Miracle-dz/UniFAR-FTUs huggingface dataset:
Publication V

Energy consumption of wireless data transmission, a significant part of the overall energy consumption on a mobile device, is context-dependent it depends on several internal and external contexts, such as application workload and wireless signal strength. In this paper, we propose an event-driven framework that can be used for efficient power management on mobile devices. The framework adapts the behavior of a device component or an application to the changes in contexts, defined as events, according to developer-specified event-condition-action (ECA) rules that describe the power management mechanism. In contrast to previous work, our framework supports complex event processing in addition to simple event processing. By correlating events, complex event processing helps to discover complex events that are relevant to power consumption. Using our framework developers can implement and configure power management applications by editing event specifications and ECA rules through XML-based interfaces. We evaluate this framework with two applications in which the data transmission is adapted to traffic patterns and wireless link quality. These applications can save roughly 12% more energy compared to normal operation.
WHY IS ANT A USEFUL FRAMEWORK FOR INVESTIGATING TECHNOLOGICAL INNOVATION?

This preface discusses Actor-Network Theory (ANT), and in particular Innovation Translation – its approach to innovation adoption and its value as an analytical framework for theorising technological innovation. It begins by noting the controversy with the name: actor-network theory (ANT), and whether this should be changed to something like “actant-rhyzome ontology” of “the sociology of translation. ” It then makes use of a number of early classic papers by Latour, Callon
Network Topology and Communication Data Rate for Consensusability of Discrete-Time Multi-Agent Systems

This paper investigates the joint effect of agent dynamic, network topology and communication data rate on consensusability of linear discrete-time multi-agent systems. Neglecting the finite communication data rate constraint and under undirected graphs, a necessary and sufficient condition for consensusability under a common control protocol is given, which explicitly reveals how the intrinsic entropy rate of the agent dynamic and the communication graph jointly affect consensusability. The result is established by solving a discrete-time simultaneous stabilization problem. A lower bound of the optimal convergence rate to consensus, which is shown to be tight for some special cases, is provided as well. Moreover, a necessary and sufficient condition for formationability of multi-agent systems is obtained. As a special case, the discrete-time second-order consensus is discussed where an optimal control gain is designed to achieve the fastest convergence. The effects of undirected graphs on consensability/formationability and optimal convergence rate are exactly quantified by the ratio of the second smallest to the largest eigenvalues of the graph Laplacian matrix. An extension to directed graphs is also made. The consensus problem under a finite communication data rate is finally investigated.
Evaluation of the NanoCHIP® Gastrointestinal Panel (GIP) Test for Simultaneous Detection of Parasitic and Bacterial Enteric Pathogens in Fecal Specimens

Infectious gastroenteritis is a global health problem associated with high morbidity and mortality rates. Rapid and accurate diagnosis is crucial to allow appropriate and timely treatment. Current laboratory stool testing has a long turnaround time (TAT) and demands highly qualified personnel and multiple techniques. The need for high throughput and the number of possible enteric pathogens compels the implementation of a molecular approach which uses multiplex technology, without compromising performance requirements. In this work we evaluated the feasibility of the NanoCHIP® Gastrointestinal Panel (GIP) (Savyon Diagnostics, Ashdod, IL), a molecular microarray-based screening test, to be used in the routine workflow of our laboratory, a big outpatient microbiology laboratory. The NanoCHIP® GIP test provides simultaneous detection of nine major enteric bacteria and parasites: Campylobacter spp. , Salmonella spp. , Shigella spp. , Giardia sp. , Cryptosporidium spp. , Entamoeba histolytica, Entamoeba dispar, Dientamoeba fragilis, and Blastocystis spp. The required high-throughput was obtained by the NanoCHIP® detection system together with the MagNA Pure 96 DNA purification system (Roche Diagnostics Ltd., Switzerland). This combined system has demonstrated a higher sensitivity and detection yield compared to the conventional methods in both, retrospective and prospective samples. The identification of multiple parasites and bacteria in a single test also enabled increased efficiency of detecting mixed infections, as well as reduced hands-on time and work load. In conclusion, the combination of these two automated systems is a proper response to the laboratory needs in terms of improving laboratory workflow, turn-around-time, minimizing human errors and can be efficiently integrated in the routine work of the laboratory.
Learning to name faces: a multimodal learning scheme for search-based face annotation

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, this paper proposes a novel framework of Learning to Name Faces (L2NF) -- a unified multimodal learning approach for search-based face annotation, which consists of the following major components: (i) we enhance the weak labels of top-ranked similar images by exploiting the "label smoothness" assumption; (ii) we construct the multimodal representations of a facial image by extracting different types of features; (iii) we optimize the distance measure for each type of features using distance metric learning techniques; and finally (iv) we learn the optimal combination of multiple modalities for annotation through a learning to rank scheme. We conduct a set of extensive empirical studies on two real-world facial image databases, in which encouraging results show that the proposed algorithms significantly boost the naming accuracy of search-based face annotation task.
Characterization of a disulphide‐bound Pir‐cell wall protein (Pir‐CWP) of Yarrowia lipolytica

