Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Levels of the Reverse Transactivator Fail to Induce Target Transgene Expression in Vascular Smooth Muscle Cells

Hutchinson-Gilford progeria syndrome (HGPS) is a genetic disease with multiple features that are suggestive of premature aging. Most patients with HGPS carry a mutation on one of their copies of the LMNA gene. The LMNA gene encodes the lamin A and lamin C proteins, which are the major proteins of the nuclear lamina. The organs of the cardiovascular system are amongst those that are most severely affected in HGPS, undergoing a progressive depletion of vascular smooth muscle cells, and most children with HGPS die in their early teens from cardio-vascular disease and other complications from atherosclerosis. In this study, we developed a transgenic mouse model based on the tet-ON system to increase the understanding of the molecular mechanisms leading to the most lethal aspect of HGPS. To induce the expression of the most common HGPS mutation, LMNA c.1824C>T; p.G608G, in the vascular smooth muscle cells of the aortic arch and thoracic aorta, we used the previously described reverse tetracycline-controlled transactivator, sm22α-rtTA. However, the expression of the reverse sm22α-transactivator was barely detectable in the arteries, and this low level of expression was not sufficient to induce the expression of the target human lamin A minigene. The results from this study are important because they suggest caution during the use of previously functional transgenic animal models and emphasize the importance of assessing transgene expression over time.
An index rewriting scheme using compression for flash memory database systems

Flash memories are one of the best media to support portable computers' storage areas in mobile database environments. Their features include non-volatility, low power consumption, and fast access time for read operations, which are sufficient to present flash memories as major database storage components for portable computers. However, we need to improve traditional index management schemes based on B-Tree due to the relatively slow characteristics of flash operations, as compared to RAM memory. In order to achieve this goal, we propose a new index rewriting scheme based on a compressed index called F-Tree. F-Tree-based index management improves index operation performance by compressing pointers and keys in tree nodes and rewriting the nodes without a slow erase operation in node insert/delete processes. Based on the results of the performance evaluation, we conclude that the F-Tree-based scheme outperforms the traditional schemes.
Interaction detection for hybrid decomposable problems

In this paper, we present a perturbation-based linkage identification algorithm that employs a novel metric to detect linkages. The proposed metric is a combination of linearity and multiplicative relationship. The proposed method is called Interaction Detection for Hybrid Decomposable Problems (IDHDP) algorithm. Our algorithm can be applied to the additive and multiplicative decomposable problems and problems that have both kind of decomposability, i.e. hybrid decomposability. By using IDHDP, an interaction matrix is computed that represents the degree of interaction between pairs of loci. To extract linkage groups from the interaction matrix, a local threshold is calculated for each variable by the two-means algorithm. We apply IDHDP to problems with different types of decomposability. A comparison with some existing algorithms shows the efficiency and effectiveness of IDHDP.
MEDWATCH THE CLINICAL IMPACT OF ADVERSE EVENT REPORTING

(Merital R), an antide-pressant that had been available in Germany since 1976, had been prescribed to an estimated ten million patients prior to its marketing in the U.S. in July, 1985 1,2. Initial labeling for the product reflected a variety of long-recognized hypersensitivi-ty reactions, including fever, liver injury, hemolytic anemia and eosinophilia, that were apparently all readily reversible 3. At the time of U.S. approval, FDA was aware of reports of less than twenty hemolytic anemia cases, all non-fatal; however, in 1985, when foreign adverse reaction reports showed the hemolytic anemia might be fatal, labeling was revised to reflect the potential seriousness of the reaction 3. Due to an increase in serious hemolytic anemia cases seen in Europe, marketing of nomifensine was reconsidered by the manufacturer, who announced a worldwide withdrawal of the drug on January 21, 1986 3,4. The case of nomifensine illustrates that the safety profile of a drug evolves over its lifetime on the market. Even after almost ten years experience, or longer, new information that will impact the clinical use of a medical product can be detected. Consequently, all medical products need to be continually assessed for safety within the context of their perceived benefit. Medical product safety monitoring is an ongoing process accomplished through Postmarketing Surveillance, the collection of data about drugs [or any other medical product] once they are marketed and thus available to the general population 5. This process encompasses adverse event reports evaluation, generation of safety-related hypotheses and use of techniques to evaluate these hypotheses. While the U.S. has one of the most rigorous approval processes in the world, it is not possible to detect all potential problems during premarketing clinical trials. Medical product studies, ranging from preclinical animal testing and medical device bench testing to final tests in humans, have inherent limitations no matter how well they are designed or conducted. The need for postmarketing surveillance is a direct result of these limitations. Premarketing Animal Studies Most medical products are first tested in animals prior to introduction into humans. Animal studies have limitations in their ability to predict human toxicity; this is demonstrated by the case of prac-tolol, a ß 1-adrenoreceptor blocking agent withdrawn from the U.K. market in 1976 after several years of widespread use 6,7 , and never marketed in the U.S. The U.K. action was prompted by the serious adverse reactions of dermatitis, keratoconjunctivitis and sclerosing peri-tonitis, collectively …
C3a Receptor Antagonist Ameliorates Inflammatory and Fibrotic Signals in Type 2 Diabetic Nephropathy by Suppressing the Activation of TGF-β/smad3 and IKBα Pathway

