Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial responses of primary gingival cells to Porphyromonas gingivalis.

BACKGROUND Human beta-defensins (hBDs) and the C-C chemokine ligand 20 (CCL20) produced by gingival epithelial cells (GECs) and fibroblasts (HGFs) are antimicrobial peptides (AMPs) that play an important role in innate immunity. The aim of this study was to determine the differential immune response of GECs and HGFs to the oral commensal Streptococcus gordonii (SG) and the pathogen Porphyromonas gingivalis (PG). MATERIAL AND METHODS In addition to the analysis of gingival biopsies, primary GECs and HGFs were exposed to SG and/or PG, and expression of various AMPs and pro-inflammatory mediators was studied by real-time PCR and ELISA. RESULTS Gene expression of AMPs was detected in gingival connective tissue. Both SG and PG induced the mRNA-expression of hBD-2 and hBD-3 in GECs as well as HGFs after 24 h (p < 0.05). In HGFs, the commensal bacterium SG stimulated the mRNAs of hBD-3 and CCL20 after 24 h (p < 0.05), while not in GECs. In GECs, the inductive effect of PG on the mRNA-expression of hBD-2 was amplified when cells were first exposed to commensal SG (for 1 h) prior to stimulation with PG (SG-PG; p < 0.05). CONCLUSION Our data indicate that cell-bacteria interactions and/or bacteria-bacteria cross-talk may have an impact on AMP-regulation in gingiva.
Multi-stage Chinese Dependency Parsing Based on Dependency Direction

This paper presents a novel method for multistage dependency parsing based on dependency direction. In the method, dependency parsing processes are divided into multiple sub-stages, and each stage is in a sequential pattern, which makes it easier to take applicable solutions for different issues in dependency parsing. Meanwhile, dependency parsing in the previous stage provides a clearer context for next stage. Furthermore, due to the dependency direction, the proposed method has lower search complexity than that of classic graph-based methods. Experimental results show that compared with common methods, the proposed method in this paper offers comparable accuracy and higher efficiency.
Spanish adaptation of the Green Paranoid Thought Scales.

BACKGROUND The aim of this study was to adapt and obtain validity evidence of the Spanish Green Paranoid Thought Scales (S-GPTS). METHOD 191 Spanish people responded to S-GPTS, Peters Delusions Inventory (PDI), and measures of psychopathology. RESULTS Principal Component Analyses on the polychoric correlation matrix identified two factors accounting for 71.0% of the cumulative variance. Cronbach alphas for S-GPTS total and its subscales were above . 90 in clinical and non-clinical group. The value of the area under the receiver operating characteristic curve was higher for the S-GPTS (.898), than for the PDI (.859). The best S-GPTS threshold to discriminate between cases and non-cases was 92 (sensitivity, 97.35%; specificity, 65%). S-GPTS scores positively correlated with PDI and measures of anxiety and depression. CONCLUSION The S-GPTS has adequate psychometric properties to provide valid measures of delusional ideation in a Spanish population.
Region-of-interest based error resilient method for HEVC video transmission

In this paper, we propose a region-of-interest (ROI) based error resilient algorithm for High Efficiency Video Coding (HEVC) video transmission applications. We use moving region (MR) as ROI so that the proposed method can address a wide variety of different scenarios. Based on the MR information, the depth level of each coding unit (CU) is determined. This can reduce the computation complexity of encoder significantly. A feedback channel from decoder is assumed in our system. According to feedback information and MR information, coding mode for each CU is selected. In addition, current rate control algorithm of HEVC is modified so that it can handle CU-level intra refresh. Experimental results demonstrate that the proposed method can achieve better subjective and objective quality, compared to the conventional reference picture selection (RPS) algorithm which is an effective error resilient tool for low delay application.
Influence of Formulation and Process Variables on the Formation of Rifampicin Nanoparticles by Ionic Gelation Technique

Chitosan, a natural biodegradable polymer is recommended for development of nanoparticles intended for chronic infection such as tuberculosis. Formulation and processing variables play a critical role in the formation of nanoparticles prepared with chitosan. The present study investigated the influence of polymer (chitosan) molecular weight, stabilizing agent (tween 80) concentration and stirring speed on rifampicin nanoparticles prepared by ionic gelation of chitosan with sodium tripolyphosphate. FTIR and DSC analysis revealed no chemical interaction between rifampicin and chitosan. DSC thermogram showed amorphous formation of the drug and polymer.Chitosan molecular weight, tween 80 concentration and stirring speed did not affect morphology of nanoparticles; however influenced the particle size, zeta potential, polydispersity index, drug encapsulation and loading efficiency and release of rifampicin nanoparticles. Our findings suggest that chitosan molecular weight, tween 80 concentration and stirring speed are important factors that need to be optimized to meet the desired qualities of rifampicin loaded chitosan nanoparticles.
Difference in Susceptibility of Various Cell Cultures to Cytotoxic Culture Filtrates of Clostridium sordellii

