Miracle-dz/UniFAR-FTUs huggingface dataset:
PATHOGENIC MYCOBACTERIA CUSTOMIZE THEIR VIRULENCE MECHANISMS FOR INTRACELLULAR REPLICATION IN DIFFERENT HOSTS 6

6 SUMMARY Broad-host pathogens have adapted a lifestyle that enables them to survive and replicate in different environments. Mycobacterium marinum, a close relative of Mycobacterium tuberculosis, is such an intracellular pathogen that can infect, survive and replicate in phagocytic cells of vertebrates as well as protozoan species. Here, we employed transposon-directed insertion-site sequencing (TraDIS) of a large pool of M. marinum transposon mutants to determine which genes are required, advantageous or disadvantageous for survival and replication in phagocytic cells of different host species. We found a number of general virulence mechanisms relevant for all hosts, including the ESX-1 protein secretion system, biosynthesis of the polyketide lipid phthiocerol dimycocerosate (PDIM) and proteins involved in uptake and metabolism of cholesterol. In addition, we could identify the putative CpsA regulator as a new general virulence factor. Interestingly, our data also showed that M. marinum contains a large set of host-specific virulence determinants. For instance, the general virulence-associated ESX-1 system requires auxiliary proteins that play a crucial role in species-dependent infection. We show that the bacterial factors required for a successful infection of protozoan species frequently differ from those used for macrophages derived from vertebrates, indicating that broad-host range pathogens such as M. marinum contain different sets of virulence factors that are customized for specific hosts. Furthermore, these data also imply that although amoebae may be used as an experimental model to study host-pathogen interactions for intracellular pathogens, they do not fully manifest interactions with pathogens relevant for crossing species or even kingdom barriers.
An Inclusive Process for Departmental Textbook Selection

This article reports on the development and use of an inclusive process to select a departmental textbook for general psychology. It describes a method for developing department-specific instruments to narrow the available textbook choices and an in-class textbook trial used in the final selection. Reported advantages of this method include “buy-in” by faculty, student evaluation of the books and ancillary materials, and classroom testing. We also provide several recommendations to assist other departments in employing our rubric.
The impact of differing maternal expectations on the academic achievements of primary school children in Urban Bangalore, South: a comparison between boys and girls

Education of girls in India lags behind that of boys and several communities in India fare worse than others. Because of their secondary status in the society, Indian girls tend to suffer from low self-esteem. Thus, it is necessary to study the reasons why girls are being discouraged from attending and completing school as well as what are the expectations mothers have from them when compared to their male siblings. Data was collected using structured questionnaires and semi-structured interviews with the participants. Of 15 mothers, 12 completed the questionnaires; of these, five mothers were interviewed, and their data was used to present the findings of a comparative study on the maternal expectations on academic achievements in primary school children residing in Urban Bangalore, South. This study also throws light on some of the peripheral issues that emerge while considering maternal expectations on academic outcomes. The study examines how maternal expectations might differ in the case of boys versus girls within an Indian context, particularly within an urban setting in Bangalore, Karnataka. No differences were seen in the expectations mothers had of their sons compared to their daughters, but several other issues emerged. The results of this study may not be used conclusively as this is just a small-scale research project and hence no generalisation can be made to address such prevalent cultural issues in India, particularly in Urban South Bangalore, Karnataka, South India. The results of this study illustrate the need for more literature within the Indian context. [ABSTRACT FROM AUTHOR]
Preventive Effect of Rebamipide Gargle on Chemoradiotherpy-Induced Oral Mucositis in Patients with Oral Cancer: a Pilot Study

