Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of ARF6, Rab11 and External Hsp90 in the Trafficking and Recycling of Recombinant-Soluble Neisseria meningitidis Adhesin A (rNadA) in Human Epithelial Cells

Neisseria meningitidis adhesin A (NadA) is a meningococcus surface protein thought to assist in the adhesion of the bacterium to host cells. We have previously shown that NadA also promotes bacterial internalization in a heterologous expression system. Here we have used the soluble recombinant NadA (rNadA) lacking the membrane anchor region to characterize its internalization route in Chang epithelial cells. Added to the culture medium, rNadA internalizes through a PI3K-dependent endocytosis process not mediated by the canonical clathrin or caveolin scaffolds, but instead follows an ARF6-regulated recycling pathway previously described for MHC-I. The intracellular pool of rNadA reaches a steady state level within one hour of incubation and colocalizes in endocytic vesicles with MHC-I and with the extracellularly labeled chaperone Hsp90. Treatment with membrane permeated and impermeable Hsp90 inhibitors 17-AAG and FITC-GA respectively, lead to intracellular accumulation of rNadA, strongly suggesting that the extracellular secreted pool of the chaperone is involved in rNadA intracellular trafficking. A significant number of intracellular vesicles containing rNadA recruit Rab11, a small GTPase associated to recycling endosomes, but do not contain transferrin receptor (TfR). Interestingly, cell treatment with Hsp90 inhibitors, including the membrane-impermeable FITC-GA, abolished Rab11-rNadA colocalization but do not interfere with Rab11-TfR colocalization. Collectively, these results are consistent with a model whereby rNadA internalizes into human epithelial cells hijacking the recycling endosome pathway and recycle back to the surface of the cell via an ARF6-dependent, Rab11 associated and Hsp90-regulated mechanism. The present study addresses for the first time a meningoccoccal adhesin mechanism of endocytosis and suggests a possible entry pathway engaged by N. meningitidis in primary infection of human epithelial cells.
Genomic Alteration in Head and Neck Squamous Cell Carcinoma (HNSCC) Cell Lines Inferred from Karyotyping, Molecular Cytogenetics, and Array Comparative Genomic Hybridization

Genomic alteration in head and neck squamous cell carcinoma (HNSCC) was studied in two cell line pairs (HN30-HN31 and HN4-HN12) using conventional C-banding, multiplex fluorescence in situ hybridization (M-FISH), and array comparative genomic hybridization (array CGH). HN30 and HN4 were derived from primary lesions in the pharynx and base of tongue, respectively, and HN31 and HN12 were derived from lymph-node metastatic lesions belonging to the same patients. Gain of chromosome 1, 7, and 11 were shared in almost all cell lines. Hierarchical clustering revealed that HN31 was closely related to HN4, which shared eight chromosome alteration cases. Large C-positive heterochromatins were found in the centromeric region of chromosome 9 in HN31 and HN4, which suggests complex structural amplification of the repetitive sequence. Array CGH revealed amplification of 7p22.3p11. 2, 8q11.23q12. 1, and 14q32.33 in all cell lines involved with tumorigenesis and inflammation genes. The amplification of 2p21 (SIX3), 11p15.5 (H19), and 11q21q22.3 (MAML2, PGR, TRPC6, and MMP family) regions, and deletion of 9p23 (PTPRD) and 16q23.1 (WWOX) regions were identified in HN31 and HN12. Interestingly, partial loss of PTPRD (9p23) and WWOX (16q23.1) genes was identified in HN31 and HN12, and the level of gene expression tended to be the down-regulation of PTPRD, with no detectable expression of the WWOX gene. This suggests that the scarcity of PTPRD and WWOX genes might have played an important role in progression of HNSCC, and could be considered as a target for cancer therapy or a biomarker in molecular pathology.
Xenotropic Murine Leukemia Virus-Related Virus Establishes an Efficient Spreading Infection and Exhibits Enhanced Transcriptional Activity in Prostate Carcinoma Cells

ABSTRACT Xenotropic murine leukemia virus-related virus (XMRV) is a novel human gammaretrovirus discovered in association with human prostate tumors. XMRV was first identified in prostate stromal cells surrounding the tumors of patients carrying a mutation in the HPC1 gene locus. To determine the tropism of XMRV in cell culture, we tested the ability of XMRV to spread and replicate in various prostate and nonprostate cell lines. We found that although the expression of XMRV viral proteins and the spread of infectious virus were minimal in a variety of cell lines, XMRV displayed robust expression and infection in LNCaP prostate tumor cells. The transcriptional activity of the XMRV long terminal repeat (LTR) was found to be higher than the Moloney murine leukemia virus LTRs in both LNCaP and WPMY-1 (simian virus 40-transformed prostate stromal cells). The U3 promoter of XMRV and a glucocorticoid response element (GRE) within the U3 were required for the transcriptional activity in LNCaP cells. Coexpression of the androgen receptor and stimulation with dihydrotestosterone stimulated XMRV-LTR-dependent transcription in 293T cells, and the GRE was required for this activity. These data suggest that XMRV may replicate more efficiently in LNCaP cells in part due to the transcriptional environment in LNCaP cells.
Extracting Topical Information of Tweets Using Hashtags

