Miracle-dz/UniFAR-FTUs huggingface dataset:
WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.
Cognitive changes as early signs of HIV infection

ABSTRACT‐ Neuropsychological examination was performed on 13 patients and 10 matched controls to assess the brain involvement of patients with human immunodeficiency virus (HIV) infection. HIV‐infected patients showed a significant decline in visuomotor, visuoconstructive and practical abilities as well as in motorfree visuospatial performance and repeating a long sentence. These findings could not be explained by the concomitant mental depression of these patients. Neither were they associated with any particular stage of HIV infection. The results suggest that mild cognitive changes may be detected even in the early stages of HIV infection, when examined by appropriate neuropsychological methods.
A Novel Acetyltransferase Found in Saccharomyces cerevisiae Σ1278b That Detoxifies a Proline Analogue, Azetidine-2-carboxylic Acid*

l-Azetidine-2-carboxylic acid (AZC), a toxic four-membered ring analogue ofl-proline, is transported into the cells via proline transporters. It causes misfolding of the proteins into which it is incorporated competitively with l-proline and thereby inhibits the growth of the cells. We recently have discovered, on the chromosome of Saccharomyces cerevisiae Σ1278b, a novel gene MPR1 required for the resistance of Σ1278 background strains to toxic AZC. This gene was missing in the particular yeast strain used for the genomic sequence determination. Although the protein sequence was homologous to that of the S. cerevisiae transcriptional regulator, Mpr1p did not affect the expression of genes involved in proline uptake. However, gene expression in Escherichia coli and enzymatic analysis showed that the MPR1 gene encodes a novel AZC acetyltransferase, by which l-proline itself and otherl-proline analogues are not acetylated. Mpr1p was considered to be a member of the N-acetyltransferase superfamily based on the results of an Ala-scan mutagenesis through the highly conserved region involved in binding acetyl-CoA in members of the superfamily. Our findings suggest that Mpr1p detoxifies AZC by acetylating it in the cytoplasm. This enzyme might be utilized as a selective marker in a wide variety of organisms, because the cells expressing the MPR1 gene acquire the AZC-resistant phenotype.
A method for improving the machine recognition of confusing Chinese characters

This paper presents a novel low-cost method to improve the recognition of confusing Chinese characters which usually cause the misclassifications in character recognition systems. This proposed method can automatically analyze and select better discriminative features to differentiate confusing characters without any complex operations which are required in many other related approaches. According to our experimental results, this method improves the recognition rate of a recognition system by correcting about 30% of the misclassifications.
Sentioscope: A Soccer Player Tracking System Using Model Field Particles

Tracking multiple players is crucial to analyze soccer videos in real time. Yet, rapid illumination changes and occlusions among players who look similar from a distance make tracking in soccer very difficult. Particle-filter-based approaches have been utilized for their ability in tracking under occlusion and rapid motions. Unlike the common practice of choosing particles on targets, we introduce the notion of shared particles densely sampled at fixed positions on the model field. We globally evaluate targets' likelihood of being on the model field particles using our combined appearance and motion model. This allows us to encapsulate the interactions among the targets in the state-space model and track players through challenging occlusions. The proposed tracking algorithm is embedded into a real-life soccer player tracking system called Sentioscope. We describe the complete steps of the system and evaluate our approach on large-scale video data gathered from professional soccer league matches. The experimental results show that the proposed algorithm is more successful, compared with the previous methods, in multiple-object tracking with similar appearances and unpredictable motion patterns such as in team sports.
Therapy of nontuberculous mycobacterial disease

Purpose of review Nontuberculous mycobacterial disease, especially pulmonary disease, is increasingly encountered by clinicians. Therapy of the most common nontuberculous mycobacterial pathogen, Mycobacterium avium complex, improved with the introduction of macrolide-containing regimens, but treatment for this and most other nontuberculous mycobacterial pathogens remains difficult. Recent findings Treatment trials with macrolide-containing regimens for Mycobacterium avium complex lung disease have yielded generally favorable outcomes. Studies consistently show that in-vitro susceptibility to macrolides remains the only in-vitro susceptibility for Mycobacterium avium complex that correlates with in-vivo response. Patients who have macrolide-resistant Mycobacterium avium complex isolates are much harder to treat and have higher mortality than patients with macrolide-susceptible isolates. Studies also consistently show that patients who fail therapy, even those who remain macrolide susceptible in vitro, are more difficult to treat than patients without previous therapy. Summary There have been no significant treatment advances for Mycobacterium avium complex lung disease, and nontuberculous mycobacterial disease in general, since the advent of the newer macrolides. It has become clear that the best opportunity for treatment success is the first treatment effort. It is also clear that protection against the emergence of macrolide-resistant Mycobacterium avium complex isolates is critically important. For further progress in the treatment of these pathogens, new and more active drugs must be developed.
A hardware-based predictive motion estimation algorithm