ABSTRACT Objectives To assess the efficacy and safety of rebamipide in preventing chemoradiotherapy-induced oral mucositis in patients with oral cancer. Material and Methods Patients with oral cancer treated with chemoradiotherapy (daily radiotherapy plus docetaxel hydrate once a week) were enrolled for this study. They were assigned in a double-blind fashion to receive either rebamipide gargle or placebo on the days of chemoradiotherapy. Oral mucositis was assessed using the WHO grading system. The primary endpoint of this study was the incidence of grade 3 - 4 mucositis after exposure to 40 Gy radiation (4 weeks). The secondary endpoint was the effect of rebamipide gargle on tumour response to chemoradiotherapy. Results Twenty-four patients were randomly assigned to receive rebamipide gargle (n = 12) or placebo-gargle (n = 12) during chemoradiotherapy. The number of patients with severe mucositis (WHO ≥ 3) was higher in the placebo group than in the rebamipide group (83.3% vs. 33.3%, P = 0.036). In addition, no effect of rebamipide gargle on tumour response to chemoradiotherapy was recognized compared with the placebo group. Conclusions For patients with oral cancer undergoing chemoradiotherapy, rebamipide gargle may contribute to decrease the severity of oral mucositis.
The Role of Lipoprotein-Associated Phospholipase A₂ in a Murine Model of Experimental Autoimmune Uveoretinitis

Macrophage activation is, in part, regulated via hydrolysis of oxidised low density lipoproteins by Lipoprotein-Associated phospholipase A2 (Lp-PLA2), resulting in increased macrophage migration, pro-inflammatory cytokine release and chemokine expression. In uveitis, tissue damage is mediated as a result of macrophage activation; hence inhibition of Lp-PLA2 may limit macrophage activation and protect the tissue. Utilising Lp-PLA2 gene-deficient (KO) mice and a pharmacological inhibitor of Lp-PLA2 (SB-435495) we aimed to determine the effect of Lp-PLA2 suppression in mediating retinal protection in a model of autoimmune retinal inflammation, experimental autoimmune uveoretinitis (EAU). Following immunisation with RBP-3 (IRBP) 1–20 or 161–180 peptides, clinical disease was monitored and severity assessed, infiltrating leukocytes were enumerated by flow cytometry and tissue destruction quantified by histology. Despite ablation of Lp-PLA2 enzyme activity in Lp-PLA2 KO mice or wild-type mice treated with SB-435495, the number of infiltrating CD45+ cells in the retina was equivalent to control EAU animals, and there was no reduction in disease severity. Thus, despite the reported beneficial effects of therapeutic Lp-PLA2 depletion in a variety of vascular inflammatory conditions, we were unable to attenuate disease, show delayed disease onset or prevent progression of EAU in Lp-PLA2 KO mice. Although EAU exhibits inflammatory vasculopathy there is no overt defect in lipid metabolism and given the lack of effect following Lp-PLA2 suppression, these data support the hypothesis that sub-acute autoimmune inflammatory disease progresses independently of Lp-PLA2 activity.
Using Random Neural Network for Load Balancing in Data Centers

A data center which consists of thousands of connected computer servers can be considered as a shared resource of processing capacity (CPU), memory, and disk space etc. The jobs arriving at the cloud data center are distributed to different servers via different paths. In addition, the internal traffic between servers inside the data center needs to be load balanced to multiple paths between them as well. How to select the underutilized or idle paths for the traffic so as to achieve load balancing and throughput optimality is a big challenge. The Random Neural Network (RNN) is a recurrent neural network in which neurons interact with each other by exchanging excitatory and inhibitory spiking signals. The stochastic excitatory and inhibitory interactions in the network makes the RNN an excellent modeling tool for various interacting entities. It has been applied in a number of applications such as optimization, communication systems, simulation pattern recognition and classification. In this paper, we propose to use Random Neural Network (RNN) to solve the load balancing problem in data centers. RNN is able to achieve adaptive load balancing based on the online measurements of path congestion gathered from the network.
Transformation of managerial skills of engineers