Twitter is one of the largest micro blogging web sites where users share news, their opinions, moods, recommendations by posting text messages, and it is mostly used like a news media. Since the data being shared via Twitter is vast, many researches are focusing on extracting meaningful information with the help of information retrieval systems. Retrieving meaningful information from social media applications became important for several tasks such as sentiment analysis, detecting anomalies, and recommendation systems. Topic modeling is one of the mostly studied and hard problems in information retrieval area, and it is even more challenging to model topics when the documents are too short such as tweets. In this paper, we focus on developing an effective and efficient method to overcome this challenge of tweets being too short for topic modeling. We compare different topic modeling schemes, one of which is not studied before, based on Latent Dirichlet Allocation (LDA) that merges tweets in order to improve LDA performance. We also demonstrate our experimental results with unbiased data collection and evaluation methodologies.
Putting NLP Tools into Action : Incorporating NLP Tools in Web-based Language Learning Environment

Sophisticated Natural Language Processing (NLP) Tools have been developed over the last few decades for many languages. For example, the ChaSen Morphological Analyzer and the KNP Parser for Japanese; the SWATH Segmenter for Thai; and CRL-New Mexico’s Segmenter for Chinese are being widely used by NLP researchers. Several copyright-free machine readable dictionaries (MRD) are also available now, and some of these MRDs also include pronunciations (sound files) and example sentences, etc. By putting a few NLP Tools together, it is possible to build smart applications that can enhance foreign language teaching and learning experiences. In this paper, we will investigate such applications in a Web-based foreign language learning
Behind the Stripes: An Exploration of Female Football Officials' Experiences

This study focused on the personal and work-related experiences of high school and college female football officials. The purpose of this study was to gain a holistic perspective of the experiences of these women working in the male-dominated career of football officiating. Previous literature has examined sports officials in regards to retention, recruitment, and their safety, but relatively little research has focused specifically on female football officials. The participants were able to share their unique experiences in this vocation. Qualitative research was employed in order to illustrate the experiences of female football officials. The theoretical framework for this study was based on the feminist perspective along with social role theory (Eagly, 1997). A case study methodology was used with eight purposeful and criterion selected participants. In-depth interviews were conducted with each participant and served as the primary source of data collection. Data was
General Practice and Pandemic Influenza: A Framework for Planning and Comparison of Plans in Five Countries

Background Although primary health care, and in particular, general practice will be at the frontline in the response to pandemic influenza, there are no frameworks to guide systematic planning for this task or to appraise available plans for their relevance to general practice. We aimed to develop a framework that will facilitate planning for general practice, and used it to appraise pandemic plans from Australia, England, USA, New Zealand and Canada. Methodology/Principal Findings We adapted the Haddon matrix to develop the framework, populating its cells through a multi-method study that incorporated the peer-reviewed and grey literature, interviews with general practitioners, practice nurses and senior decision-makers, and desktop simulation exercises. We used the framework to analyse 89 publicly-available jurisdictional plans at similar managerial levels in the five countries. The framework identifies four functional domains: clinical care for influenza and other needs, public health responsibilities, the internal environment and the macro-environment of general practice. No plan addressed all four domains. Most plans either ignored or were sketchy about non-influenza clinical needs, and about the contribution of general practice to public health beyond surveillance. Collaborations between general practices were addressed in few plans, and inter-relationships with the broader health system, even less frequently. Conclusions This is the first study to provide a framework to guide general practice planning for pandemic influenza. The framework helped identify critical shortcomings in available plans. Engaging general practice effectively in planning is challenging, particularly where governance structures for primary health care are weak. We identify implications for practice and for research.
Network Intrusion Detection using Random Forests

Network Intrusion Detection Systems (NIDSs) have become an important component in network security infrastructure. Currently, many NIDSs are rule-based systems whose performances highly depend on their rule sets. Unfortunately, due to the huge volume of network traffic, coding the rules by security experts becomes difficult and time-consuming. Since data mining techniques can build intrusion detection models adaptively, data mining-based NIDSs have significant advantages over rule-based NIDSs. Therefore, we apply one of the efficient data mining algorithms called random forests for network intrusion detection. The NIDS can be employed to detect intrusions online. In this paper, we discuss the approaches for handling imbalanced intrusions, selecting features, and optimizing the parameters of random forests. We also report our experimental results over the KDD’99 datasets. The results show that the proposed approach provides better performance compared to the best results from the
Results from the CERN pilot CLOUD experiment