For low-power motion estimation hardware solutions, advanced hardware aimed algorithms and architectures are a necessity. A hardware solution for the modern emerging standard H.264 is investigated. Design trade-offs, including search patterns, predictors, and memory architecture, have been made with power consumption and hardware complexity in mind. The memory architecture has been optimised for the variable block sizes used in H.264 motion estimation. All predictors are refined with the small search pattern. An average PSNR of -0.0438 dB compared to the full-search algorithm was simulated. A 2.793 mm/sup 2/ layout, using 0.18 /spl mu/m CMOS technology, of the design running at 7.61 MHz is presented.
Thermodynamic prediction of vapor pressures for polychlorinated dibenzo‐p‐dioxins, polychlorinated dibenzofurans, and polybrominated dibenzo‐p‐dioxins

The vapor pressures and thermal properties of nonmeasured polychlorinated dibenzo‐p‐dioxins and dibenzofurans (PCDD/Fs) and seven polybrominated dibenzo‐p‐dioxins (PBDDs) were predicted using a correlation method based on the experimental vapor pressures for 22 PCDD/Fs and octabrominated dibenzo‐p‐dioxin obtained by the Knudsen effusion method. The vapor pressures of all 59 PCDDs and 131 PCDFs predicted in the present study were more or less higher than the data predicted by Rordorf, although the calculation method was the same. For the most toxic 2,3,7,8‐tetrachlorinated dibenzo‐p‐dioxin, the vapor pressure at 298 K predicted in the present study was 6.2 × 10−6 Pa, which was 31‐fold higher than the value provided by Rordorf (2.0 × 10−7 Pa). The predicted vapor pressures for low‐brominated PBDDs agreed with the data estimated by Rordorf. For 2,3,7,8‐tetrabrominated dibenzo‐p‐dioxin, the vapor pressure predicted in the present study was 1.8 × 10˜8 Pa, obviously lower than those predicted by Rordorf.
Mayer‐Rokitansky‐Kustner Hauser syndrome complicated by either uterine leiomyoma or ovarian tumor

A 50‐year‐old Japanese woman with Mayer‐Rokitansky‐Kustner‐Hauser syndrome and two pelvic tumors underwent laparotomy. Laparotomy revealed that no torsion of the right ovarian tumor had occurred, with uterine leiomyoma originating from the right side of a rudimentary uterus. Histopathological examination demonstrated leiomyoma of the rudimentary uterus with positive staining for estrogen and progesterone receptors, and mucinous cyst adenoma of the right ovary. Uterine leiomyoma is rare in this syndrome and the present report represents the first published case complicated by ovarian tumor.
A contribution to safety analysis of railway CBTC systems using Scola

Regarding their complexity, industrial systems are hard to design and even harder to validate and maintain. We try to address some particular issues of the railway systems conception. Railway systems are characterized by their identified and limited number of failure accidents. Thus, safety analyses is mainly based on the research of failure scenarios that lead to these accidents. Those scenarios represent the misbehavior that must be avoided or corrected in the system. But, the specifications ambiguity makes it difficult to obtain a consistency and completeness in the analysis. At this point, the main issue is the systematic errors. They consist on the gaps on the system description that not only affect the comprehension but also the completeness of the analysis. In this article, we propose an approach for system formalization and safety analyses. We define ScOLA, a modeling language built to understand and to formalize the specifications based on core concepts. We explain how, using a formal description and a stepwise simulation of the system, safety analysis can be performed easier and faster. The approach is applied to the Trainguard Mass Transit (the CBTC product of Siemens) CBTC(Communication Based Train Control) system of Siemens
Associations between comorbid anxiety, diabetes control, and overall medical burden in patients with serious mental illness and diabetes*

