Miracle-dz/UniFAR-FTUs huggingface dataset:
Choice of normal ovarian control influences determination of differentially expressed genes in ovarian cancer expression profiling studies.

PURPOSE As with many cancers thought to be of epithelial origin, expression profiling studies of ovarian cancer have relied on a variety of sources of normal cells for comparison with tumors, including whole ovary samples (WO), ovarian surface epithelium (OSE) exposed to short-term culture, and immortalized OSE cell lines (IOSE). Our purpose was to assess the impact of the use of different types of normal controls on the determination of gene expression alterations in ovarian cancer studies. EXPERIMENTAL DESIGN We compared the gene expression profiles generated on an 11,000-element cDNA microarray of OSE brushings, whole ovary samples, short-term cultures of normal OSE, SV40 large T antigen-immortalized OSE cell lines, and telomerase-immortalized OSE cell lines. The function of the groups as normal controls was then assessed by separate comparisons of each group to a set of 24 serous ovarian carcinoma samples. RESULTS The normal groups formed robust, distinct clusters in hierarchical clustering and multidimensional scaling. The Pearson correlation coefficient for all combinations of any two of the groups ranged from 0.04 to 0.54, emphasizing the disparity of the groups. In the gene lists produced by comparing each normal group with the ovarian cancer samples, the majority of genes were unique to that normal-cancer comparison, with no gene appearing on all five lists. CONCLUSIONS These results suggest that the selection of a normal control to compare with epithelial ovarian cancer samples in microarray studies strongly influences the genes that are identified as differentially expressed and complicates comparison with studies using a different normal control.
USENIX Association Proceedings of the First Symposium on Networked Systems Design and Implementation

This paper focuses on load management in looselycoupled federated distributed systems. We present a distributed mechanism for moving load between autonomous participants using bilateral contracts that are negotiated offline and that set bounded prices for moving load. We show that our mechanism has good incentive properties, efficiently redistributes excess load, and has a low overhead in practice. Our load management mechanism is especially wellsuited for distributed stream-processing applications, an emerging class of data-intensive applications that employ a “continuous query processing” model. In this model, streams of data are processed and composed continuously as they arrive rather than after they are indexed and stored. We have implemented the mechanism in the M dusadistributed stream processing system, and we demonstrate its properties using simulations and experiments.
Brain Mechanisms of Persuasion: How "Expert Power" Modulates Memory and Attitudes

Human behaviour is affected by various forms of persuasion. The general persuasive effect of high expertise of the communicator, often referred to as 'expert power', is well documented. We found that a single exposure to a combination of an expert and an object leads to a long-lasting positive effect on memory for and attitude towards the object. Using functional magnetic resonance imaging, we probed the neural processes predicting these behavioural effects. Expert context was associated with distributed left-lateralized brain activity in prefrontal and temporal cortices related to active semantic elaboration. Furthermore, experts enhanced subsequent memory effects in the medial temporal lobe (i.e. in hippocampus and parahippocampal gyrus) involved in memory formation. Experts also affected subsequent attitude effects in the caudate nucleus involved in trustful behaviour, reward processing and learning. These results may suggest that the persuasive effect of experts is mediated by modulation of caudate activity resulting in a re-evaluation of the object in terms of its perceived value. Results extend our view of the functional role of the dorsal striatum in social interaction and enable us to make the first steps toward a neuroscientific model of persuasion.
Performance analysis of rain attenuation at Ku-band in Malaysia

Due to the lack of reliable analysis in Malaysia as tropical region, this study presents an analysis of experimental data compared against existing rain attenuation prediction models, namely the Dissanayake Allnut Haidara (DAH), and International Telecommunication Union rain ITU-R models, which have been used previously in satellite communication systems. Experimental data was measured at Universiti Putra Malaysia, Selangor, by retrieving signals from the MEASAT-3 satellite, which is geostationary at 91.5°E; at 11.096 GHz. Serdang Selangor is the southern-most state of the Kuala Lumpur region of Malaysia. Data analysis was conducted in two ways. Firstly, by performing statistical analysis on rain attenuation prediction models; and secondly, by making a comparison between measured data and the existing rain attenuation prediction model. Of all, the ITU-R model gave the lowest real mean square value of (2.2) for the three chosen states in Malaysia.
Silent fMRI acquisition methods for large acoustic noise during scan.

