA than the state-of-art method for pen-based writing of music.
La potencia estadística prospectiva del voto retrospectivo en España (1996-2015)

This research evaluates the explanatory and predictive capacity that the evaluation of the political performance / management of the PP and the PSOE has on the electoral results of these political parties. The assessment of the two political parties is made in advance, between one and two months before the elections. The design includes both political parties, so the hypothesis tested is about the relationship between performance appraisal and governmental party management with their election results (Downs, 1957). That is, the effectiveness of the retrospective voting model to explain and anticipate electoral results in general elections in Spain. The calls for papers are the general elections in Spain in 1996, 2000, 2004, 2008, 2011 and 2015. The evaluation data come from the Centre for Sociological Research and the election results of the Ministry of the Interior. After testing the hypotheses of association and dependence between the anticipated retrospective voting and the electoral results, it is concluded that the performance / management indicator of the PP and the PSOE has, in advance of one to two months, an association and significant explanatory power of electoral results. The predictive power, on the other hand, is low. Due to the sample size, the two political parties are included in the same model. Also, an analysis of the statistical power of several models is carried out, comparing different specifications.
Docosahexaenoic Acid Modulates Invasion and Metastasis of Human Ovarian Cancer via Multiple Molecular Pathways

Objective We investigated the effect of docosahexaenoic acid (DHA) on the invasion and metastasis of ovarian cancer cells (A2780, HO8910, and SKOV-3). Methods Cytotoxicity assay was performed to determine the optimal doses of DHA in this experiment. The effects of DHA on invasion ability were assessed by invasion assay. The expressions of messenger RNA and/or proteins associated with invasion or metastasis were detected by quantitative Real Time-Polymerase Chain Reaction or Western blot. The effect of DHA on cell metastasis was assessed in xenograft model of zebrafish. Results Docosahexaenoic acid and &agr;-linolenic acid could reduce the cell vitalities in dose-dependent manner. However, DHA inhibited the invasion and metastasis of ovarian cancer cells, but &agr;-linolenic acid did not (**P < 0.01). Docosahexaenoic acid could downregulate the expressions of WAVE3, vascular endothelial cell growth factor, and MMP-9, and upregulate KISS-1, TIMP-1, and PPAR-γ, which negatively correlated with cell invasion and metastasis (*P < 0.05). Docosahexaenoic acid restrained the development of subintestinal vessels and cancer cell metastasis in xenograft model of zebrafish (**P < 0.01). Conclusions Docosahexaenoic acid inhibited the invasion and metastasis of ovarian cancer cells in vitro and in vivo through the modulation of NF-κB signaling pathway, suggesting that DHA is a promising candidate for ovarian cancer therapy.
Child survival: district hospitals and paediatricians

In a previous article in this series, Zulfiquar Bhutta outlined many of the key sociopolitical issues, both national and international, that currently affect the delivery of health care to children in developing countries.1 The clear summary of our situation is that we are failing to provide even basic health care (both preventive and curative) that could reduce child mortality globally by more than half.2 Paediatricians, who have perhaps in the past felt they were at the forefront of articulating and promoting a global health agenda, should be challenged by these conclusions. The successful ratification of the United Nations Convention on the Rights of the Child3 that unequivocally target health was not a finishing line, a goal achieved, but rather a foundation for action. Therefore while researchers might have felt some satisfaction at successes in defining optimum treatment approaches, the pathways to delivering services were, and remain, far from clear. Progress is further complicated by the diverse conditions and obstacles that may be encountered worldwide.