Miracle-dz/UniFAR-FTUs huggingface dataset:
PPAR Agonist Rescue of Ethanol-Impaired Brain Insulin Signaling: Cerebellar Slice Culture Model

Cerebellar abnormalities in fetal alcohol spectrum disorder (FASD) have been linked to brain insulin resistance and oxidative stress. Peroxisome proliferator-activated receptor (PPAR) agonists, which have insulin sensitizer and antioxidant effects, could potentially be used to treat neurodevelopmental abnormalities in FASD. Slice cultures generated with cerebella from control or ethanol-exposed rat pups were treated with vehicle, or a PPAR-α ,P PAR- δ ,o r PPAR-γ agonist to examine effects on cytotoxicity, histopathology, insulin signaling, and neuronal/glial protein expression. PPAR agonists reduced ethanol-mediated cytotoxicity; restored cerebellar architecture; increased expression or activation of insulin/IGF- 1 receptors, IRS-1, Akt, and PRAS40; and inhibited GSK-3β. PPAR agonists increased the expression of Hu, myelin-associated glycoprotein-1, and choline acetyltransferase, and decreased the oxidative stress in ethanol-exposed cultures. The PPAR-δ and PPAR-γ agonists were more effective than the PPAR-α agonist in abrogating the adverse effects of ethanol. Therefore, early treatment with PPAR-δ or PPAR-γ agonists may help prevent long-term consequences of FASD.
Coordination Structure Analysis using Dual Decomposition

Coordination disambiguation remains a difficult sub-problem in parsing despite the frequency and importance of coordination structures. We propose a method for disambiguating coordination structures. In this method, dual decomposition is used as a framework to take advantage of both HPSG parsing and coordinate structure analysis with alignment-based local features. We evaluate the performance of the proposed method on the Genia corpus and the Wall Street Journal portion of the Penn Treebank. Results show it increases the percentage of sentences in which coordination structures are detected correctly, compared with each of the two algorithms alone.
Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control

Conventional heating, ventilating, and air conditioning (HVAC) systems have traditionally used the temperature and the humidity ratio as the quantitative indices of comfort in a room. Recently, the carbon dioxide (CO 2 ) concentration has also been recognized as having an important contribution to room comfort. This paper presents the modeling of an augmented HVAC system including CO 2 concentration, and its control strategies. Because the proposed augmented HVAC system is multi-input multi-output (MIMO) and has no relative degree problem, the dynamic extension algorithm can be employed; then, a feedback linearization technique is applied. A linear-quadratic regulator (LQR) is designed to optimize control performance and to stabilize the proposed HVAC system. Simulation results are provided to validate the proposed system model, as well as its linearized control system.
Analyzing Script Concordance Test Scoring Methods and Items by Difficulty and Type

Background: A battery of various psychometric assessments has been conducted on script concordance tests (SCTs) that are purported to measure data interpretation, an essential component of clinical reasoning. Although the breadth of published SCT research is broad, best practice controversies and evidentiary gaps remain. Purposes: In this study, SCT data were used to test the psychometric properties of 6 scoring methods. In addition, this study explored whether SCT items clustered by difficulty and type were able to discriminate between medical training levels. Methods: SCT scores from a problem-solving SCT (SCT-PS; n = 522) and emergency medicine SCT (SCT-EM; n = 1,040) were collected at a large institution of medicine. Item analyses were performed to optimize each dataset. Items were categorized into difficulty levels and organized into types. Correlational analyses, one-way multivariate analysis of variance (MANOVA), repeated measures analysis of variance (ANOVA), and one-way ANOVA were conducted to explore study aims. Results: All 6 scoring methods differentiated between training levels. Longitudinal analysis of SCT-PS data reported that MS4s significantly (p < .001) outperformed their scores as MS2s in all difficulty categories. Cross-sectional analysis of SCT-EM data reported significant differences (p < .001) between experienced EM physicians, EM residents, and MS4s at each level of difficulty. Items categorized by type were also able to detect training level disparities. Conclusions: Of the 6 scoring methods, 5-point scoring solutions generated more reliable measures of data interpretation than 3-point scoring methods. Data interpretation abilities were a function of experience at every level of item difficulty. Items categorized by type exhibited discriminatory power providing modest evidence toward the construct validity of SCTs.
Service differentiation based medium access control protocol for real-time application