Functional magnetic resonance imaging (fMRI) is now the method of choice for studying neural correlates of various tasks in normal subjects as well as patients. This method, however, is inevitably coupled with the acoustic noise produced during the image acquisition process. This is a problem not only in auditory experiments but also in other cognitive tasks in general. The problems caused by such noise are modulation of auditory activation, impaired perception of auditory stimuli, and deterioration of task performance possibly due to stress from the abnormal circumstances. While both hardware and software solutions have been reported, several methods are introduced here that focus on software solutions that can be implemented in scanners already installed. Their advantages and disadvantages differ depending on the kinds of tasks involved, i.e. whether block design or event-related design, and they are discussed with a view to better utilization.
Sparse Submodular Probabilistic PCA

We propose a novel approach for sparse probabilistic principal component analysis, that combines a low rank representation for the latent factors and loadings with a novel sparse variational inference approach for estimating distributions of latent variables subject to sparse support constraints. Inference and parameter estimation for the resulting model is achieved via expectation maximization with a novel variational inference method for the E-step that induces sparsity. We show that this inference problem can be reduced to discrete optimal support selection. The discrete optimization is submodular, hence, greedy selection is guaranteed to achieve 1-1/e fraction of the optimal. Empirical studies indicate eectiveness of the proposed approach for the recovery of a parsimonious decomposition as compared to established baseline methods. We also evaluate our method against state-of-the-art methods on high dimensional fMRI data, and show that the method performs as well as or better than other methods.
Health Records on the Cloud : A Security Framework

The current study investigates the process of selecting a cloud service provider for implementing an electronic health record (EHR) system on the cloud from a security standpoint. This is an important issue because many eligible physicians still do not use EHRs and thus cannot meet the meaningful use criteria and receive incentives provided by the Centers for the Medicare and Medicaid Services (CMS). To facilitate the process of selecting a cloud provider, we propose a framework focusing on the security issues related to implementing EHR systems on the cloud. The framework targets evaluators from healthcare practitioners and is designed to be a comprehensive tool for improving decisionmaking. The current study contributes to the literature as it utilizes design science methods to healthcare information systems and provides an overview of the current state of EHR, cloud computing, and security. The study also discusses many practical implications and recommendations for healthcare.
A two-stage classifier for identification of protein-protein interface residues

MOTIVATION The ability to identify protein-protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. RESULTS We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein-protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.
Explorer Autofolding for Source Code Summarization

Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code folding, which allows one to selectively hide blocks of code. However this is impractical to use as folding decisions must be made manually or based on simple rules. We introduce the autofolding problem, which is to automatically create a code summary by folding less informative code regions. We present a novel solution by formulating the problem as a sequence of AST folding decisions, leveraging a scoped topic model for code tokens. On an annotated set of popular open source projects, we show that our summarizer outperforms simpler baselines, yielding a 28% error reduction. Furthermore, we find through a case study that our summarizer is strongly preferred by experienced developers. More broadly, we hope this work will aid program comprehension by turning code folding into a usable and valuable tool.
Determinants of pain and functioning in knee osteoarthritis: a one-year prospective study

Objective: To identify predictors of pain and disability in knee osteoarthritis. Design: A one-year prospective analysis of determinants of pain and functioning in knee osteoarthritis. Study setting: Primary care providers in a medium-sized city. Patients: A total of 111 patients aged from 35 to 75 with clinical symptoms and radiographic grading (Kellgren-Lawrence 2–4) of knee osteoarthritis who participated in a randomized controlled trial. Main measures: The outcome measures were self-reported pain and function, which were recorded at 0, 3 and 12 months. Disease-specific pain and functioning were assessed using the pain and function subscales of the Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index. Generic physical and mental functioning were assessed using the RAND-36 subscales for function, and physical and mental component summary scores. Possible baseline predictors for these outcomes were 1) demographic, socioeconomic and disease-related variables, and 2) psychological measures of resources, distress, fear of movement and catastrophizing. Results: Multivariate linear mixed model analyses revealed that normal mood at baseline measured with the Beck Anxiety Inventory predicted significantly better results in all measures of pain (WOMAC P=0.02) and function (WOMAC P=0.002, RAND-36 P=0.002) during the one-year follow-up. Psychological resource factors (pain self-efficacy P=0.012, satisfaction with life P=0.002) predicted better function (RAND-36). Pain catastrophizing predicted higher WOMAC pain levels (P=0.013), whereas fear of movement (kinesiophobia) predicted poorer functioning (WOMAC P=0.046, RAND-36 P=0.024). Conclusions: Multiple psychological factors in people with knee osteoarthritis pain are associated with the development of disability and longer term worse pain.
Fast interactive real-time volume rendering of real-time three-dimensional echocardiography: an implementation for low-end computers