Objective Diabetic nephropathy (DN) is a serious complication for patients with diabetes mellitus (DM). Emerging evidence suggests that complement C3a is involved in the progression of DN. The aim of this study was to investigate the effect of C3a Receptor Agonist (C3aRA) on DN and its potential mechanism of action in rats with type 2 diabetes mellitus (T2DM). Methods T2DM was induced in SD rats by a high fat diet (HFD) plus repeated low dose streptozocin (STZ) injections. T2DM rats were treated with vehicle or C3aRA for 8 weeks. Biochemical analysis, HE and PAS stains were performed to evaluate the renal function and pathological changes. Human renal glomerular endothelial cells (HRGECs) were cultured and treated with normal glucose (NG), high glucose (HG), HG+C3a, HG+C3a+C3aRA and HG+C3a+BAY-11-7082 (p-IKBα Inhibitor) or SIS3 (Smad3 Inhibitor), respectively. Real-time PCR, immunofluorescent staining and western blot were performed to detect the mRNA and protein levels, respectively. Results T2DM rats showed worse renal morphology and impaired renal function compared with control rats, including elevated levels of serum creatinine (CREA), blood urea nitrogen (BUN) and urine albumin excretion (UACR), as well as increased levels of C3a, C3aR, IL-6, p-IKBα, collagen I, TGF-β and p-Smad3 in the kidney of T2DM rats and C3a-treated HRGECs. In contrast, C3aRA treatment improved renal function and morphology, reduced CREA, UACR and the intensity of PAS and collagen I staining in the kidney of T2DM rats, and decreased C3a, p-IKBα, IL-6, TGF-β, p-Smad3 and collagen I expressions in HRGECs and T2DM rats. Conclusion C3a mediated pro-inflammatory and pro-fibrotic responses and aggravated renal injury in T2DM rats. C3aRA ameliorated T2DN by inhibiting IKBα phosphorylation and cytokine release, and also TGF-β/Smad3 signaling and ECM deposition. Therefore, complement C3a receptor is a potential therapeutic target for DN.
Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.
Teachers’ Views on Factors Affecting Effective Integration of Information Technology in the Classroom: Developmental Scenery

This article reports on an exploratory, longitudinal study, which examined six teachers’ views on the factors that affect technology use in classrooms. The research examined teachers of grades 4, 5, and 6—for three years, studying the teachers both as a group and as individual case studies. Three case studies were selected for analysis, with the aim of exploring the relation between the changes that occurred in the teachers’ educational views and practices as a result of their exposure to teaching and learning with the aid of rich technology and their views on factors affecting technology integration. The findings point to two developmental patterns in teachers’ views on the factors affecting technology use in the classroom: the first is concerned with the source of influence on technology adoption, and focuses mainly on the human factor; the second is concerned with the nature of the influence when using technology in the classroom, ranging from technical to cognitive transformation. The three case studies reveal three different profiles of change and demonstrate the complex relations between teachers’ orientations concerning the conditions affecting technology use, and the changes that occur in teachers’ views and practices.
Journal of Graph Algorithms and Applications Parameterized Algorithms for (r, L)-partization