Today Wireless Sensor Networks are increasingly used for real-time applications, which leads to take up scientist focus as a promising technology in the field of embedded systems. The toughest challenge of these networks is the energy conservation. So, most of MAC protocols designed for WSNs, aim to reduce energy consumption, which affects negatively the latency, especially if these networks are deployed in emergency response applications. In this paper we present a strategy that respects energy constraint and, in the same time, aims to manage the medium access according to the priority of messages. Our results prove that our algorithm enhances the quality of service (QoS) in terms of prioritized message latency, as well in other performance criteria.
Addressing System-Level Optimization with OpenVX Graphs

During the performance optimization of a computer vision system, developers frequently run into platform-level inefficiencies and bottlenecks that can not be addressed by traditional methods. OpenVX is designed to address such system-level issues by means of a graph-based computation model. This approach differs from the traditional acceleration of one-off functions, and exposes optimization possibilities that might not be available or obvious with traditional computer vision libraries such as OpenCV.
Rethinking Virtual Machine Interference in the Era of Cloud Applications

Data centers are increasingly employing virtualization as a means to ensure the performance isolation for latency-sensitive applications while allowing co-locations of multiple applications. Previous research has shown that virtualization could offer excellent resource isolation. However, whether virtualization can mitigate the interference among micro-architectural resources has not been well studied. This paper presents an in-depth analysis of the performance isolation effect of virtualization technology on various micro-architectural resources (i.e., L1 D-Cache, L2 Cache, last level cache (LLC), hardware prefetchers and Non-Uniform Memory Access) by mapping the CloudSuite benchmarks to different sockets, different cores of one chip, and different threads of one core. For each resource, we investigate the correlation between performance variations and contention by changing VM mapping policies according to different application characteristics. Our experiments show that virtualization has rather limited micro-architectural isolation effects. Specifically, LLC interference can degrade applications performance by as much as 28%. When it comes to intra-core resources, the applications performance degradation can be as much as 27%. Additionally, we outline several opportunities to improve performance by reducing misbehavior VM interference.
Cloudifying the 3GPP IP multimedia subsystem for 4G and beyond: A survey

4G systems have been continuously evolving to cope with the emerging challenges of human-centric and M2M applications. Research has also now started on 5G systems. Scenarios have been proposed and initial requirements derived. 4G and beyond systems are expected to easily deliver a wide range of human-centric and M2M applications and services in a scalable, elastic, and cost-efficient manner. The 3GPP IMS was standardized as the service delivery platform for 3G networks. Unfortunately, it does not meet several requirements for provisioning applications and services in 4G and beyond systems. However, cloudifying it will certainly pave the way for its use as a service delivery platform for 4G and beyond. This article presents a critical overview of the architectures proposed so far for cloudifying the IMS. There are two classes of approaches; the first focuses on the whole IMS system, and the second deals with specific IMS entities. Research directions are also discussed. IMS granularity and a PaaS for the development and management of IMS functional entities are the two key directions we currently foresee.
Feminism and Pornography after the Sex Wars: Diversifying Pornography

Both through its newfound prominence in mainstream popular culture, and by the sheer mass of material available to the consumer, the dictates of mainstream pornography on conceptions of gender and sexuality are at best highly visible and at worst deeply resonant within the cultural psyche. Using the force of the viscerally and psychologically affective medium of graphic sexual imagery, what I term mainstream pornography consistently maps binary gender roles to dominant and submissive power relations.
A Novel Image Retrieval Approach with Bag-of-Word Model and Gabor Feature