This article argues that there is a difference in leadership styles between experienced and inexperienced engineers in South Africa. It was found that experienced engineers are more transformational and more transactional than inexperienced engineers. To demonstrate this in the study, the researcher uses the Multifactor Leadership Questionnaire (MLQ) of Bass and Avolio (1995) to identify leadership style. The argument is substantiated by the results of a statistical analysis of leadership style administered to 85 experienced and inexperienced engineers in total. Opsomming Hierdie artikel debateer die verskil in leierskapstyle tussen ervare en onervare ingenieurs in Suid-Afrika. Dit is bevind dat ervare ingenieurs meer transformasioneel en meer transaksioneel is wanneer vergelyk word met onervare ingenieurs. Om hierdie studie te demonstreer, het die navorser gebruik gemaak van die Multifaktor Leierskapvraelys (MLV) van Bass en Avolio (1995) om leierskapstyl te identifiseer Die argument word verder ondersteun deur die resultate van statistiese analise van leierskapstyl wat uitgevoer is op 85 deelnemers, beide van ervare en onervare ingenieurs in totaal.
Scale-Optimized Textons for Image Categorization and Segmentation

Texton is a representative dense visual word and it has proven its effectiveness in categorizing materials as well as generic object classes. Despite its success and popularity, no prior work has tackled the problem of its scale optimization for a given image data and associated object category. We propose scale-optimized textons to learn the best scale for each object in a scene, and incorporate them into image categorization and segmentation. Our textonization process produces a scale-optimized codebook of visual words. We approach the scale-optimization problem of textons by using the scene-context scale in each image, which is the effective scale of local context to classify an image pixel in a scene. We perform the textonization process using the randomized decision forest which is a powerful tool with high computational efficiency in vision applications. Our experiments using MSRC and VOC 2007 segmentation dataset show that our scale-optimized textons improve the performance of image categorization and segmentation.
Mining control flow graph as API call-grams to detect portable executable malware

Present day malware shows stealthy and dynamic capability and avails administrative rights to control the victim computers. Malware writers depend on evasion techniques like code obfuscation, packing, compression, encryption or polymorphism to avoid detection by Anti-Virus (AV) scanners as AV primarily use syntactic signature to detect a known malware. Our approach is based on semantic aspect of PE exectable that analyses API Call-grams to detect unknown malicious code. As in--exact source code is analysed, the machine is not infected by the executable. Moreover, static analysis covers all the paths of code which is not possible with dynamic behavioural methods as latter does not gurantee the execution of sample being analysed. Modern malicious samples also detect controlled virtual and emulated environments and stop the functioning. Semantic invariant approach is important as signature of known samples are changed by code obfuscation tools. Static analysis is performed by generating an API Call graph from control flow of an executable, then mining the Call graph as API Call-gram to detect malicious files.
Data Collection for Interactive Learning through the Dialog

This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog. This interactive learning will help with one of the most prevailing problems of open domain dialog system, which is the sparsity of facts a dialog system can reason about. The proposed dataset, consisting of 1900 collected dialogs, allows simulation of an interactive gaining of denotations and questions explanations from users which can be used for the interactive learning.
Hystopathological study of the intestinal epithelium submitted to different times of programmed ischemia and twenty four hours reperfusion.

PURPOSE To investigate the changes in the intestinal epithelium of rats, when submitted to the ischemia of varied time and scheduled reperfusion. METHODS For this purpose, the following groups were established six groups: control, 10, 20, 30, 40, 60 minutes of ischemia and reperfusion time (24 hours). Their intestines being resected for histopathological analysis: the length and width of the villus, and mitotic index. The nonparametric test Kruskal-Wallis and multiple comparisons test were applied. RESULTS The qualitatively the intestinal epithelium is regenerated when submitted to different ischemia, and 24 hours reperfusion time. It was observed that morphometry analyzing taking villus length and width and mitotic index as parameter, meaningful alterations were detected. CONCLUSION The epithelium is regenerated, when subjected to different time planned of ischemia and reperfusion. It was observed: decrease the length and increase the width of the villi, when compared Group VI with I and II, and Group VI with I; increased mitotic index when compared Groups III with I. Every analysis was statistically significant.
Involvement of macrophage-like cells in growth of tumors induced by avian sarcoma virus.

