Miracle-dz/UniFAR-FTUs huggingface dataset:
Twenty Years of Full-Mouth Disinfection: The Past, the Present and the Future

Background: Conventional periodontal treatment, performed quadrant by quadrant in multiple visits, was re-evaluated in the early 1990s when the full-mouth disinfection concept was introduced. Over the years, several modifications to the full-mouth disinfection approach have been suggested. Objective: The purpose of this article is to review the evolution of full-mouth disinfection during the past 20 years, to specify its indications and to consider the prospects for this approach. Materials and Methods: An electronic and manual search of the literature, ending in December 2016, was performed by two independent researchers. Only pivotal studies and randomized controlled clinical trials published in the English language that evaluated a new approach to full-mouth disinfection were selected. Results: According to the studies included in our analysis (21 articles), several modified full-mouth disinfection protocols have been designed including: full-mouth treatment without chlorhexidine, the extension of hygiene methods and an increase in the duration of post-treatment chlorhexidine use, the replacement of chlorhexidine with other antiseptics, supplementation with antibiotics or probiotics, full-mouth antimicrobial photodynamic therapy and one-stage full-mouth disinfection combined with a periodontal dressing. Conclusion: Since 1995, several modifications have been suggested to improve the effectiveness of full-mouth disinfection. The majority of the studies demonstrate that the results obtained with full-mouth disinfection and its variants are equivalent to each other and to those obtained with the conventional quadrant method. Currently, the selection of this technique remains empirical and depends on the preferences of the practitioner and the patient. In the future, a patient-centered approach should be the best indication for the use of this technique.
An unsupervised framework for detecting anomalous messages from syslog log files

System logs provide valuable information about the health status of IT systems and computer networks. Therefore, log file monitoring has been identified as an important system and network management technique. While many solutions have been developed for monitoring known log messages, the detection of previously unknown error conditions has remained a difficult problem. In this paper, we present a novel data mining based framework for detecting anomalous log messages from syslog- based system log files. We also describe the implementation and performance of the framework in a large organizational network.
How outcome dependencies affect decisions under risk

Many economic theories of decision making assume that people evaluate options independently of other available options. However, recent cognitive theories such as decision field theory suggest that people’s evaluations rely on a relative comparison of the options’ potential consequences such that the subjective value of an option critically depends on the context in which it is presented. To test this prediction, we examined pairwise choices between monetary gambles and varied the degree to which the gambles’ outcomes covered with one another. When people evaluate options by comparing their outcomes, a high covariance between these outcomes should make a decision easier, as suggested by decision field theory. In line with this prediction, the observed choice proportions in 2 experiments (N = 39 and 24, respectively) depended on the magnitude of the covariance. We call this effect the covariance effect. Our findings are in line with the theoretic predictions and show that the discriminability ratio in decision field theory can reflect the choice difficulty. These results confirm that interdependent evaluations of options play an important role in human decision making under risk and show that covariance is an important aspect of the choice context.
Evaluation of Scheimpflug Tomography Parameters in Subclinical Keratoconus, Clinical Keratoconus and Normal Caucasian Eyes

Objectives: To evaluate tomographic and topographic parameters in subclinical and clinical keratoconus eyes by comparing them with normal eyes in a young Caucasian population. Materials and Methods: This cross-sectional study included 88 normal eyes (control group), bilateral data from the preclinical stage of 24 progressive keratoconus eyes (bilateral subclinical keratoconus group), 40 fellow eyes of patients with unilateral keratoconus (fellow eyes group) and 97 eyes with mild keratoconus (clinical keratoconus group). Topographic and tomographic data, data from enhanced elevation maps and keratoconus indices were measured in all study eyes using Scheimpflug tomography. Receiver operating characteristic (ROC) curve analysis was used to assess individual parameters to discriminate eyes of patients with subclinical and clinical keratoconus from control eyes. The sensitivity and specificity of the main effective parameters were evaluated and optimal cut-off points were identified to differentiate subclinical keratoconus and keratoconus from normal corneas. Results: Comparison of all subclinical and clinical keratoconus eyes from the normal group revealed significant differences in most diagnostic parameters. The ROC curve analysis showed high overall predictive accuracy of several Pentacam parameters (overall D value, anterior and posterior elevations and difference elevations, pachymetry progression index, index of surface variance, index of height decentration and keratoconus index) in discriminating ectatic corneas from normal ones. These outcomes were proportionally less pronounced in all subclinical keratoconus eyes than in the clinical keratoconus eyes. Pachymetric readings were progressively lower in the bilateral subclinical keratoconus eyes and sensitivity and specificity of the analyzed tomographic and topographic parameters were higher than the fellow eyes group when differentiating subclinical keratoconus from healthy corneas. Conclusion: Scheimpflug tomography parameters such as D value, elevation parameters, progression index and several surface indices can effectively differentiate keratoconus from normal corneas in a Caucasian population. Nevertheless, a combination of different data is required to distinguish subclinical keratoconus.
The emergence of trophoblast cell-surface antigen 2 (TROP-2) as a novel cancer target