Recently we demonstrated that the lethal toxin-producing strains of Clostridium sordellii were cytotoxigenic for baby hamster kidney cell cultures (4), which had never been used to test the cytotoxigenicity of C. sordellii (1-3, 6), and that some of the cytotoxigenic C. sordellii strains produced a positive reaction in the ligated rabbit ileal loop (7). The purpose of this study was to find cultured cell lines, other than baby ham sterkidney cells, susceptible to the cytotoxic culture filtrates of C. sordellii and those specifically susceptible to the culture filtrates of ligated ileal loop response-positive strains of C. sordellii. Two cytotoxigenic and ileal loop response-positive strains and 13 cytotoxigenic but ileal loop response-negative strains (4, 7) were used. Of these, one cytotoxigenic and ileal loop response-positive strain, 3703, and one cytotoxigenic but ileal loop response-negative strain, KZ 1047, were mainly used. Cytotoxic culture filtrates were prepared from cultures of C. sordellii strains incubated in m-BHI medium according to the method previously described (4, 7). The cell cultures tested were eight established cell lines and one primary human embryonic lung cell culture, HEL, which was prepared from a 6-month embryonic lung in our laboratory. Baby hamster kidney (BHK-21/WI-2) and human amnion (FL) cells were cultured in Eagle's minimal essential medium (Eagle's MEM) (Nissui, Tokyo) containing 5% calf serum; human embryonic intestine (Intestine 407), human embryonic lung
Fast Semantic Extraction Using a Novel Neural Network Architecture

We describe a novel neural network architecture for the problem of semantic role labeling . Many current solutions are complicated, consist of several stages and hand-built features, and are too slow to be applied as part of real applications that require such semantic labels, partly because of their use of a syntactic parser (Pradhan et al., 2004; Gildea and Jurafsky, 2002). Our method instead learns a direct mapping from source sentence to semantic tags for a given predicate without the aid of a parser or a chun-ker. Our resulting system obtains accuracies comparable to the current state-of-the-art at a fraction of the computational cost.
PCR Biases Distort Bacterial and Archaeal Community Structure in Pyrosequencing Datasets

As 16S rRNA gene targeted massively parallel sequencing has become a common tool for microbial diversity investigations, numerous advances have been made to minimize the influence of sequencing and chimeric PCR artifacts through rigorous quality control measures. However, there has been little effort towards understanding the effect of multi-template PCR biases on microbial community structure. In this study, we used three bacterial and three archaeal mock communities consisting of, respectively, 33 bacterial and 24 archaeal 16S rRNA gene sequences combined in different proportions to compare the influences of (1) sequencing depth, (2) sequencing artifacts (sequencing errors and chimeric PCR artifacts), and (3) biases in multi-template PCR, towards the interpretation of community structure in pyrosequencing datasets. We also assessed the influence of each of these three variables on α- and β-diversity metrics that rely on the number of OTUs alone (richness) and those that include both membership and the relative abundance of detected OTUs (diversity). As part of this study, we redesigned bacterial and archaeal primer sets that target the V3–V5 region of the 16S rRNA gene, along with multiplexing barcodes, to permit simultaneous sequencing of PCR products from the two domains. We conclude that the benefits of deeper sequencing efforts extend beyond greater OTU detection and result in higher precision in β-diversity analyses by reducing the variability between replicate libraries, despite the presence of more sequencing artifacts. Additionally, spurious OTUs resulting from sequencing errors have a significant impact on richness or shared-richness based α- and β-diversity metrics, whereas metrics that utilize community structure (including both richness and relative abundance of OTUs) are minimally affected by spurious OTUs. However, the greatest obstacle towards accurately evaluating community structure are the errors in estimated mean relative abundance of each detected OTU due to biases associated with multi-template PCR reactions.
Automated facial affect analysis for one-on-one tutoring applications

In this paper, we explore the use of computer vision techniques to analyze students' moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student's mood and affective state. We find that the problem of accurately determining a child's mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.
Hardware Implementation of Face Detection Using ADABOOST Algorithm

One of the main challenges of computer vision is efficiently detecting and classifying objects in an image or video sequence. Several machine learning approaches have been applied to this problem, demonstrating significant improvements in detection accuracy and speed. However, most approaches have limited object detection to a single class of objects, such as faces or pedestrians. A common benchmark for computer vision researchers is face detection. Given a set of images, a face detection algorithm determines which images have sub-windows containing faces. This task is trivial for humans, but is computationally expensive for machines. Most face detection systems simplify the face detection problem by constraining the problem to frontal views of non-rotated faces. Approaches have been demonstrated capable of relaxing these constraints, at the cost of additional computation. They utilize AdaBoost to select a set of features and train a classifier. The detector uses a cascade structure to reduce the number of features considered for each sub- window. This approach is significantly faster than previous techniques and is applicable for real-time systems. There is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object
Endurance training improves responsiveness to insulin and modulates insulin signal transduction through the phosphatidylinositol 3-kinase/Akt-1 pathway.

