Miracle-dz/UniFAR-FTUs huggingface dataset:
COBRA - Mining Web for Corporate Brand and Reputation Analysis

Corporations are extremely sensitive to issues such as brand stewardship and product reputation. Traditional brand image and reputation tracking is limited to news wires and contact centres analysis. However, with the emergence of web, Consumer Generated Media (CGM), such as blogs, news forums, message boards, and web pages/sites, is rapidly becoming the "voice of the people". This paper describes a COBRA (COrporate Brand and Reputation Analysis) solution that mines a wide range of CGM contents for brand and reputation analysis. The solution contains a flexible ETL (Extract, Transform, and Load) engine that processes diverse sets of structured and unstructured information, a suite of analytical capabilities that mines CGM content to extract semantic entities and insights out of the data, and an alerting mechanism that utilizes the analytics results to accurately generate brand and reputation alerts. We use a real-world case study to demonstrate the effectiveness of our approach.
Performance Modeling and Prediction of Face Recognition Systems

It is a challenging task to accurately model the performance of a face recognition system, and to predict its individual recognition results under various environments. This paper presents generic methods to model and predict the face recognition performance based on analysis of similarity measurement. We first introduce a concept of "perfect recognition", which only depends on the intrinsic structure of a recognition system. A metric extracted from perfect recognition similarity scores (PRSS) allows modeling the face recognition performance without empirical testing. This paper also presents an EM algorithm to predict the recognition rate of a query set. Furthermore, features are extracted from similarity scores to predict recognition results of individual queries. The presented methods can select algorithm parameters offline, predict recognition performance online, and adjust face alignment online for better recognition. Experimental results show that the performance of recognition systems can be greatly improved using presented methods.
Efficacy of Antioxidant Treatment in Reducing Resistin Serum Levels: A Randomized Study

Objectives: Few in vitro studies have examined the participation of resistin, a recently discovered adipokine, in oxidative processes. We investigated whether in vivo treatment with the antioxidant vitamin C might affect resistin serum levels. Design: Randomized prospective open trial. Setting: San Giovanni Battista Hospital, Turin, Italy. Participants: Eighty healthy individuals. Intervention: Administration of 2 g of ascorbic acid orally for 2 wk (n = 40; experimental group) or no supplementation (n = 40; control group). Outcome measures: The primary end point was the between-group difference in the before–after change in resistin serum level after vitamin C supplementation. Secondary endpoints were the within- and between-group changes in glucose, insulin, lipid parameters, C-reactive protein fasting values, and markers of oxidative stress. Results: In the experimental group, vitamin C supplementation was significantly associated with both resistin concentration reduction (from 4.3 ± 1.5 to 2.9 ± 0.8 ng/ml; 95% confidence interval [CI] −1.87, −1.03) and ascorbic acid level increase (from 9.4 ± 2.9 to 19.0 ± 5.2 mg/l; 95% CI 7.9, 11.2). In the control group, resistin levels did not change significantly (from 4.2 ± 1.0 to 4.3 ± 0.9 ng/ml; 95% CI −0.07, 0.37). The between-group differences were highly significant (p < 0.001). Vitamin C supplementation was also associated with a statistically significant reduction in nitrotyrosine level and incremental increase in reduced glutathione. In a linear regression model, within-individual changes in vitamin C concentrations were inversely correlated with changes in resistin levels in both groups (each unit increase of vitamin C corresponded to a decrease of about 0.10 units of resistin levels (95% CI 0.13, 0.08; p < 0.001). Conclusion: This is to our knowledge the first randomized trial in humans that has demonstrated that short-term vitamin C supplementation could significantly reduce resistin levels, independent of changes in inflammatory or metabolic variables. Future investigations of resistin participation in oxidative processes are warranted.
SimRound, A novel approach to transitions of Junior Medical Students into Clinical Clerkships: Rationale, Development and Lessons Learnt