Abstract. During a 4-week run in October–November 2006, a pilot experiment was performed at the CERN Proton Synchrotron in preparation for the Cosmics Leaving OUtdoor Droplets (CLOUD) experiment, whose aim is to study the possible influence of cosmic rays on clouds. The purpose of the pilot experiment was firstly to carry out exploratory measurements of the effect of ionising particle radiation on aerosol formation from trace H2SO4 vapour and secondly to provide technical input for the CLOUD design. A total of 44 nucleation bursts were produced and recorded, with formation rates of particles above the 3 nm detection threshold of between 0.1 and 100 cm−3s−1, and growth rates between 2 and 37 nm h−1. The corresponding H2O concentrations were typically around 106 cm−3 or less. The experimentally-measured formation rates and H2SO4 concentrations are comparable to those found in the atmosphere, supporting the idea that sulphuric acid is involved in the nucleation of atmospheric aerosols. However, sulphuric acid alone is not able to explain the observed rapid growth rates, which suggests the presence of additional trace vapours in the aerosol chamber, whose identity is unknown. By analysing the charged fraction, a few of the aerosol bursts appear to have a contribution from ion-induced nucleation and ion-ion recombination to form neutral clusters. Some indications were also found for the accelerator beam timing and intensity to influence the aerosol particle formation rate at the highest experimental SO2 concentrations of 6 ppb, although none was found at lower concentrations. Overall, the exploratory measurements provide suggestive evidence for ion-induced nucleation or ion-ion recombination as sources of aerosol particles. However in order to quantify the conditions under which ion processes become significant, improvements are needed in controlling the experimental variables and in the reproducibility of the experiments. Finally, concerning technical aspects, the most important lessons for the CLOUD design include the stringent requirement of internal cleanliness of the aerosol chamber, as well as maintenance of extremely stable temperatures (variations below 0.1 °C
Exchange Rate Dynamics in a Model with Staggered Wage Contracts

A Dornbusch model with overlapping multi-period wage contracts and rational expectations is specified and estimated with Canadian and U.S. data. Estimated wage-price dynamics imply a typical contract length of about six quarters. Simulations indicate: 1) the exchange rate overshoots following a permanent money supply shock; 2) managed floating exacerbates the impact of foreign business cycles on the Canadian economy; 3) anticipated cold-turkey disinflation is fairly cheap in terms of lost output.
A novel approach of Structural Health Monitoring by the application of FFT and wavelet transform using an index of frequency dispersion

Monitoring the state of a structure provides the ability to anticipate structural failures and secure the safe use of a structure. The ability to reveal valuable information from the monitoring data is many times difficult and without result due to the fact that there are many limitations in terms of defining when a structure becomes inappropriate for use, when the data reveal damage, when the structural response of a structure has non-linear characteristics which could hide damage. This work suggests a novel approach in structural health monitoring by measuring the dispersion of the fundamental frequency of a structure. We apply fast furrier transform and wavelet transform in SHM data and proposed a novel index, which measures the dispersion of the building’s fundamental frequency. This approach focuses on the frequency dispersion and presents its variability with and without structural damage and try to reveal nonlinear characteristics of SHM data from a building under seismic or man-made excitation in order to work as an index of a probable structural damage. The suggested approach is applied in recorded accelerometer data, from a wired SHM network established in two neighbouring buildings of different age in the city of Chania, in Crete. The recording data were created by seismic activity, weather conditions and man-made activity. Keywords— digital signal processing, structural health monitoring, acceleration recordings, wavelets, Fast Fourier Transform.
Discovering the Patterns of Human Interaction in Meetings using Tree Based Mining

Discovering semantic learning is noteworthy for comprehension and translating how individuals collaborate in a meeting exchange. In this paper, we propose a mining technique to remove regular examples of human connection taking into account the caught substance of eye to eye gatherings. Human cooperation’s, for example, proposing a thought, giving remarks, and communicating a positive assessment, demonstrate client aim toward a point or part in a talk. Human cooperation stream in an exchange session is spoken to as a tree. Tree based communication mining calculations are intended to investigate the structures of the trees and to concentrate association stream designs. The trial results demonstrate that we can effectively separate a few fascinating examples that are valuable for the elucidation of human conduct in meeting talks, for example, deciding regular communications, commonplace cooperation streams, and connections between various sorts of collaborations.
Effect of Climate Change on Hydrology, Sediment and Nutrient Losses in Two Lowland Catchments in Poland