Objective While previous work has demonstrated elevation of both comorbid anxiety disorders and diabetes mellitus type II in individuals with serious mental illness, little is known regarding the impact of comorbid anxiety on diabetes mellitus type II outcomes in serious mental illness populations. We analyzed baseline data from patients with serious mental illness and diabetes mellitus type II to examine relationships between comorbid anxiety, glucose control as measured by hemoglobin A1c score, and overall illness burden. Methods Using baseline data from an ongoing prospective treatment study involving 157 individuals with serious mental illness and diabetes mellitus type II, we compared individuals with and without a comorbid anxiety disorder and compared hemoglobin A1c levels between these groups to assess the relationship between anxiety and management of diabetes mellitus type II. We conducted a similar analysis using cumulative number of anxiety diagnoses as a proxy for anxiety load. Finally, we searched for associations between anxiety and overall medical illness burden as measured by Charlson score. Results Anxiety disorders were seen in 33.1% (N = 52) of individuals with serious mental illness and diabetes mellitus type II and were associated with increased severity of depressive symptoms and decreased function. Hemoglobin A1c levels were not significantly different in those with or without anxiety, and having multiple anxiety disorders was not associated with differences in diabetes mellitus type II control. However, depressive symptoms were significantly associated with higher hemoglobin A1c levels. Neither comorbid anxiety nor anxiety load was significantly associated with overall medical burden. Conclusion One in three people with serious mental illness and diabetes mellitus type II had anxiety. Depressive symptoms were significantly associated with Hb1Ac levels while anxiety symptoms had no relation to hemoglobin A1c; this is consistent with previously published work. More studies are needed to better understand the relationship between depression, anxiety, and health management in people with serious mental illness and diabetes mellitus type II.
Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user’s familiarity with VR and user’s interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.
A marker of acid‐secreting membrane movement in rat gastric parietal cells

A monoclonal antibody (mab 146.14) marker of the movement of acid‐secreting membranes in rat gastric partial cells has been produced and characterized. Mab 146.14 recognized a 95‐kD major component of a purified membrane fraction of rat gastric mucosa, the protein composition of which was similar to that of well characterized porcine H+‐K+ ATPase‐enriched membranes, and that presented the characteristic shift of density depending on whether it was purified from resting or stimulated tissues. Further biochemical analysis characterized the antigen as a membranous protein that might be in its native form, part of a higher multimolecular complex.
Cell-mediated immune responses to respiratory syncytial virus infection

We evaluated the cell-mediated immune (CMI) response to RSV acute infection including the magnitude, kinetics and correlates with morbidity and age. Twenty-nine RSV-infected patients with mean ± SD age of 15 ± 14 months were enrolled during their first week of disease. Th1, Th2, Th9, Th17 and Th22 responses were measured at entry and 2 and 6 weeks later. All subjects were hospitalized for a median (range) of 5 (3–11) days. RSV-specific effector and memory Th1 CMI measured by lymphocyte proliferation and IFNγ ELISPOT significantly increased over time (P ≤ 0.03). In contrast, Th22 responses decreased over time (P ≤ 0.03). Other changes did not reach statistical significance. The severity of RSV disease measured by the length of hospitalization positively correlated with the magnitude of Th9, Th22 and TNFα inflammatory responses (rho ≥ 0.4; P ≤ 0.04) and negatively with memory CMI (rho = –0.45; P = 0.04). The corollary of this observation is that robust Th1 and/or low Th9, Th22, and TNFα inflammatory responses may be associated with efficient clearance of RSV infection and therefore desirable characteristics of an RSV vaccine. Young age was associated with low memory and effector Th1 responses (rho ≥ 0.4; P ≤ 0.04) and high Th2, Th9, Th17, Th22 and TNFα inflammatory responses (rho ≤ –0.4; P ≤ 0.04), indicating that age at vaccination may be a major determinant of the CMI response pattern.
Immersion Ultrasonography: Simultaneous A‐Scan and B‐Scan

In eyes with opaque media, ophthalmic ultrasound provides a unique source of information that can dramatically affect the course of patient management. In addition, when an ocular abnormality can be visualized, ultrasonography provides information that supplements and complements other diagnostic testing. It provides documentation and differentiation of abnormal states, such as vitreous hemorrhage and intraocular tumor, as well as differentiation of orbital tumors from inflammatory causes of exophthalmos. Additional capabilities of ultrasound are biometric determinations for calculation of intraocular lens implant powers and drug-effectiveness studies. Maximal information is derived from ultrasonography when A-scan and B-scan techniques are employed simultaneously. Flexibility of electronics, variable-frequency transducers, and the use of several different manual scanning patterns aid in detection and interpretation of results. The immersion system of ultrasonography provides these features optimally.
Machine Learning Assisted Prediction Algorithm of Atmospheric Ion Mobility