Introduction Junior students making the transition from pre-clinical to clinical years often struggle to integrate into ward teams during ward-round activities. The SimRound is a novel approach to facilitate embedding of junior medical students into incumbent ward teams through simulation rounds specially designed to replicate actual ward-round proceedings. Using the situated learning and cognitive load theories, this study aims to describe the rationale, processes and lessons learnt from our experience in developing the SimRound.
Where to Go: Breaking the Symmetry in Cell Motility

Cell migration in the “correct” direction is pivotal for many biological processes. Although most work is devoted to its molecular mechanisms, the cell’s preference for one direction over others, thus overcoming intrinsic random motility, epitomizes a profound principle that underlies all complex systems: the choice of one axis, in structure or motion, from a uniform or symmetric set of options. Explaining directional motility by an external chemo-attractant gradient does not solve but only shifts the problem of causation: whence the gradient? A new study in PLOS Biology shows cell migration in a self-generated gradient, offering an opportunity to take a broader look at the old dualism of extrinsic instruction versus intrinsic symmetry-breaking in cell biology.
Joint Modeling of Longitudinal and Time-to-Event Data

It has been long recognized that missing data are the norm rather than the exception when it comes to the analysis of real data. In this chapter we focus on follow-up studies and on the statistical analysis of longitudinal outcomes with missing data. Due to the wide use of longitudinal studies in many different disciplines, there has been a lot of research in the literature on extensions of selection and pattern-mixture models, which can be considered as the two traditional modeling frameworks for handling missing data (Little and Rubin, 2002; Molenberghs and Kenward, 2007), to the longitudinal setting; see for instance, Verbeke and Molenberghs, (2000), Fitzmaurice et al. (2004), and references therein. These models are applied in non-random missing datasettings, i.e., when the probability of missingness may depend on unobserved longitudinal responses, and require defining the joint distribution of the longitudinal and dropout processes.
Extrapolation-Directed Crossover for Job-shop Scheduling Problems: Complementary Combination with JOX

In this paper, we propose a new Genetic Algorithm for JSP using two crossovers. The crossover, JOX, obtained relatively good results, however offspring generated by JOX exist around parents or within an intermediate area of them. This feature of JOX induces a convergence of the whole population. To deal with this fault of JOX, we propose a complementary combination of two crossovers. One is JOX, and the other, EDX, is our proposal. EDX is designed to have the population enlarge using a local search and explores the area where the population uncovers. Although a mutation is applied for exploration in general, we apply a framework of crossover to EDX for a more efficient exploration. The combination of two crossovers, which has a different search area, is able to compensate for each other's fault. The GA designed with these two crossovers was applied to large-size JSP benchmarks, and we show its effectiveness.
The rice ERF transcription factor OsERF922 negatively regulates resistance to Magnaporthe oryzae and salt tolerance

Rice OsERF922, encoding an APETELA2/ethylene response factor (AP2/ERF) type transcription factor, is rapidly and strongly induced by abscisic acid (ABA) and salt treatments, as well as by both virulent and avirulent pathovars of Magnaporthe oryzae, the causal agent of rice blast disease. OsERF922 is localized to the nucleus, binds specifically to the GCC box sequence, and acts as a transcriptional activator in plant cells. Knockdown of OsERF922 by means of RNAi enhanced resistance against M. oryzae. The elevated disease resistance of the RNAi plants was associated with increased expression of PR, PAL, and the other genes encoding phytoalexin biosynthetic enzymes and without M. oryzae infection. In contrast, OsERF922-overexpressing plants showed reduced expression of these defence-related genes and enhanced susceptibility to M. oryzae. In addition, the OsERF922-overexpressing lines exhibited decreased tolerance to salt stress with an increased Na+/K+ ratio in the shoots. The ABA levels were found increased in the overexpressing lines and decreased in the RNAi plants. Expression of the ABA biosynthesis-related genes, 9-cis-epoxycarotenoid dioxygenase (NCED) 3 and 4, was upregulated in the OsERF922-overexpressing plants, and NCED4 was downregulated in the RNAi lines. These results suggest that OsERF922 is integrated into the cross-talk between biotic and abiotic stress-signalling networks perhaps through modulation of the ABA levels.
A reliable brain computer interface implemented on FPGA for mobile dialing system