BACKGROUND Endurance training increases insulin-stimulated muscle glucose transport and leads to improved metabolic control in diabetic patients. OBJECTIVE To analyze the effects of endurance training on the early steps of insulin action in muscle of rats. DESIGN Male rats submitted to daily swimming for 6 weeks were compared with sedentary controls. At the end of the training period, anesthetized animals received an intravenous (i.v.) injection of insulin and had a fragment of their gastrocnemius muscle excised for the experiments. METHODS Associations between insulin receptor, insulin receptor substrates (IRS)-1 and -2 and phosphatidylinositol 3-kinase (PI3-kinase) were analyzed by immunoprecipitation and immunoblotting. Akt-1 serine phosphorylation and specific protein quantification were detected by immunoblotting of total extracts, and IRS-1/IRS-2-associated PI3-kinase activity were determined by thin-layer chromatography. RESULTS Insulin-induced phosphorylation of IRS-1 and IRS-2 increased respectively by 1.8-fold (P<0.05) and 1.5-fold (P<0.05), whereas their association with PI3-kinase increased by 2.3-fold (P<0.05) and 1.9-fold (P<0.05) in trained rats as compared with sedentary controls, respectively. The activity of PI3-kinase associated with IRS-1 and IRS-2 increased by 1.8-fold (P<0.05) and 1.7-fold (P<0.05) respectively, in trained rats as compared with their untrained counterparts. Serine phosphorylation of Akt-1/PKB increased 1.7-fold (P<0.05) in trained rats in response to insulin. These findings were accompanied by increased responsiveness to insulin as demonstrated by a reduced area under the curve for insulin during an i.v. glucose tolerance test, by increased glucose disappearance rate during an insulin tolerance test, and by increased expression of glucose transporter-4. CONCLUSIONS The increased responsiveness to insulin induced by chronic exercise in rat skeletal muscle may result, at least in part, from the modulation of the insulin signaling pathway at different molecular levels.
Development of architecture and software technologies in high-performance low-power SoC design

For the success of an SoC design, a design platform and some key design technologies are needed. For this purpose, a research project in "Technology Development Program for Academia" was granted recently in Taiwan to develop the following technologies toward a high-performance low-power SoC design platform. First, a soft intellectual property (soft IP) and related RTOS, compiler, and integrated design environment (IDE) software of an Advanced Taiwan VLTW DSP Processor Core, which can be used as the Star IP of an SoC design platform, will be developed. Second, low-power and low-voltage digital and mixed-signal circuit design technologies will be developed based on the advanced MTCMOS process. Third, some key multimedia and communication soft or hard IPs will be developed. The developed advanced key technologies can be used as the technology driver to facilitate the design of SoC-based products, and they will also help to enhance the design capability of the Taiwan SoC industry. In this paper, we introduce the research project and focus on architecture and software technologies developing in one of the subitems.
User Authentication with Keystroke Dynamics Using Fixed Text

User authentication is an important problem for any restricted access computer system, especially with remote access. Constant spread of Internet makes efficient remote authentication a pressing issue while the distributed nature of interaction introduces additional difficulties. Authentication methods based on most biometrics techniques require dedicated hardware that is unhandy for remote applications. In contrast keystroke dynamics requires only limited dedicated software and does not require any dedicated hardware. The approach we propose is aimed at efficient user authentication with keystroke dynamics using short fixed text, that may for example occur simultaneously to logging process. We have tested the suggested approach on a large group of individuals, with data gathered over Internet using browser-based WWW application. The obtained results are promising and encourage further development in this area.
Catching the PHEVer: Simulating Electric Vehicle Diffusion with an Agent-Based Mixed Logit Model of Vehicle Choice