In the past few years, image retrieval has been one of the hot spots in computer vision field. Among many image retrieval techniques, Bag-of, Word (BoW) model is one of the effective and efficient methods that can search images with visual vocabularies and it is insensitive to massive data and various geometric attacks. But the classical BoW algorithm used some descriptors as its visual words, such as SIFT, which are also used to build visual vocabulary. The main problem with traditional BoW algorithm is that the visual vocabulary could not reflect the spatial information of visual words. And most BoW algorithms utilize one single feature as their feature vector, some other important features are ignored. All these factors have influenced the accuracy of final result in traditional BoW model. In order to solve the problem, we propose a novel image retrieval method with BoW and Gabor feature. The paper first proposes a new saliency map extraction method, and then the saliency score is used to build visual vocabulary. At last, Gabor feature is combined with visual vocabulary together to compute similarity. In order to test the effectiveness of the algorithm, we evaluate our method on the SIMPLICITY dataset and Stanford dataset. Experiments results demonstrate the effectiveness and accuracy of the proposed method.
Life Satisfaction and Frailty Among Older Adults

Functional and physical impairment are factors believed to lead to declined life satisfaction among older adults. This study aimed to examine life satisfaction among older adults and the influence of frailty. Baseline data from two studies addressing frail older adults aged 80+ in Gothenburg, Sweden, (n=577) were used. Frailty was measured through eight indicators. Life satisfaction was measured with Fugl-Meyer’s instrument LiSat-11. Perceived life satisfaction was rather high within the studied population, with 66% being satisfied with life as a whole. Most life satisfaction items were significantly associated with frailty status, with non-frail participants being satisfied to a higher extent for all items with the exception of financial situation, sexual life and partnership relation. The factors significantly explaining life satisfaction were psychological health, partner relationship, leisure and ADL. This study shows that older adults’ satisfaction with life as a whole is almost as high as in younger age groups. Respondents with higher degree of frailty reported significantly lower degrees of life satisfaction, indicating a possibility to maintain life satisfaction by preventing or delaying the development of frailty.
Extraction of Physical Motion Dictating the Difference between Skilled Cyclist and Beginner

In the field of cycling competition, cyclists commonly utilize binding pedals to maximize their competitive performance. The feature of binding pedals is to hold the sole of cycling shoe on the upper body of pedal, which enables a cyclist to not only push down the pedals but also pull up the pedals in one revolution of pedaling motion. Pedaling motion is implemented by the combination of muscle activities of lower extremity. The conversion efficiency from the physical strength of a cyclist to the propulsion power of a bicycle has been considered as the skill of pedaling exercise. By the way, neither effective instruments nor methods to accurately evaluate the pedaling skill have been established. Then, this study aims to extract the difference of physical motions between a skilled cyclist and a beginner by using the mechanical data of a bicycle and the electrophysiological data of a subject while pedaling exercise. This paper firstly introduces the experimental device and describes the protocol of measurement experiment. The measured data are statistically processed by using principal component analysis to extract the physical motions dictating the difference of pedaling skill between a skilled cyclist and a beginner.
Evaluation of fault-tolerant systems with nonhomogeneous workloads

A methodology is presented for evaluating fault-tolerant systems when workloads and fault arrivals are not time-homogeneous. Of particular interests are systems whose environments vary considerably between different utilization phases of random duration. In such cases, evaluations of overall system performability must account for the corresponding differences in workload effects, especially with regard to fault recovery. The proposed methodology uses analytic techniques based on Markov processes and stochastic activity networks. Examples of evaluation studies, using this approach, are presented. These include evaluation of a system wherein self-exercising is varied between phases of passive and active use. <<ETX>>
Ki67 index in breast cancer: correlation with other prognostic markers and potential in pakistani patients.