This paper demonstrates a high performance brain-computer interface (BCI) that allows users to dial phone numbers. The system is based on Canonical Correlation Analysis (CCA) and Steady-State Visual Evoked Potential (SSVEP). Through six buttons (9Hz, 10Hz, 11Hz, 12Hz, 13 Hz, 14Hz) displayed on the screen, subjects can choose the number by gazing at the computer interface. This proposed EEG (Electroencephalography) system has been implemented in Field-Programmable Gate Arrays (FPGA) and it features high accuracy, integration density, and low cost. These features are meaningful for implementing a real time SSVEP-based BCI.
Inference from Clustering with Application to Gene-Expression Microarrays

There are many algorithms to cluster sample data points based on nearness or a similarity measure. Often the implication is that points in different clusters come from different underlying classes, whereas those in the same cluster come from the same class. Stochastically, the underlying classes represent different random processes. The inference is that clusters represent a partition of the sample points according to which process they belong. This paper discusses a model-based clustering toolbox that evaluates cluster accuracy. Each random process is modeled as its mean plus independent noise, sample points are generated, the points are clustered, and the clustering error is the number of points clustered incorrectly according to the generating random processes. Various clustering algorithms are evaluated based on process variance and the key issue of the rate at which algorithmic performance improves with increasing numbers of experimental replications. The model means can be selected by hand to test the separability of expected types of biological expression patterns. Alternatively, the model can be seeded by real data to test the expected precision of that output or the extent of improvement in precision that replication could provide. In the latter case, a clustering algorithm is used to form clusters, and the model is seeded with the means and variances of these clusters. Other algorithms are then tested relative to the seeding algorithm. Results are averaged over various seeds. Output includes error tables and graphs, confusion matrices, principal-component plots, and validation measures. Five algorithms are studied in detail: K-means, fuzzy C-means, self-organizing maps, hierarchical Euclidean-distance-based and correlation-based clustering. The toolbox is applied to gene-expression clustering based on cDNA microarrays using real data. Expression profile graphics are generated and error analysis is displayed within the context of these profile graphics. A large amount of generated output is available over the web.
Acacia-RDF: An X10-Based Scalable Distributed RDF Graph Database Engine

Linked data mining has become one of the key questions in High Performance graph mining in recent years. However, the existing Resource Description Framework (RDF) database engines are not scalable and are less reliable in heterogeneous clouds. In this paper we describe the design and implementation of Acacia-RDF which is a scalable distributed RDF graph database engine developed with X10 programming language to solve this issue. Acacia-RDF partitions the RDF data sets into subgraphs following vertex cut paradigm. The partitioned data sets are persisted on secondary storage across X10 places. We developed a scalable SPARQL (an RDF query language) processor for Acacia-RDF which operates on top of partitioned RDF data. Furthermore, we designed and implemented a replication based fault tolerance mechanism for Acacia-RDF. We present performance results gathered from Acacia with different scales of LUBM (Lehigh University Benchmark) RDF benchmark data sets. We make a comparison of Acacia-RDF's performance against Neo4j graph database server. From the scalability experiments conducted upto 16 X10 places, we observed that Acacia-RDF scales well with LUBM data sets. Acacia-RDF reported less than ten seconds elapsed times on 16 places for running the first and the third queries of the LUBM benchmark on LUBM 160 universities data set with 3.6 million vertices and 28.5 million edges which was 1.7GB in size. Through this work we describe and demonstrate the use of X10 language for development of scalable RDF graph data management systems.
On the Optimality of Binning for Distributed Hypothesis Testing