We have investigated the mechanism(s) whereby the s.c. injection of Bacillus Calmette-Guerin (BCG) into chicken wing webs, 7 days prior to inoculation with avian sarcoma virus (ASV) at the same site, can dramatically stimulate the growth of ASV-induced tumors. The results show that neoplasms induced in this fashion contain a much higher percentage of macrophage-like cells than do those induced by ASV alone. These macrophage-like cells have been identified on the basis of adherence properties and their abilities to phagocytose red blood cells, to express Fc receptors at their surface, and to express nonspecific esterase activity. They are apparently infected by ASV, and both produce progeny virus in culture and express viral antigens at their surface. Inoculation of such cells into chickens leads to tumor development, indicating that they can produce ASV in vivo as well as in vitro . The macrophage-like cells in question are present most predominantly during the earliest stages of ASV-induced tumor growth and can survive in tissue culture over long periods. Although pretreatment with BCG at sites other than that used for ASV inoculation had no effect on tumor growth, the administration of BCG at any site was stimulatory to the development of antitumor immunity. This was obviously of little avail in the case of animals developing tumors resulting from the BCG enhancement effect. We conclude that pretreatment with BCG, under the circumstances tested, gives rise to an acute granulomatous reaction which provides large numbers of target cells for transformation by ASV administered at the same site. Many of these cells are macrophages which provide a reservoir for continuous virus production in the face of an antitumor immune response. Macrophage-like cells are also present in tumors which arise following inoculation by ASV alone, but to an extent apparently insufficient to permit irreversible tumor growth.
Data-driven modeling of skin and muscle deformation

In this paper, we present a data-driven technique for synthesizing skin deformation from skeletal motion. We first create a database of dynamic skin deformations by recording the motion of the surface of the skin with a very large set of motion capture markers. We then build a statistical model of the deformations by dividing them into two parts: static and dynamic. Static deformations are modeled as a function of pose. Dynamic deformations are caused by the actions of the muscles as they move the joints and the inertia of muscles and fat. We approximate these effects by fitting a set of dynamic equations to the pre-recorded data. We demonstrate the viability of this approach by generating skin deformations from the skeletal motion of an actor. We compare the generated animation both to synchronized video of the actor and to ground truth animation created directly from the large marker set.
Data Mining: Understanding Data and Disease Modeling

Analyzing large data sets requires proper understanding of the data in advance. This would help domain experts to influence the data mining process and to properly evaluate the results of a data mining application. In this paper, we introduce an algorithm to identify anomalies in the data. We also propose an approach to include the results of data characteristics checking in a data mining application. The application, reported in this paper, involves developing a disease model from gene expression data using machine learning techniques. We demonstrate how: (i) simple models can be generated from a large set of attributes and (ii) the structure of the models change, when potentially anomalous cases are removed.
Gravitational Lensing of the Sloan Digital Sky Survey High-Redshift Quasars

We predict the effects of gravitational lensing on the color-selected flux-limited samples of zs ~ 4.3 and zs ≳ 5.8 quasars, recently published by the Sloan Digital Sky Survey (SDSS). Our main findings are the following: (1) The lensing probability should be 1-2 orders of magnitude higher than for conventional surveys. The expected fraction of multiply imaged quasars is highly sensitive to redshift and the uncertain slope of the bright end of the luminosity function, βh. For βh = 2.58 (3.43) we find that at zs ~ 4.3 and i* < 20.0 the fraction is ~4% (13%), while at zs ~ 6 and z* < 20.2 the fraction is ~7% (30%). (2) The distribution of magnifications is heavily skewed; sources having the redshift and luminosity of the SDSS zs ≳ 5.8 quasars acquire median magnifications of med(μobs) ~ 1.1-1.3 and mean magnifications of ⟨μobs⟩ ~ 5-50. Estimates of the quasar luminosity density at high redshift must therefore filter out gravitationally lensed sources. (3) The flux in the Gunn-Peterson trough of the highest redshift (zs = 6.28) quasar is known to be fλ < 3 × 10-19 ergs s-1 cm-2 Å-1. Should this quasar be multiply imaged, we estimate a 40% chance that light from the lens galaxy would have contaminated the same part of the quasar spectrum with a higher flux. Hence, spectroscopic studies of the epoch of reionization need to account for the possibility that a lens galaxy, which boosts the quasar flux, also contaminates the Gunn-Peterson trough. (4) Microlensing by stars should result in ~ of multiply imaged quasars in the zs ≳ 5.8 catalog varying by more than 0.5 mag over the next decade. The median emission-line equivalent width of multiply imaged quasars would be lowered by ~20% with respect to the intrinsic value because of differential magnification of the continuum and emission-line regions.
Interference Suppression Using Generalized Inverse Precoder for Downlink Heterogeneous Networks