In this work we have studied the disulphide‐bound group of cell wall mannoproteins of Yarrowia lipolytica and Candida albicans. In the case of Y. lipolytica, SDS–PAGE analysis of the β‐mercaptoethanol‐extracted material from the purified cell walls of the yeast form, showed the presence of a main polypeptide of 45 kDa and some minor bands in the 100–200 kDa range. This pattern of bands is similar to that obtained in identical extracts in Saccharomyces cerevisiae (Moukadiri et al., 1999 ), and besides, all these bands cross‐react with an antibody raised against β‐mercaptoethanol‐extracted material from the purified cell walls of S. cerevisiae, suggesting that the 45 kDa band could be the homologue of Pir4 of S. cerevisiae in Y. lipolytica. To confirm this possibility, the amino‐terminal sequences of two internal regions of the 45 kDa protein were determined, and degenerate oligonucleotides were used to clone the gene. The gene isolated in this way codes a 286 amino acid polypeptide that shows homology with the Pir family of proteins of S. cerevisiae (Russo et al., 1992 ; Toh‐e et al., 1993 ), accordingly we have named this gene YlPIR1. Disruption of YlPIR1 led to a slight increase in the resistance of the cells to calcofluor white, Congo red and zymolyase, but did not cause changes in cell morphology, growth rate or morphological transition. Copyright © 2003 John Wiley & Sons, Ltd.
Androgenic and Estrogenic Response of Green Mussel Extracts from Singapore’s Coastal Environment Using a Human Cell-Based Bioassay

In the last decade, evidence of endocrine disruption in biota exposed to environmental pollutants has raised serious concern. Human cell-based bioassays have been developed to evaluate induced androgenic and estrogenic activities of chemical compounds. However, bioassays have been sparsely applied to environmental samples. In this study we present data on sex hormone activities in the green mussel, Perna viridis, in Singapore’s coastal waters. P. viridis is a common bioindicator of marine contamination, and this study is a follow-up to an earlier investigation that reported the presence of sex hormone activities in seawater samples from Singapore’s coastal environment. Specimens were collected from eight locations around the Singapore coastline and analyzed for persistent organic pollutants (POPs) and heavy metals. Tissue extracts were then screened for activities on androgen receptors (ARs) and estrogen receptors (ER-α and ER-β) using a reporter gene bio-assay based on a HeLa human cell line. Mussel extracts alone did not exhibit AR activity, but in the presence of the reference androgenic hormone dihydrotestosterone (DHT), activities were up to 340% higher than those observed for DHT alone. Peak activities were observed in locations adjacent to industrial and shipping activities. Estrogenic activities of the mussel extract both alone and in the presence of reference hormone were positive. Correlations were statistically investigated between sex hormone activities, levels of pollutants in the mussel tissues, and various biological parameters (specimen size, sex ratio, lipid and moisture content). Significant correlations exist between AR activities, in the presence of DHT, and total concentration of POPs (r = 0.725, p < 0.05).
An Experimental Study of Phase Transitions in Matching

Finding models of a predicate logic formula is a well-known hard problem, whose complexity is exponential in the number of variables. However, even though this number is kept constant, substantial differences in complexity arise when searching for solutions in different problem instances. Such a behavior appears to be quite general, according to recent results reported in the literature; in fact, several classes of hard problems exhibit a narrow phase transition with respect to some order parameter, in correspondence of which the complexity dramatically rises up, still remaining tractable elsewhere. In this paper we provide an extensive experimental study on the emergence of a phase transition in the problem of matching a Horn clause to a universe, searching for a model of the clause or for a proof that no such model exists. As it turns out, phase transition in the matching problem depends in an essential way on two order parameters, one capturing syntactic aspects of the clause structure (intensional aspect), while the other related to the structure of the universe (extensional aspect).
Crown Segmentation From Computed Tomography Images With Metal Artifacts

Tooth segmentation from dental computed tomography (CT) images with metal artifacts is challenging as metal artifacts make some of the crown boundaries unrecognizable. This letter proposes a semiautomatic method for crown segmentation from CT images with metal artifacts. A user manually selects a starting slice and initializes this slice. Then crown contours are segmented automatically from volumetric CT images slice by slice. In the segmentation of each slice, the Radon transform is used to extract a line to separate neighboring crowns into independent ones. A statistical shape prior-based level set model is then applied to segment each crown from the mesial or distal side of the line. The proposed method was tested on 15 set of volumetric images. Experimental results validated that it is effective to extract crown contours from CT images with metal artifacts.
Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection.