This research develops then merges two separate models to simulate electric vehicle diffusion through recreation of the Boston metropolitan statistical area vehicle market place. The first model is a mixed (random parameters) logistic regression applied to data from the US Department of Transportation's 2009 National Household Travel Survey. The second, agent-based model simulates social network interactions through which agents' vehicle choice sets are endogenously determined. Parameters from the first model are applied to the choice sets determined in the second. Results indicate that electric vehicles as a percentages of vehicle stock range from 1% to 22% in the Boston metropolitan statistical area in the year 2030, percentages being highly dependent on scenario specifications. A lower price is the main source of competitive advantage for vehicles but other characteristics, such as vehicle classification and range, are demonstrated to influence consumer choice. Government financial incentive availability leads to greater market shares in the beginning years and helps to spread diffusion in later years due to an increased base of initial adopters. Although seen as a potential hindrance to EV diffusion, battery cost scenarios have relatively small impacts on EV diffusion in comparison to policy, range, miles per gallon (MPG), and vehicle miles travelled (VMT) as a percentage of range assumptions. Pessimistic range assumptions decrease overall PHEV and BEV percentages of vehicle stock by 50% and 30%, respectively, relative to the EPA-estimated range scenarios. Fuel cost scenarios do not considerably alter estimated BEV and PHEV stock but increase the ratio of car stock to light truck stock in the internal combustion engine (ICE) vehicle spectrum. Specifically, cars are estimated at 55% of ICE vehicle stock in the default fuel price scenario but increase to 62% of ICE vehicle stock in the high world oil price scenario, with LTs covering the appropriate differences.
A directed cycle‐based column‐and‐cut generation method for capacitated survivable network design

A network is said to be survivable if it has sufficient capacity for rerouting all of its flow under the failure of any one of its edges. Here, we present a polyhedral approach for designing survivable networks. We describe a mixed‐integer programming model, in which sufficient slack is explicitly introduced on the directed cycles of the network while flow routing decisions are made. In case of a failure, flow is rerouted along the slacks reserved on directed cycles. We give strong valid inequalities that use the survivability requirements. We present a computational study with a column‐and‐cut generation algorithm for designing capacitated survivable networks. © 2004 Wiley Periodicals, Inc.
EFW: A cross-layer metric for reliable routing in wireless mesh networks with selfish participants

Wireless mesh networks (WMNs) have emerged as a flexible and low-cost network infrastructure, where heterogeneous mesh routers managed by different users collaborate to extend network coverage. Several routing protocols have been proposed to improve the packet delivery rate based on enhanced metrics that capture the wireless link quality. However, these metrics do not take into account that some participants can exhibit selfish behavior by selectively dropping packets sent by other mesh routers in order to prioritize their own traffic and increase their network utilization.
Granules of Words from Fuzzy Relations and Spectral Clustering

Conventional information retrieval systems have proven ineffective in dealing with information overload. One possible solution is to incorporate some features that allow users of these systems to custom handle this information. In order to enable systems of this kind, some of the characteristics of present-day systems should be reviewed. Among other features, all documents are described with the same level of detail. We believe that the redrafting of document models is the starting point for reform of these systems. The paradigm of granular computing has proven to be very suitable for the treatment of complex problems and can produce significant results in large-scale environments such as the Web. This paper explores the granulation process of words with a view to its application in the subsequent improvement in document representation. We use fuzzy relations and spectral clustering in this process and present some result.
Inventories and Interventions: (Re)figuring the Archive

The project that spawned this collection of essays, Refiguring the Archive, edited by Carolyn Hamilton, Verne Harris, Jane Taylor, Michelle Pickover, Graeme Reid, and Razia Saleh, emerged from a 1998 multidisciplinary seminar series at the University of the Witwatersrand. The objective of refiguring the archive was a response to the way that apartheid had created and shaped the production of knowledge, if one views archives as "legal reposito ries of knowledge" (93). The South African regime's intervention included a massive destruction of material from 1990 to 1994 "in an attempt to keep the apartheid state's darkest secrets hidden" (135). So, the book under review, with many revised versions of papers from the series, concentrates on challenging the real, exist ing archives of South Africa by either transforming them or by producing or figuring for the first time new archives. In this work, the authors go beyond the archive of the documents, revered and utilized by many as the essence of knowledge, and move into photographs, literature, and art. In its presentation and form, Refiguring the Archive is a post modern adventure; it has sidenotes rather than footnotes or endnotes, illustrations galore, verbatim lectures and interview transcripts, fractured texts where various separate but distinct essays run alongside each other (intertextuality the editors call it), multicolored pages, and (most unfortunately) a minimalist index. The essays are meant to be held together by Jacques Derrida's
Increased amygdala activation during mania: a functional magnetic resonance imaging study.

OBJECTIVE This study sought to investigate neural activity in the amygdala during episodes of mania. METHOD Nine manic subjects and nine healthy comparison subjects underwent functional magnetic resonance imaging (fMRI) while performing a neuropsychological paradigm known to activate the amygdala. Subjects viewed faces displaying affect (experimental task) and geometric forms (control task) and matched them to one of two simultaneously presented similar images. RESULTS Manic subjects had significantly increased activation in the left amygdala and reduced bilateral activation in the lateral orbitofrontal cortex relative to the comparison subjects. CONCLUSIONS Increased activation in the amygdala and decreased activation in the orbitofrontal cortex may represent disruption of a specific neuroanatomic circuit involved in mania. These brain regions may be implicated in disorders involving regulation of affect.