INTRODUCTION Breast cancer aggressiveness can be correlated with proliferation status of tumor cells, which can be ascertained with tumor grade and Ki67 indexing. However due to lack of reproducibility, the ASCO do not recommend routine use of Ki67 in determining prognosis in newly diagnosed breast cancers. We therefore aimed to determine associations of the Ki67 index with other prognostic markers like tumor size, grade, lymph node metastasis, ER, PR and HER2neu status. METHODS A total of 194 cases of newly diagnosed breast cancer were included in the study. Immunohistochemical staining for ER, PR, HER2neu and Ki67 was performed by the DAKO envision method. Associations of the Ki67 index with other prognostic factors were evaluated both as continuous and categorical variables. RESULTS Mean age of the patients was 51.7 years (24-90). Mean Ki67 index was 26.9% (1-90). ER, PR, HER2neu positivity was noted in 90/194 cases (46.4%), 74/194 cases (38.1%) and 110/194 cases (56.70%) respectively. Significant association was found between Ki67 and tumor grade, PR, HER2neu positivity and lymph node status, but no link was apparent with ER positivity and tumor size. There wasan inverse relation between Ki67 index and PR positivity, whereas a direct correlation was seen with HER2neu positivity. However, high Ki67 (>30%) was associated with decreased HER2neu positivity as compared to intermediate Ki67 (16-30%). The same trend was established with lymph node metastasis. CONCLUSION Our study indicates that with high grade tumors, clinical utility of ki67 is greater in combination with other prognostic markers because we found that tumors with Ki67 higher than 30% have better prognostic profile compared to tumors with intermediate Ki67 level, as reflected by slightly lower frequency of lymph node metastasis and HER2neu expression. Therefore we suggest that Ki67 index should be categorized into high, intermediate and low groups when considering adjuvant chemotherapy and prognostic stratification.
Expanding Approach to Information Retrieval Using Semantic Similarity Analysis Based on WordNet and Wikipedia

Performance of information retrieval (IR) systems greatly relies on textual keywords and retrieval documents. Inaccurate and incomplete retrieval results are always induced by query drift and ignorance of semantic relationship among terms. Expanding retrieval approach attempts to incorporate expansion terms into original query, such as unexplored words combing from pseudo-relevance feedback (PRF) or relevance feedback documents semantic words extracting from external corpus etc. In this paper a semantic analysis-based query expansion method for information retrieval using WordNet and Wikipedia as corpus are proposed. We derive semantic-related words from human knowledge repositories such as WordNet and Wikipedia, which are combined with words filtered by semantic mining from PRF document. Our approach automatically generates new semantic-based query from original query of IR. Experimental results on TREC datasets and Google search engine show that performance of information retrieval can be significantly improved using proposed method over previous results.
Modeling the Other Race Effect with ICA

Principal component analysis (PCA) learns the second-order dependencies be-tween image pixels, and performs information maximization when the input is Gaussian. Although PCA has been used for image analysis, images are not inherently Gaussian. Independent component analysis (ICA) learns higher order dependencies among image pixels and performs information maximization for many distributions. An ICA model (Bartlett et al. 2002) has already been successfully applied to face processing and has been shown to be superior to PCA for facial recognition using both a spatially local image basis and a spatially global image basis (Bartlett 2007). The other race effect refers to the well-known phenomenon that people are better at recognizing faces from members of their own race than members of other races. This effect has been demonstrated for a PCA model (Haque and Cottrell 2005) trained on Caucasian and Asian faces from the FERET database. We extended the ICA model to account for the other race effect. We found that the ICA1 model demonstrated the other race effect and this effect was stronger for the ICA1 (local) model than the PCA model. The results were not as compelling for the ICA2 (global)model.
Thin slicing