BACKGROUND Staging hepatic fibrosis by liver biopsy guides prognosis and treatment of hepatitis C, but is invasive and expensive. We sought to create an algorithm of serum markers that accurately and reliably predict liver fibrosis stage among hepatitis C patients. METHODS Ten biochemical markers were measured at time of liver biopsy in 117 untreated hepatitis C patients (training set). Multivariate logistic regression and ROC curve analyses were used to create a predictive model for significant fibrosis (METAVIR F2, F3, and F4), advanced fibrosis (F3 and F4), and cirrhosis (F4). The model was validated in 104 patients from other institutions. RESULTS A model (Hepascore) of bilirubin, gamma-glutamyltransferase, hyaluronic acid, alpha(2)-macroglobulin, age, and sex produced areas under the ROC curves (AUCs) of 0.85, 0.96, and 0.94 for significant fibrosis, advanced fibrosis, and cirrhosis, respectively. In the training set, a score > or = 0.5 (range, 0.0-1.0) was 92% specific and 67% sensitive for significant fibrosis, a score <0.5 was 81% specific and 95% sensitive for advanced fibrosis, and a score <0.84 was 84% specific and 71% sensitive for cirrhosis. Among the validation set, the AUC for significant fibrosis, advanced fibrosis, and cirrhosis were 0.82, 0.90, and 0.89, respectively. A score > or = 0.5 provided a specificity and sensitivity of 89% and 63% for significant fibrosis, whereas scores <0.5 had 74% specificity and 88% sensitivity for advanced fibrosis. CONCLUSIONS A model of 4 serum markers plus age and sex provides clinically useful information regarding different fibrosis stages among hepatitis C patients.
MhRep: Multi-hop Replication Scheme for Data Survival in Unattended Wireless Sensor Networks

Unattended Wireless Sensors Networks (UWSNs) are emerging Wireless Sensor Networks (WSNs) characterized by periodic absence of trusted entities, like the Sink Collectors. The time periods with no sink attendance, force the network sensors to locally store collected data until the next sink visit. This means that data collection is not performed in real time, but it falls at regular intervals. Such conditions define a different paradigm regarding traditional WSNs and introduce several new security issues, data survival above all. At the best of our knowledge, only two strategies were proposed to mitigate mobile adversary data deletion attack : encryption protocols (implementing backward and/or forward secrecy) and replication. While encryption protocols can impose an extensive computational effort and experience several security issues (like key exposure), replication schemes seem to capture a renewed interest from researchers, since their power requirement seem compatible with the constraints of wireless sensors. In this paper we focus on a new replication scheme and compare its performances with some other proposals, previously presented in literature. The schemes share some model assumptions, in terms of network and adversary attack strategy and objectives.
Historical Trends of Articulation in America: A Review of the Literature

Articulation agreements have been part of the American educational scene for well over 100 years. Initially implemented as a tool for more effectively and efficiently delivering liberal educations, they became more common as the numbers of community colleges grew during the 1960s and 1970s. During the mid-1980s, the universal education promise of open enrollment was seen by many as a way of “dead-ending” minority students. This perception attracted much scholarly attention and foundation funding. It also resulted in many efforts to improve minority transfer, including negotiated articulation agreements. The past decade has seen higher education expenses increase more rapidly than other state expenses. Articulation agreements are attractive during such periods because of their cost savings potential. Scholars and administrators involved in developing these agreements can prepare themselves for discussions and negotiations by understanding some of the reasons why they have varied in popularity and utility. This literature review analyzed the sociocultural and historical contexts affecting articulation. It also sought answers to research questions addressing the chronological progression and prevalent trends in the development of the processes of articulation, the relationship these trends have to the historical and regional variations in support for articulation agreements, and the levels of student success in articulated programs of study.
Scientific Storytelling Using Visualization

Scientists frequently tell stories using visualizations of scientific data, in the process of disseminating findings to peers and the general public. However, techniques and methods for effective scientific storytelling have received little attention so far. This article explores how literary and theatrical narrative conventions can inform the design and presentation of visualizations, and discusses the challenges of adapting scientific visualizations for broader audiences. It also summarizes recent workshops' findings on the role of storytelling in visualizations, and presents several examples of successful scientific-storytelling production teams. The conclusion is that scientific storytelling deserves greater support and recognition by the visualization community.
Humanized Monoclonal Antibodies Derived from Chimpanzee Fabs Protect against Japanese Encephalitis Virus In Vitro and In Vivo