TROP-2 is a glycoprotein first described as a surface marker of trophoblast cells, but subsequently shown to be increased in many solid cancers, with lower expression in certain normal tissues. It regulates cancer growth, invasion and spread by several signaling pathways, and has a role in stem cell biology and other diseases. This review summarizes TROP-2's properties, especially in cancer, and particularly its role as a target for antibody-drug conjugates (ADC) or immunotherapy. When the irinotecan metabolite, SN-38, is conjugated to a humanized anti-TROP-2 antibody (sacituzumab govitecan), it shows potent broad anticancer activity in human cancer xenografts and in patients with advanced triple-negative breast, non-small cell and small-cell lung, as well as urothelial cancers.
Hierarchical Cloud Computing Architecture for Context-Aware IoT Services

This paper presents a new cloud computing model for context-aware Internet of Things services. The proposed computing model is hierarchically composed of two layers: a cloud control layer (CCL) and a user control layer (UCL). The CCL manages cloud resource allocation, service scheduling, service profile, and service adaptation policy from a system performance point of view. Meanwhile, the UCL manages end-to-end service connection and service context from a user performance point of view. The proposed model can support nonuniform service binding and its real-time adaptation using meta-objects. Furthermore, it supports intelligent service-context management using a supervised and reinforcement learning-based machine learning framework. We implemented a lightweight prototype of the proposed computing model. Evaluations confirm that the proposed computing model offers enhanced performance compared with legacy uniform computing models.
Suppression of photo-oxidation of organic chromophores by strong coupling to plasmonic nanoantennas

Strong coupling of organic chromophores to plasmonic nanoparticles was shown to markedly improve molecular photostability. Intermixed light-matter quasi-particles—polaritons—have unique optical properties owing to their compositional nature. These intriguing hybrid states have been extensively studied over the past decades in a wide range of realizations aiming at both basic science and emerging applications. However, recently, it has been demonstrated that not only optical but also material-related properties, such as chemical reactivity and charge transport, may be significantly altered in the strong coupling regime of light-matter interactions. We show that a nanoscale system, composed of a plasmonic nanoprism strongly coupled to excitons in a J-aggregated form of organic chromophores, experiences modified excited-state dynamics and, therefore, modified photochemical reactivity. Our experimental results reveal that photobleaching, one of the most fundamental photochemical reactions, can be effectively controlled and suppressed by the degree of plasmon-exciton coupling and detuning. In particular, we observe a 100-fold stabilization of organic dyes for the red-detuned nanoparticles. Our findings contribute to understanding of photochemical properties in the strong coupling regime and may find important implications for the performance and improved stability of optical devices incorporating organic dyes.
Spatio-Temporal Instance Learning: Action Tubes from Class Supervision

The goal of this work is spatio-temporal action localization in videos, using only the supervision from video-level class labels. The state-of-the-art casts this weakly-supervised action localization regime as a Multiple Instance Learning problem, where instances are a priori computed spatio-temporal proposals. Rather than disconnecting the spatio-temporal learning from the training, we propose Spatio-Temporal Instance Learning, which enables action localization directly from box proposals in video frames. We outline the assumptions of our model and propose a max-margin objective and optimization with latent variables that enable spatio-temporal learning of actions from video labels. We also provide an efficient linking algorithm and two reranking strategies to facilitate and further improve the action localization. Experimental evaluation on four action datasets demonstrate the effectiveness of our approach for localization from weak supervision. Moreover, we show how to incorporate other supervision levels and mixtures, as a step towards determining optimal supervision strategies for action localization.
Layered Double Hydroxide Nanomaterials Encapsulating Angelica gigas Nakai Extract for Potential Anticancer Nanomedicine