Real-time three-dimensional echocardiography (RT3DE) is an innovative cardiac imaging modality. However, partly due to lack of user-friendly software, RT3DE has not been widely accepted as a clinical tool. The object of this study was to develop and implement a fast and interactive volume renderer of RT3DE datasets designed for a clinical environment where speed and simplicity are not secondary to accuracy. Thirty-six patients (20 regurgitation, 8 normal, 8 cardiomyopathy) were imaged using RT3DE. Using our newly developed software, all 3D data sets were rendered in real-time throughout the cardiac cycle and assessment of cardiac function and pathology was performed for each case. The real-time interactive volume visualization system is user friendly and instantly provides consistent and reliable 3D images without expensive workstations or dedicated hardware. We believe that this novel tool can be used clinically for dynamic visualization of cardiac anatomy.
Minimal residual disease assessment in childhood acute lymphoblastic leukaemia: a Swedish multi‐centre study comparing real‐time polymerase chain reaction and multicolour flow cytometry

Minimal residual disease (MRD) assessment is a powerful prognostic factor for determining the risk of relapse in childhood acute lymphoblastic leukaemia (ALL). In this Swedish multi‐centre study of childhood ALL diagnosed between 2002 and 2006, the MRD levels were analysed in 726 follow‐up samples in 228 children using real‐time quantitative polymerase chain reaction (RQ‐PCR) of rearranged immunoglobulin/T‐cell receptor genes and multicolour flow cytometry (FCM). Using an MRD threshold of 0·1%, which was the sensitivity level reached in all analyses, the concordance between RQ‐PCR and FCM MRD values at day 29 was 84%. In B‐cell precursor ALL, an MRD level of ≥0·1% at day 29 predicted a higher risk of bone marrow relapse (BMR) with both methods, although FCM was a better discriminator. However, considering the higher median MRD values achieved with RQ‐PCR, a higher MRD cut‐off (≥0·2%) improved the predictive capacity of RQ‐PCR. In T‐ALL, RQ‐PCR was notably superior to FCM in predicting risk of BMR. That notwithstanding, MRD levels of ≥0·1%, detected by either method at day 29, could not predict isolated extramedullary relapse. In conclusion, the concordance between RQ‐PCR and FCM was high and hence both methods are valuable clinical tools for identifying childhood ALL cases with increased risk of BMR.
Personality Trait Classification via Co-Occurrent Multiparty Multimodal Event Discovery

This paper proposes a novel feature extraction framework from mutli-party multimodal conversation for inference of personality traits and emergent leadership. The proposed framework represents multi modal features as the combination of each participant's nonverbal activity and group activity. This feature representation enables to compare the nonverbal patterns extracted from the participants of different groups in a metric space. It captures how the target member outputs nonverbal behavior observed in a group (e.g. the member speaks while all members move their body), and can be available for any kind of multiparty conversation task. Frequent co-occurrent events are discovered using graph clustering from multimodal sequences. The proposed framework is applied for the ELEA corpus which is an audio visual dataset collected from group meetings. We evaluate the framework for binary classification task of 10 personality traits. Experimental results show that the model trained with co-occurrence features obtained higher accuracy than previously related work in 8 out of 10 traits. In addition, the co-occurrence features improve the accuracy from 2 % up to 17 %.
A Two-tiered compensatory response to loss of DNA repair modulates aging and stress response pathways

Activation of oxidative stress-responses and downregulation of insulin-like signaling (ILS) is seen in Nucleotide Excision Repair (NER) deficient segmental progeroid mice. Evidence suggests that this is a survival response to persistent transcription-blocking DNA damage, although the relevant lesions have not been identified. Here we show that loss of NTH-1, the only Base Excision Repair (BER) enzyme known to initiate repair of oxidative DNA damage inC. elegans, restores normal lifespan of the short-lived NER deficient xpa-1 mutant. Loss of NTH-1 leads to oxidative stress and global expression profile changes that involve upregulation of genes responding to endogenous stress and downregulation of ILS. A similar, but more extensive, transcriptomic shift is observed in the xpa-1 mutant whereas loss of both NTH-1 and XPA-1 elicits a different profile with downregulation of Aurora-B and Polo-like kinase 1 signaling networks as well as DNA repair and DNA damage response genes. The restoration of normal lifespan and absence oxidative stress responses in nth-1;xpa-1 indicate that BER contributes to generate transcription blocking lesions from oxidative DNA damage. Hence, our data strongly suggests that the DNA lesions relevant for aging are repair intermediates resulting from aberrant or attempted processing by BER of lesions normally repaired by NER.
Performance analysis of MAC protocol and Routing protocol for Wireless Sensor Networks