ABSTRACT Japanese encephalitis virus (JEV)-specific Fab antibodies were recovered by repertoire cloning from chimpanzees initially immunized with inactivated JE-VAX and then boosted with attenuated JEV SA14-14-2. From a panel of 11 Fabs recovered by different panning strategies, three highly potent neutralizing antibodies, termed Fabs A3, B2, and E3, which recognized spatially separated regions on the virion, were identified. These antibodies reacted with epitopes in different domains: the major determinant for Fab A3 was Lys179 (domain I), that for Fab B2 was Ile126 (domain II), and that for Fab E3 was Gly302 (domain III) in the envelope protein, suggesting that these antibodies neutralize the virus by different mechanisms. Potent neutralizing antibodies reacted with a low number of binding sites available on the virion. These three Fabs and derived humanized monoclonal antibodies (MAbs) exhibited high neutralizing activities against a broad spectrum of JEV genotype strains. Demonstration of antibody-mediated protection of JEV infection in vivo is provided using the mouse encephalitis model. MAb B2 was most potent, with a 50% protective dose (ED50) of 0.84 μg, followed by MAb A3 (ED50 of 5.8 μg) and then MAb E3 (ED50 of 24.7 μg) for a 4-week-old mouse. Administration of 200 μg/mouse of MAb B2 1 day after otherwise lethal JEV infection protected 50% of mice and significantly prolonged the average survival time compared to that of mice in the unprotected group, suggesting a therapeutic potential for use of MAb B2 in humans.
Percutaneous coronary intervention compared with coronary artery bypass graft in coronary artery disease patients with chronic kidney disease: a systematic review and meta-analysis

Abstract Previous reports of percutaneous coronary intervention versus coronary artery bypass graft outcomes in coronary artery disease patients with chronic kidney disease (CKD) were inconsistent. We evaluated the optimal revascularization strategy for CKD patients. We searched Pub Med, EMBASE, and the Cochrane Central Register of Controlled Trials and scanned the references of relevant articles and reviews. All studies that compared relevant clinical outcomes between percutaneous coronary intervention and coronary artery bypass graft in CKD patients were selected. We defined short-term and long-term all-cause mortality as primary outcome, and long-term incidences of myocardial infarction and revascularization as secondary outcomes. A total of 2235 citations were retrieved, and 31 studies involving 99,054 patients, with 55,383 receiving percutaneous coronary intervention and 43,671 receiving coronary artery bypass graft, were included. In subgroup analyses of dialysis patients receiving percutaneous coronary intervention with stents versus coronary artery bypass graft, CKD patients with multivessel coronary disease, and CKD patients receiving drug-eluting stent versus coronary artery bypass graft, the pooled outcomes revealed that percutaneous coronary intervention possessed lower short-term mortality, but higher late revascularization risk. No significant differences in long-term mortality were observed between the two strategies in these subgroup analyses. In conclusion, in some specific clinical circumstances, CKD patients receiving percutaneous coronary intervention possessed lower short-term all-cause mortality, but higher long-term revascularization risk, than coronary artery bypass graft; long-term all-cause mortality was not different between the two strategies.
Stochastic Recognition of Regular Structures in Facade Images

We present a method for recognition of structured images and demonstrate it on the detection of windows in facade images. Given an ability to obtain local low-level data evidence on primitive elements of a structure (like window in a facade image), we determine their most probable number, attribute values (location, size) and neighborhood relation. The embedded structure is weakly modeled by pair-wise attribute constraints, which allow structure and attributes to mutually support each other. We use a very general framework of reversible jump MCMC, which allows simple implementation of a specific structure model and plug-in of almost arbitrary element classifiers. We have chosen the domain of window recognition in facade images to demonstrate that the result is an efficient algorithm achieving performance of other strongly informed methods for regular structures.
Discriminative base decomposition for time-frequency matrix decomposition

Time-frequency matrix (TFM) decomposition using non-negative matrix factorization (NMF) has been recently considered as a successful tool for time-frequency (TF) quantification. In this paper, we modify the constraints of traditional cost function of NMF to make the method a better fit for TF quantification, and denote the new method with NMF discriminant base (NMFDB) decomposition. We evaluate the proposed method, and show that it successfully identifies the discriminant bases. Additionally, we measure the discrimination ability of NMFDB over the signals with very low discriminations, and compare it with the discrimination of the decomposed bases derived using traditional NMF. It is concluded that the proposed method is able to locate the region of difference with 20% better performance compared to the conventional NMF.
Modal Filtering for Optimized Surface Plasmon Resonance Sensing in Multimode Plastic Optical Fibers

In this paper, we present the numerical and the experimental results of a new low cost surface plasmon resonance sensor configuration. It is based on a plasmonic sensor platform and an efficient higher order modes filtering in plastic multimode fibers, exploiting a tapered plastic optical fiber at the output of the sensor system. The experimental results have demonstrated that the tapered filter positioned after the sensor system improves the performances in terms of refractive index range and depth of the resonance curve. The results are in agreement with the numerical simulations.