Program slicing systematically identifies parts of a program relevant to a seed statement. Unfortunately, slices of modern programs often grow too large for human consumption. We argue that unwieldy slices arise primarily from an overly broad definition of relevance, rather than from analysis imprecision. While a traditional slice includes all statements that may affect a point of interest, not all such statements appear equally relevant to a human. As an improved method of finding relevant statements, we propose thin slicing. A thin slice consists only of producer statements for the seed, i.e., those statements that help compute and copy avalue to the seed. Statements that explain why producers affect the seed are excluded. For example, for a seed that reads a value from a container object, a thin slice includes statements that store the value into the container, but excludes statements that manipulate pointers to the container itself. Thin slices can also be hierarchically expanded to include statements explaining how producers affect the seed, yielding a traditional slice in the limit. We evaluated thin slicing for a set of debugging and program understanding tasks. The evaluation showed that thin slices usually included the desired statements for the tasks (e.g., the buggy statement for a debugging task). Furthermore, in simulated use of a slicing tool, thin slices revealed desired statements after inspecting 3.3 times fewer statements than traditional slicing for our debugging tasks and 9.4 times fewer statements for our program understanding tasks. Finally, our thin slicing algorithm scales well to relatively large Java benchmarks, suggesting that thin slicing represents an attractive option for practical tools.
Brain Science on Chronic Fatigue

The sense of fatigue is one of the important bio-alarm systems like pain or fever. However, the neural and molecular mechanisms of fatigue remain unclear. Chronic fatigue syndrome (CFS), involving a long-lasting sensation of fatigue, seems to be a good model for studying these mechanisms underlying chronic fatigue sensation. Recently, to explore the neural and molecular mechanisms of fatigue/chronic fatigue and to investigate the pathogenesis of CFS, we organized a study group of Japanese investigators from various fields, such as virology, immunology, endocrinology, physiology, biochemistry, psychiatry, and neuroscience. From our recent results, CFS can be understood as a special condition based on abnormality of the psycho-neuroendocrino-immunological system, with the distinguishing feature of CFS seeming to be the secondary brain dysfunction caused by several cytokines and/or autoantibodies.
Tool Integration Environment

Synop sis: This report presents the design of a workbench for enterprise architecture that serves as a tool integration environment and a modelling tool at the same time: it supports both technical integration of existing modelling tools and conceptual integration of modelling schemas. The workbench is a viewpoint-driven environment that provides the means to bring together and elaborate upon existing heterogeneous content, as well as to break down existing content into more specific content managed by dedicated tools. V ArchiMate Organisations need to adapt increasingly quickly and anticipate changing customer requirements and business goals. This need influences the entire chain of activities of a business, from the organisational structure to the network infrastructure. How can you control the impact of these changes? Architecture may be the answer. The ArchiMate project will develop an integrated architectural approach that describes and visualises the different business domains and their relations. Using these integrated architectures aids stakeholders in assessing the impact of design choices and changes. Architecture is a consistent whole of principles, methods and models that are used in the design and realisation of organisational structure, business processes, information systems, and infrastructure. However, these domains are often not approached in an integrated way, which makes it difficult to judge the effects of proposed changes. Every domain speaks its own language, draws its own models, and uses its own techniques and tools. Communication and decision making across domains is seriously impaired. The goal of the ArchiMate project is to provide this integration. By developing an architecture language and visualisation techniques that picture these domains and their relations, ArchiMate will provide the architect with instruments that support and improve the architecture process. Existing and emerging standards will be used or integrated whenever possible. ArchiMate will actively participate in national and international fora and standardisation organisations, to promote the dissemination of project results. The project will deliver a number of results. First of all, we will provide a visual design language with adequate concepts for specifying interrelated architectures, and specific viewpoints for selected stakeholders. This will be accompanied by a collection of best practices and guidelines. Furthermore, ArchiMate will develop techniques that support architects in visualisation and analysis of architectures. Finally, project results will be validated in real-life cases within the participating organisations. To have a real impact on the state of the art in architecture, the ArchiMate project consists of a broad consortium from industry …