During the decade, wireless sensor networks have gained increasing attention from both the research community as well as actual users. The most critical aspects of wireless sensor networks is to face concern how to reduce the energy consumption of nodes due to fact that sensor nodes are generally battery-powered devices in order to extend the network lifetime to reasonable times. Hence in this paper we are presenting the model of microsesnor node and energy model in order to verify the various aspects of minimizing the energy consumption of sensor nodes according to applications. Considering the heterogeneous aspect of a sensor node, the developed model allows comparing different node configurations in order to make the best choice of components according to the specifications of the application. The experimental evaluations of this approach will carried out through the NS2 tool in which it is designed to the WSN for various applications or network sizes and analyze its performance by applying the energy models with different configuration parameters of transmitter node and receiver node. In this project we have evaluated four MAC protocols (802.11, TMAC) with two routing protocols (AODV and DSDV) in terms of all energy parameters.
The effect of ecosystem biodiversity on virus genetic diversity depends on virus species: A study of chiltepin-infecting begomoviruses in Mexico

Current declines in biodiversity put at risk ecosystem services that are fundamental for human welfare. Increasing evidence indicates that one such service is the ability to reduce virus emergence. It has been proposed that the reduction of virus emergence occurs at two levels: through a reduction of virus prevalence/transmission and, as a result of these epidemiological changes, through a limitation of virus genetic diversity. Although the former mechanism has been studied in a few host-virus interactions, very little is known about the association between ecosystem biodiversity and virus genetic diversity. To address this subject, we estimated genetic diversity, synonymous and non-synonymous nucleotide substitution rates, selection pressures, and frequency of recombinants and re-assortants in populations of Pepper golden mosaic virus (PepGMV) and Pepper huasteco yellow vein virus (PHYVV) that infect chiltepin plants in Mexico. We then analyzed how these parameters varied according to the level of habitat anthropization, which is the major cause of biodiversity loss. Our results indicated that genetic diversity of PepGMV (but not of PHYVV) populations increased with the loss of biodiversity at higher levels of habitat anthropization. This was mostly the consequence of higher rates of synonymous nucleotide substitutions, rather than of adaptive selection. The frequency of recombinants and re-assortants was higher in PepGMV populations infecting wild chiltepin than in those infecting cultivated ones, suggesting that genetic exchange is not the main mechanism for generating genetic diversity in PepGMV populations. These findings provide evidence that biodiversity may modulate the genetic diversity of plant viruses, but it may differentially affect even two closely related viruses. Our analyses may contribute to understanding the factors involved in virus emergence.
The comparison of the psychological skills in sport trainers and recreational table tennis players

The main aim of the study was to compare the differences and correlations in psychological skills important for the sport achievement in two samples: 86 Croatian trainers of various sports and 86 recreational table tennis players, using adjusted Dutch Youth Version of the Psychological Skills Inventory for Sports (PSIS-Youth). The relationship among different psychological skills are established separately on each sample, according the education level, age, league, position on the ranking list, years of playing. The differences in psychological skills are analysed according to different independent variables. Separate correlation matrices for the sport trainers and recreational table tennis players indicate similar trends in both studied samples. Variables such as degree, age, rank of league, position on the ranking list, years of playing low correlated with psychological skills. Among all the tested psychological skills, motivation is the most important psychological skill that distinguishes athletes and coaches in most of the independent variables: coaches who have completed their athletic career voluntarily or because of lack of further ambitions have more pronounced motivation.
Combination Antifungal Therapy in Treatment of Murine Pulmonary Mucormycosis: Roles of Quinolones and Azoles

ABSTRACT Amphotericin B is the only recognized antifungal used in the treatment of mucormycosis. In this study, we evaluated various combinations of amphotericin B, fluconazole, and trovafloxacin or ciprofloxacin in the treatment of murine pulmonary mucormycosis. The combination of fluconazole and a quinolone has a marked effect on the outcome of murine pulmonary mucormycosis. Even though we did not optimize therapy with the drugs, these experiments suggest that azoles, especially fluconazole, in combination with either trovafloxacin or ciprofloxacin were effective in the treatment of this aggressive mycosis in the mouse model.
Collective Personal Profile Summarization with Social Networks

Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many interesting applications, such as talent recommendation and contextual advertising. However, personal profiles usually lack organization confronted with the large amount of available information. Therefore, it is always a challenge for people to find desired information from them. In this paper, we address the task of personal profile summarization by leveraging both personal profile textual information and social networks. Here, using social networks is motivated by the intuition that, people with similar academic, business or social connections (e.g. co-major, co-university, and cocorporation) tend to have similar experience and summaries. To achieve the learning process, we propose a collective factor graph (CoFG) model to incorporate all these resources of knowledge to summarize personal profiles with local textual attribute functions and social connection factors. Extensive evaluation on a large-scale dataset from LinkedIn.com demonstrates the effectiveness of the proposed approach. *