We study a hypothesis testing problem in which data are compressed distributively and sent to a detector that seeks to decide between two possible distributions for the data. The aim is to characterize all achievable encoding rates and exponents of the type 2 error probability when the type 1 error probability is at most a fixed value. For related problems in distributed source coding, schemes based on random binning perform well and are often optimal. For distributed hypothesis testing, however, the use of binning is hindered by the fact that the overall error probability may be dominated by errors in the binning process. We show that despite this complication, binning is optimal for a class of problems in which the goal is to “test against conditional independence. ” We then use this optimality result to give an outer bound for a more general class of instances of the problem.
The A-kinase anchoring protein Yotiao binds and regulates adenylyl cyclase in brain

A-kinase anchoring proteins (AKAPs) influence the spatial and temporal regulation of cAMP signaling events. Anchoring of PKA in proximity to certain adenylyl cyclase (AC) isoforms is thought to enhance the phosphorylation dependent termination of cAMP synthesis. Using a combination of immunoprecipitation and enzymological approaches, we show that the plasma membrane targeted anchoring protein AKAP9/Yotiao displays unique specificity for interaction and the regulation of a variety of AC isoforms. Yotiao inhibits AC 2 and 3, but has no effect on AC 1 or 9, serving purely as a scaffold for these latter isoforms. Thus, Yotiao represents an inhibitor of AC2. The N terminus of AC2 (AC2-NT), which binds directly to amino acids 808–957 of Yotiao, mediates this interaction. Additionally, AC2-NT and Yotiao (808–957) are able to effectively inhibit the association of AC2 with Yotiao and, thus, reverse the inhibition of AC2 by Yotiao in membranes. Finally, disruption of Yotiao–AC interactions gives rise to a 40% increase in brain AC activity, indicating that this anchoring protein functions to directly regulate cAMP production in the brain.
Innovative approaches to establish and characterize primary cultures: an ex vivo 3D system and the zebrafish model

ABSTRACT Patient-derived specimens are an invaluable resource to investigate tumor biology. However, in vivo studies on primary cultures are often limited by the small amount of material available, while conventional in vitro systems might alter the features and behavior that characterize cancer cells. We present our data obtained on primary dedifferentiated liposarcoma cells cultured in a 3D scaffold-based system and injected into a zebrafish model. Primary cells were characterized in vitro for their morphological features, sensitivity to drugs and biomarker expression, and in vivo for their engraftment and invasiveness abilities. The 3D culture showed a higher enrichment in cancer cells than the standard monolayer culture and a better preservation of liposarcoma-associated markers. We also successfully grafted primary cells into zebrafish, showing their local migratory and invasive abilities. Our work provides proof of concept of the ability of 3D cultures to maintain the original phenotype of ex vivo cells, and highlights the potential of the zebrafish model to provide a versatile in vivo system for studies with limited biological material. Such models could be used in translational research studies for biomolecular analyses, drug screenings and tumor aggressiveness assays. Summary: Patient-derived primary cells maintain their original phenotype when cultured on 3D scaffolds, and their in vivo behavior can be effectively studied with innovative zebrafish models.
Modeling the Factors Affecting the Adoption of Mobile Commerce

Mobile commerce also known as M-commerce is thought to be the next big phase in technologically dependent society after following E-commerce era. This study aims to identify some factors that affect the adoption of M-commerce in Malaysia by developing M-commerce adoption Model based on exiting technology model such as, TRA, TPB, TAM and DOI. Structural Equation Modeling (SEM) is used to test the proposed model. The findings revealed that Perceived Usefulness, Perceived Ease of Use and Perceived trust are positively associated with consumer attitude towards use which is affecting consumer intention to use. PI and perceived cost were found to have insignificant effect on consumer attitude towards use. The findings made a contribution, allowing us to understand the factors that can affect the adoption of M-commerce. The findings have showed that the extended and modified model has good fit to the early adopter of M-commerce services and it explained 39 percent of the consumer’s intentions to adopt M-commerce services. The proposed M-commerce model has the potential to use as a basis for industry players’ evaluation of adoption potential of new M-commerce services.
Data memory design and exploration for low-power embedded systems