We prepared hybrids consisting of Angelica gigas Nakai (AGN) root or flower extract and layered double hydroxide (LDH) for potential anticancer nanomedicine, as decursin species (DS) in AGN are known to have anticancer activity. Dimethylsulfoxide solvent was determined hybridization reaction media, as it has affinity to both AGN and LDH moiety. In order to develop inter-particle spaces in LDH, a reversible dehydration-rehydration, so-called reconstruction route, was applied in AGN-LDH hybridization. Quantitative analyses on AGN-LDH hybrids indicated that the content of DS was two times more concentrated in the hybrids than in extract itself. Using X-ray diffraction, FT-IR spectroscopy, scanning electron microscopy, and zeta-potential measurement, we found that AGN extract moiety was incorporated into inter-particle spaces of LDH nanoparticles during the reconstruction reaction. Time-dependent DS release from hybrids at pH 7.4 (physiological condition) and pH 4.5 (lysosomal condition) exhibited a pH-dependent release of extract-incorporated LDH hybrids. An anticancer activity test using HeLa, A549, and HEK293T cells showed that the AGN-LDH hybrid, regardless of extract type, showed enhanced anticancer activity compared with extract alone at an equivalent amount of DS, suggesting a nanomedicine effect of AGN-LDH hybrids.
Impact of the Variable Killer Ig-Like Receptor–Human Leukocyte Antigen Interactions on Natural Killer Cell Cytotoxicity Toward Foreign CD4 T Cells

Background Natural killer (NK) cells are known to mount a response against foreign target cells, where the absence of the dominant inhibitory killer Ig-like receptor (KIR)–human leukocyte antigen (HLA) interaction immensely lowers the threshold for NK cell activation. NK cells could thus constitute a vital part in the mucosal defense against cell-associated sexually transmitted diseases. Here, we performed a detailed analysis of hitherto unexplored KIR–HLA-incompatible NK cell interactions. Methods and findings In vitro, healthy NK cells were cocultured with CD4+ T cells derived from human immunodeficiency virus-1 patients, and the KIR-specific NK cell cytotoxicity was measured using flow cytometry. Genotyping of KIR and HLA predicted the KIR–HLA interactions occurring during these 124 allogeneic encounters. KIR2DL1+ NK cells were seen as the strongest intrinsic responders in the absence of their ligand with a 3.2-fold increase in KIR2DL1+ NK cells in the total NK cell response. An association between the size of the alloreactive NK cell population and the amount of CD4+ T cell death (p = 0.0023) and NK cell degranulation (p = 0.0036) was only present in NK cell donors with an activating KIR haplotype. Conclusion We demonstrate differences in the activating effect of KIR–HLA incompatibility according to the KIR involved, with KIR2DL1 as the strongest responder. An activating KIR haplotype optimized the contribution of KIR–HLA-incompatible NK cells in the total NK cell response.
A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host

Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.
Recurrent Neural Networks for Enhancement of Signature-based Network Intrusion Detection Systems

Security of information passing through the Internet is threatened by today's most advanced malware ranging from orchestrated botnets to much simpler polymorphic worms. These threads, as examples of zero-day attacks, are able to change their behavior several times at the early phases of their existence to bypass the network intrusion detection systems (NIDS). It is known that even well- designed, and frequently-updated signature-based NIDS cannot detect the zero-day treats due to the lack of an adequate signature database, adaptive to intelligent attacks on the Internet. On the other hand, applying traditional machine learning methods could not narrow this gap. More importantly, having an NIDS, it should be tested on malicious traffic dataset that not only represents known attacks, but also can to some extent reflect the characteristics of unknown, zero-day attacks. Generating such traffic is identified in the literature as one of the main obstacles for evaluating the effectiveness of NIDS. To address these issues, we apply Recurrent Neural Networks (RNNs) known as powerful tools in finding complex patterns and generating similar ones. In this regard, we first examine whether it is possible to generate new, unseen mutants of a polymorphic worm. Our results demonstrate that our synthetic mutants exhibit the same characteristics as the original mutants, i.e., known mutants fed into the RNN. Besides, we assess the ability of RNNs to generate synthetic signatures from the most advanced malware. We claim that by adding the RNN-generated, synthetic signatures to the set of the signatures of a signature-based NIDS it is possible to improve the malware detection rate of that. To support this and evaluate the feasibility of our approach, we conduct extensive experiments and provide exhaustive discussion on our experimental results.
Trypanosoma cruzi Infection Induces Cellular Stress Response and Senescence-Like Phenotype in Murine Fibroblasts