We consider the (r, l)-Partization problem of finding a set of at most k vertices whose deletion results in a graph that can be partitioned into r independent sets and l cliques. Restricted to perfect graphs and split graphs, we describe sequacious fixed-parameter tractability results for (r, 0)-Partization, parameterized by k and r. For (r, l)-Partization where r + l = 2, we describe an O * (2 k) algorithm for perfect graphs. We then study the parameterized complexity hardness of a generalization of the Above Guarantee Vertex Cover by a reduction from (r, l)-Partization.
A Dispatch Method of Complex Information System Based on Service-Oriented Architecture

Service-Oriented Architecture (SOA) has become one of the most important concepts in designing complex information system. Compared with traditional architectures, SOA can be used to improve fast-respond abilities and to satisfy frequent changing of business requirements. A dispatch method of complex information system that is based on SOA is proposed in this paper. The function framework and procedure are designed with the generalized SOA architecture. In this method, a service is treated as a basic functional component. The method integrates various application modules into an all-purposed service dispatch framework. With the future development of complex information systems, SOA provides a great insight into future dispatch methods for the advances of complex information systems.
The Effect of Stemming on Arabic Text Classification: An Empirical Study

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%. DOI: 10.4018/ijirr.2011070104 International Journal of Information Retrieval Research, 1(3), 54-70, July-September 2011 55 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Mesleh et al., 2007; Rahman et al., 2003; Zubi, 2009; Al-Harbi et al., 2008). Several researches applied text classification and its techniques to English and other European languages. On the other hand, few researchers have addressed the issue of Arabic text classification. Text preprocessing and preparation; especially for Arabic, is a crucial task in several applications including; information retrieval, text mining, and natural language processing where the processing tasks include different stages such as: stop word removal and stemming. Stemming tries to reduce a word to its stem (Al-Shammari et al., 2008), stemming process uses word morphological analysis in order to get the word’s stems (Sembok et al., 2011). Stemming is a very important technique that is usually used in information retrieval and data mining as well as many other NLP applications. Stemming is important for some natural languages and unimportant in others. As reported by Sembok et al. (2011) and AlShammari (2008), stemming has the following benefits: • Stemming helps in reducing the size of the
Promise Unfulfilled: Law, Culture, and Women's Inheritance Rights in Ghana

This Report presents the findings of a Fordham delegation. It consists of three parts. Part I sets out Ghana’s obligations under international law as they relate to issues of women’s equality generally and inheritance rights specifically. Part II describes the sources of Ghanaian law relevant to the issue. Part II begins with a very general description of the Ghanaian family. It then proceeds to analyze in some detail customary law governing marriage and property, including the customary law of intestate succession, which governed estates prior to 1985. Part II then describes both constitutional and statutory law relevant to intestate succession, particularly Law 111 which created for the first time a right for spouses to inherit from the estate. Finally, Part III presents the delegation’s findings with respect to a range of problems women face in claiming their rights under the statutory regime. Part III describes and documents the problems and then offers recommendations designed to address them.
End-user debugging of machine-learned programs : toward principles for baring the logic

Many applications include machine learning algorithms intended to learn “programs” (rules of behavior) from an end user’s actions. When these learned programs are wrong, their users receive little explanation as to why, and even less freedom of expression to help the machine learn from its mistakes. In this paper, we develop and explore a set of candidate principles for providing salient debugging information to end users who would like to correct these programs. We informed the candidate principles through a formative study, built a prototype that instantiates them, and conducted a user study of the prototype to collect empirical evidence to inform future variants. Our results suggest the value of exposing the machine’s reasoning process, supporting a flexible debugging vocabulary, and illustrating the effects of user changes to the learned program’s logic. Author
Combining Targeted Metabolomic Data with a Model of Glucose Metabolism: Toward Progress in Chondrocyte Mechanotransduction