Future climate change is projected to have significant impact on water resources availability and quality in many parts of the world. The objective of this paper is to assess the effect of projected climate change on water quantity and quality in two lowland catchments (the Upper Narew and the Barycz) in Poland in two future periods (near future: 2021–2050, and far future: 2071– 2100). The hydrological model SWAT was driven by climate forcing data from an ensemble of nine bias-corrected General Circulation Models—Regional Climate Models (GCM-RCM) runs based on the Coordinated Downscaling Experiment—European Domain (EURO-CORDEX). Hydrological response to climate warming and wetter conditions (particularly in winter and spring) in both catchments includes: lower snowmelt, increased percolation and baseflow and higher runoff. Seasonal differences in the response between catchments can be explained by their properties (e.g., different thermal conditions and soil permeability). Projections suggest only moderate increases in sediment loss, occurring mainly in summer and winter. A sharper increase is projected in both catchments for TN losses, especially in the Barycz catchment characterized by a more intensive agriculture. The signal of change in annual TP losses is blurred by climate model uncertainty in the Barycz catchment, whereas a weak and uncertain increase is projected in the Upper Narew catchment.
Modular Circuitry and Network Dynamics for the Formation of Visuospatial Working Memory in the Primate Prefrontal Cortex

A model of the prefrontal cortical circuit has been constructed to investigate the dynamics for working memory processing. The model circuit is multi-layered and consists of a number of circuit modules or columns, each of which has local, excitatory and inhibitory connections as well as feedback connections. The columns interact with each other via the longrange horizontal connections. Besides these intrinsic connections, the pyramidal and spiny cells in the superficial layers receive the specific cue-related input and all the cortical neurons receive a hypothetical bias input. The model cortical circuit amplifies the response to the transient, cue-related input. The dynamics of the circuit evolves autonomously after the termination of the input. As a result, the circuit reaches in several hundred milliseconds an equilibrium state, in which the neurons exhibit graded-level, sustained activity. The sustained activity varies gradually with the cue direction, thus forming memory fields. In the formation of the memory fields, the feedback connections, the horizontal connections, and the bias input all play important roles. Varying the level of the bias input dramatically changes the dynamics of the model cortical neurons. The computer simulations show that there is an optimum level of the input for the formation of well-defined memory fields during the delay period. key words: working memory, memory field, prefrontal cortex, column, closed-loop, neural circuit
Compulsive Buying Behavior: Clinical Comparison with Other Behavioral Addictions

Compulsive buying behavior (CBB) has been recognized as a prevalent mental health disorder, yet its categorization into classification systems remains unsettled. The objective of this study was to assess the sociodemographic and clinic variables related to the CBB phenotype compared to other behavioral addictions. Three thousand three hundred and twenty four treatment-seeking patients were classified in five groups: CBB, sexual addiction, Internet gaming disorder, Internet addiction, and gambling disorder. CBB was characterized by a higher proportion of women, higher levels of psychopathology, and higher levels in the personality traits of novelty seeking, harm avoidance, reward dependence, persistence, and cooperativeness compared to other behavioral addictions. Results outline the heterogeneity in the clinical profiles of patients diagnosed with different behavioral addiction subtypes and shed new light on the primary mechanisms of CBB.
Algorithms and Data Design Issues for Basic NLP Tools

This chapter presents some of the basic language engineering preprocessing steps (tokenization, part-of-speech tagging, lemmatization, and sentence and word alignment). Tagging is among the most important processing steps and its accuracy significantly influences any further processing. Therefore, tagset design, validation and correction of training data and the various techniques for improving the tagging quality are discussed in detail. Since sentence and word alignment are prerequisite operations for exploiting parallel corpora for a multitude of purposes such as machine translation, bilingual lexicography, import annotation etc., these issues are also explored in detail.
Image enhancement by scaling the discrete wavelet coefficients based on segmentation

Histogram Equalization is one of the most effective technique to process the digital images over the years used for the purpose of image enhancement and normalization. But in some cases traditional Histogram Equalization (HE) may cause bad results. There is a remedy given in this paper which takes over the traditional HE. This paper presents a technique based on segmented histogram for contrast enhancement of the image by scaling the discrete wavelet transform coefficient. This utilizes the advantage of wavelet transform and preserves the color consistency in addition to improving the overall contrast of the image. This approach efficiently reduced the contaminated noise in the image by introducing wavelet shrinkage terms adaptively in the different scales. Experimental results and comparison is made between a number of techniques on the basis of Absolute mean brightness error (AMBE), PSNR, Entropy and Standard Deviation values.