Trypanosoma cruzi infects and replicates within a wide variety of immune and non-immune cells. Here, we investigated early cellular responses induced in NIH-3T3 fibroblasts upon infection with trypomastigote forms of T. cruzi. We show that fibroblasts were susceptible to T. cruzi infection and started to release trypomastigotes to the culture medium after 4 days of infection. Also, we found that T. cruzi infection reduced the number of fibroblasts in 3-day cell cultures, by altering fibroblast proliferation. Infected fibroblasts displayed distinctive phenotypic alterations, including enlarged and flattened morphology with a nuclei accumulation of senescence-associated heterochromatin foci. In addition, infection induced an overexpression of the enzyme senescence-associated β-galactosidase (SA-β-gal), an activation marker of the cellular senescence program, as well as the production of cytokines and chemokines involved with the senescence-associated secretory phenotype (SASP) such as IL-6, TNF-α, IL-1β, and MCP-1. Infected fibroblasts released increased amounts of stress-associated factors nitric oxide (NO) and reactive oxygen species (ROS), and the treatment with antioxidants deferoxamine (DFO) and N-acetylcysteine reduced ROS generation, secretion of SASP-related cytokine IL-6, SA-β-gal activity, and parasite load by infected fibroblasts. Taken together, our data suggest that T. cruzi infection triggers a rapid cellular stress response followed by induction of a senescent-like phenotype in NIH-3T3 fibroblasts, enabling them to act as reservoirs of parasites during the early stages of the Chagas disease.
Stochastic stability of linear systems with semi-Markovian jump parameters

Abstract Over the past two decades considerable effort has been devoted to problems of stochastic stability, stabilisation, filtering and control for linear and nonlinear systems with Markovian jump parameters, and a number of results have been achieved. However, due to the exponential distribution of the Markovian chain, there are many restrictions on existing results for practical applications. In the present paper, we study systems whose jump parameters are semi-Markovian rather than fully Markovian. We consider only linear systems with semi-Markovian jump parameters, and also study systems with phase-type semi-Markovian jump parameters, because the family of phase-type distributions is dense in the families of all probability distributions on [0, +∞). Some stochastic stability results are obtained. An example is given to show the potential of the proposed techniques.
People counting and pedestrian flow statistics based on convolutional neural network and recurrent neural network

People counting and pedestrian flow statistics are challenging tasks because of the perspective distortions, appearance changes and occlusion. In this paper, we address the two tasks: people counting in images of highly dense crowds and pedestrian flow statistics in a place over a period of time. Our first contribution is to propose a new convolution neural network (CNN) model which is composed of a deep and shallow fully convolution network to fulfill the task of people counting. We extract different layer features from the deep fully convolution network and the last layer features from the shallow fully convolution network, and concatenate them together. After that we add two deconvolution layers to make the output image have the same resolution with the input image. Our second contribution is to combine pedestrian flow statistics task with people counting task. According to the density maps that CNN model generates, we can calculate the number of people crossing a place based on the recurrent neural network (RNN). Besides, we also have collected two datasets and labelled them. Extensive experiments have been implemented, our people counting method outperforms other existing methods, and our pedestrian flow statistics method combined with CNN model also outperforms the model which only uses long-short term memory (LSTM).
Automating the Production of Communicative Gestures in Embodied Characters

In this paper we highlight the different challenges in modeling communicative gestures for Embodied Conversational Agents (ECAs). We describe models whose aim is to capture and understand the specific characteristics of communicative gestures in order to envision how an automatic communicative gesture production mechanism could be built. The work is inspired by research on how human gesture characteristics (e.g., shape of the hand, movement, orientation and timing with respect to the speech) convey meaning. We present approaches to computing where to place a gesture, which shape the gesture takes and how gesture shapes evolve through time. We focus on a particular model based on theoretical frameworks on metaphors and embodied cognition that argue that people can represent, reason about and convey abstract concepts using physical representations and processes, which can be conveyed through physical gestures.