Atmosphere ion mobility is an important electrical parameter related to the filamentary ion flow field of high voltage direct current (HVDC) transmission lines and other characteristics of the glow and streamer corona discharge. Many machine learning (ML)-based algorithms have already been widely used in the prediction of air discharge. This article proposes a genetic algorithm (GA)-support vector regression (SVR) combined with kernel principal components analysis (PCA) to predict the ion mobility, involving dimensionality reduction, feature selection, parameter optimization of SVR. Kernel PCA could reduce the dimension of data and GA with adaptive probability parameters is employed to optimize the parameters of SVR model. An improved parallel-plates ion generator is employed to produce corona discharge and then measure the saturation ion current density and then obtain the training data and testing data of ion mobility. The prediction results show that the proposed algorithm outperforms the other methods in terms of mean relative error and mean squared error criteria. In addition, the parameters of model and data features have a major influence on the performance of the prediction algorithm. Based on the measured data and reference data, the prediction result obtained on ion mobility under different humidity shows a satisfactory generalization and effectiveness of proposed model for the prediction.
Prostaglandin E2 Activates and Utilizes mTORC2 as a Central Signaling Locus for the Regulation of Mast Cell Chemotaxis and Mediator Release*

Prostaglandin (PG) E2, a potent mediator produced in inflamed tissues, can substantially influence mast cell responses including adhesion to basement membrane proteins, chemotaxis, and chemokine production. However, the signaling pathways by which PGE2 induces mast cell chemotaxis and chemokine production remains undefined. In this study, we identified the downstream target of phosphatidylinositol 3-kinase, mammalian target of rapamycin (mTOR), as a key regulator of these responses. In mouse bone marrow-derived mast cells, PGE2 was found to induce activation of mTORC1 (mTOR complexed to raptor) as indicated by increased p70S6K and 4E-BP1 phosphorylation, and activation of mTORC2 (mTOR complexed to rictor), as indicated by increased phosphorylation of AKT at position Ser473. Selective inhibition of the mTORC1 cascade by rapamycin or by the use of raptor-targeted shRNA failed to decrease PGE2-mediated chemotaxis or chemokine generation. However, inhibition of the mTORC2 cascade through the dual mTORC1/mTORC2 inhibitor Torin, or through rictor-targeted shRNA, resulted in a significant attenuation in PGE2-mediated chemotaxis, which was associated with a comparable decrease in actin polymerization. Furthermore, mTORC2 down-regulation decreased PGE2-induced production of the chemokine monocyte chemoattractant protein-1 (CCL2), which was linked to a significant reduction in ROS production. These findings are consistent with the conclusion that activation of mTORC2, downstream of PI3K, represents a critical signaling locus for chemotaxis and chemokine release from PGE2-activated mast cells.
Molecular Patterns in Human Ulcerative Colitis and Correlation with Response to Infliximab

Background:As a T cell–mediated disease of the colonic epithelium, ulcerative colitis (UC) is likely to share pathogenic elements with other T cell–mediated inflammatory diseases. Recently, microarray analysis revealed large-scale molecular changes in T cell–mediated rejection of kidney and heart transplants. We hypothesized that similar disturbances might be operating in UC and could provide insights into responsiveness to therapy. Methods:We studied 56 colon biopsies from patients with colitis characterizing the clinical and histological features and using microarrays to define the messenger RNA phenotype. We expressed the microarray results using previously defined pathogenesis-based transcript sets. We also studied 48 published microarray files from human colon biopsies downloaded from the Gene Expression Omnibus database, classified by response to infliximab therapy, to examine whether the molecular measurements derived from our studies correlated with nonresponsiveness to treatment. Results:UC biopsies manifested coordinate transcript changes resembling rejecting transplants, with effector T cell, IFNG-induced, macrophage, and injury transcripts increasing while parenchymal transcripts decreased. The disturbance in gene expression, summarized as principal component 1 (PC1), correlated with conventional clinical and histologic assessments. When assessed in microarray results from published studies, the disturbance (PC1) predicted response to infliximab: patients with intense disturbance did not achieve clinical response, although quantitative improvement was seen even in many clinical nonresponders. Similar changes were seen in Crohn's colitis. Conclusions:The molecular phenotype of UC manifests a large-scale coordinate disturbance reflecting changes in inflammatory cells and parenchymal elements that correlates with conventional features and predicts response to infliximab.