This letter proposes an optimal precoder design using a generalized inverse structure for downlink crosstier interference suppression between macro base station (MBS) and femtocell access point (FAP) transmission in heterogeneous networks. Using Tikhonov regularization, we derive a closed form relationship between given MBS transmit power or interference constraints and the regularization parameter. Determining a Pareto optimal regularization parameter requires extensive computation. As an alternative, we propose an algorithm to find a suitable regularization parameter to achieve fairness in the system, i.e., equal FAP and MBS user rates. Simulation results show that the proposed precoder greatly improves FAP user rates at the expense of a small yet tolerable decrease in average MBS user rate.
Beyond boolean search: FLEXICON, a legal tex-based intelligent system

msrRAcT FIEUCON is a new litigation support system being developed for the effective retrieval of legal documents by legal and paralegal professionals. It includes a text analysis and proceaing component, processing raw text and intelligently extracting key information in the form of electronic “headnotca”. It also includrx an innovative non-boolean search and retrieval mechanism. As well, it provide-s many features that improve legal research such as a menudriven interface, thesauri, relevance feedback and retrieval by topic.
Common Variants at the 19p13.1 and ZNF365 Loci Are Associated with ER Subtypes of Breast Cancer and Ovarian Cancer Risk in BRCA1 and BRCA2 Mutation Carriers

Background: Genome-wide association studies (GWAS) identified variants at 19p13.1 and ZNF365 (10q21.2) as risk factors for breast cancer among BRCA1 and BRCA2 mutation carriers, respectively. We explored associations with ovarian cancer and with breast cancer by tumor histopathology for these variants in mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Methods: Genotyping data for 12,599 BRCA1 and 7,132 BRCA2 mutation carriers from 40 studies were combined. Results: We confirmed associations between rs8170 at 19p13.1 and breast cancer risk for BRCA1 mutation carriers [HR, 1.17; 95% confidence interval (CI), 1.07–1.27; P = 7.42 × 10−4] and between rs16917302 at ZNF365 (HR, 0.84; 95% CI, 0.73–0.97; P = 0.017) but not rs311499 at 20q13.3 (HR, 1.11; 95% CI, 0.94–1.31; P = 0.22) and breast cancer risk for BRCA2 mutation carriers. Analyses based on tumor histopathology showed that 19p13 variants were predominantly associated with estrogen receptor (ER)-negative breast cancer for both BRCA1 and BRCA2 mutation carriers, whereas rs16917302 at ZNF365 was mainly associated with ER-positive breast cancer for both BRCA1 and BRCA2 mutation carriers. We also found for the first time that rs67397200 at 19p13.1 was associated with an increased risk of ovarian cancer for BRCA1 (HR, 1.16; 95% CI, 1.05–1.29; P = 3.8 × 10−4) and BRCA2 mutation carriers (HR, 1.30; 95% CI, 1.10–1.52; P = 1.8 × 10−3). Conclusions: 19p13.1 and ZNF365 are susceptibility loci for ovarian cancer and ER subtypes of breast cancer among BRCA1 and BRCA2 mutation carriers. Impact: These findings can lead to an improved understanding of tumor development and may prove useful for breast and ovarian cancer risk prediction for BRCA1 and BRCA2 mutation carriers. Cancer Epidemiol Biomarkers Prev; 21(4); 645–57. ©2012 AACR.