In embedded system design, the designer has to choose an on-chip memory configuration that is suitable for a specific application. To aid in this design choice, we present a memory exploration procedure based on three performance metrics, namely, cache size, the memory access time and the energy consumption. We show the importance of including energy in the performance metrics, since an increase in the cache size and line size reduces the memory access time but does not necessarily reduce the energy consumption. The memory exploration procedures enable us to find the cache configuration (cache size, line size) that satisfies the area and time constraints while minimizing the energy consumption, and the cache configuration that satisfies the area and energy constraints while minimizing the memory access time. The exploration procedures for cache configuration is very efficient since it considers only a selected set of candidate points. Finally, we validate our exploration procedures by running simulation experiments on MediaBench applications.
4×10 Gb/s coherent WDM-PON system over 110 km single mode fibre and with 55 dB ODN power budget

Simplified coherent detection of directly-modulated DFB transmitters allows for 55 dB ODN power budget in downstream of a 40 Gb/s DML-based WDM-PON with 110 km reach. The solution can enhance NGPON2, addressing long reach and high power budget links (>50 dB).
Sildenafil for improving outcomes in patients with corrected valvular heart disease and persistent pulmonary hypertension: a multicenter, double-blind, randomized clinical trial

Abstract Aims We aimed to determine whether treatment with sildenafil improves outcomes of patients with persistent pulmonary hypertension (PH) after correction of valvular heart disease (VHD). Methods and results The sildenafil for improving outcomes after valvular correction (SIOVAC) study was a multricentric, randomized, parallel, and placebo-controlled trial that enrolled stable adults with mean pulmonary artery pressure ≥ 30 mmHg who had undergone a successful valve replacement or repair procedure at least 1 year before inclusion. We assigned 200 patients to receive sildenafil (40 mg three times daily, n = 104) or placebo (n = 96) for 6 months. The primary endpoint was the composite clinical score combining death, hospital admission for heart failure (HF), change in functional class, and patient global self-assessment. Only 27 patients receiving sildenafil improved their composite clinical score, as compared with 44 patients receiving placebo; in contrast 33 patients in the sildenafil group worsened their composite score, as compared with 14 in the placebo group [odds ratio 0.39; 95% confidence interval (CI) 0.22–0.67; P < 0.001]. The Kaplan–Meier estimates for survival without admission due to HF were 0.76 and 0.86 in the sildenafil and placebo groups, respectively (hazard ratio 2.0, 95% CI = 1.0–4.0; log-rank P = 0.044). Changes in 6-min walk test distance, natriuretic peptides, and Doppler-derived systolic pulmonary pressure were similar in both groups. Conclusion Treatment with sildenafil in patients with persistent PH after successfully corrected VHD is associated to worse clinical outcomes than placebo. Off-label usage of sildenafil for treating this source of left heart disease PH should be avoided. The trial is registered with ClinicalTrials.gov, number NCT00862043.
Improved constant approximation factor algorithms for k-center problem for uncertain data

In real applications, database systems should be able to manage and process data with uncertainty. Any real dataset may have missing or rounded values, also the values of data may change by time. So, it becomes important to handle these uncertain data. An important problem in database technology is to cluster these uncertain data. In this paper, we study the $k$-center problem for uncertain points in a general metric space. First we present a greedy approximation algorithm that builds $k$ centers using a farthest-first traversal in $k$ iterations. This algorithm improves the approximation factor of the unrestricted assigned $k$-center problem from $10$ to $6$. Next we restrict the centers to be selected from a finite set of points and we show that the optimal solution for this restricted setting is a $2$-approximation factor solution for the optimal solution of the assigned $k$-center problem. Using this idea we improve the approximation factor of the unrestricted assigned $k$-center problem to $4$ by increasing the running time mildly.