Osteoarthritis is a debilitating disease likely involving altered metabolism of the chondrocytes in articular cartilage. Chondrocytes can respond metabolically to mechanical loads via cellular mechanotransduction, and metabolic changes are significant because they produce the precursors to the tissue matrix necessary for cartilage health. However, a comprehensive understanding of how energy metabolism changes with loading remains elusive. To improve our understanding of chondrocyte mechanotransduction, we developed a computational model to calculate the rate of reactions (i.e. flux) across multiple components of central energy metabolism based on experimental data. We calculated average reaction flux profiles of central metabolism for SW1353 human chondrocytes subjected to dynamic compression for 30 minutes. The profiles were obtained solving a bounded variable linear least squares problem, representing the stoichiometry of human central energy metabolism. Compression synchronized chondrocyte energy metabolism. These data are consistent with dynamic compression inducing early time changes in central energy metabolism geared towards more active protein synthesis. Furthermore, this analysis demonstrates the utility of combining targeted metabolomic data with a computational model to enable rapid analysis of cellular energy utilization.
Spatial domain entropy based local feature extraction scheme for face recognition

In this paper, an entropy based feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. Instead of considering the entire face image as a whole, first considering small segments of image along vertical direction, the entropy values are measured for each segment and thereby a vertical signature is obtained. It is shown that the variation in face geometry along the vertical direction can be closely mapped with the extracted vertical signature. In a similar fashion a horizontal signature is obtained. Both the horizontal and vertical features are employed together in the proposed feature space, which provides a very high within class compactness and between class separation. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
Knowledge Elicitation Techniques in a Knowledge Management Context

Purpose – A significant part of knowledge and experience in an organization belongs not to the organization itself, but to the individuals it employs. Therefore, knowledge management (KM) tasks should include eliciting knowledge from knowledgeable individuals. The paper aims to argue that the current palette of methods proposed for this in KM discourse is limited by idealistic assumptions about the behavior of knowledge owners. This paper also aims to enrich the repertoire of methods that can be used in an organization to extract knowledge (both tacit and explicit) from its employees by bridging KM and knowledge engineering and its accomplishments in the knowledge elicitation field.Design/methodology/approach – This paper is based on extensive literature review and 20 years of experience of one of the authors in applying various knowledge elicitation techniques in multiple companies and contexts.Findings – The paper proposes that the special agent (analyst) might be needed to elicit knowledge from individ.
A SAT Encoding for the Social Golfer Problem

When given a combinatorial problem, one has two major tasks: to model the problem and to solve the selected model. Whilst much work in SAT algorithms is for building efﬁcient solvers, we argue that many modeling decisions have a direct impact on the solvers performance. We focus on a particular combinatorial problem: the social golfer problem, and we show how to encode this problem into SAT. An important feature of the social golfer problem is the presence of symmetries, which can be tackled by adding more clauses to the encoding. Our empirical evaluation shows that different encodings can improve or degrade search dramatically depending on the solver. We also show empirically that by choosing the right encoding one may exploit the heavy-tail behavior.
Adverse childhood experiences and associations with health-harming behaviours in young adults: surveys in eight eastern European countries

Abstract Objective To evaluate the association between adverse childhood experiences – e.g. abuse, neglect, domestic violence and parental separation, substance use, mental illness or incarceration – and the health of young adults in eight eastern European countries. Methods Between 2010 and 2013, adverse childhood experience surveys were undertaken in Albania, Latvia, Lithuania, Montenegro, Romania, the Russian Federation, The former Yugoslav Republic of Macedonia and Turkey. There were 10 696 respondents – 59.7% female – aged 18–25 years. Multivariate modelling was used to investigate the relationships between adverse childhood experiences and health-harming behaviours in early adulthood including substance use, physical inactivity and attempted suicide. Findings Over half of the respondents reported at least one adverse childhood experience. Having one adverse childhood experience increased the probability of having other adverse childhood experiences. The number of adverse childhood experiences was positively correlated with subsequent reports of health-harming behaviours. Compared with those who reported no adverse experiences, respondents who reported at least four adverse childhood experiences were at significantly increased risk of many health-harming behaviours, with odds ratios varying from 1.68 (95% confidence interval, CI: 1.32–2.15) – for physical inactivity – to 48.53 (95% CI: 31.98–76.65) – for attempted suicide. Modelling indicated that prevention of adverse childhood experiences would substantially reduce the occurrence of many health-harming behaviours within the study population. Conclusion Our results indicate that individuals who do not develop health-harming behaviours are more likely to have experienced safe, nurturing childhoods. Evidence-based programmes to improve parenting and support child development need large-scale deployment in eastern European.