Miracle-dz/UniFAR-FTUs huggingface dataset:
CMD : A Multidimensional Declustering Method for Parallel Database Systems 1

I/O parallelism appears to be a promising approach to achieving high performance in parallel database systems. In such systems, it is essential to decluster database les into fragments and spread them across multiple disks so that the DBMS software can exploit the I/O bandwidth reading and writing the disks in parallel. In this paper, we consider the problem of declustering multidimensional data on a parallel disk system. Since the multidimensional range query is the main work-horse for applications accessing such data, our aim is to provide e cient support for it. A new declustering method for parallel disk systems, called coordinate modulo distribution (CMD), is proposed. Our analysis shows that the method achieves optimum parallelism for a very high percentage of range queries on multidimensional data, if the distribution of data on each dimension is stationary. We have derived the exact conditions under which optimality is achieved. Also provided are the worst and average case bounds on multidimensional range query performance. Experimental results show that the method achieves near optimum performance in almost all cases even when the stationarity assumption does not hold. Details of the parallel algorithms for range query processing and data maintenance are also provided.
Use of Baculovirus BacMam Vectors for Expression of ABC Drug Transporters in Mammalian Cells

ATP-binding cassette (ABC) drug transporters ABCB1 [P-glycoprotein (Pgp)] and ABCG2 are expressed in many tissues including those of the intestines, the liver, the kidney and the brain and are known to influence the pharmacokinetics and toxicity of therapeutic drugs. In vitro studies involving their functional characteristics provide important information that allows improvements in drug delivery or drug design. In this study, we report use of the BacMam (baculovirus-based expression in mammalian cells) expression system to express and characterize the function of Pgp and ABCG2 in mammalian cell lines. BacMam-Pgp and BacMam-ABCG2 baculovirus-transduced cell lines showed similar cell surface expression (as detected by monoclonal antibodies with an external epitope) and transport function of these transporters compared to drug-resistant cell lines that overexpress the two transporters. Transient expression of Pgp was maintained in HeLa cells for up to 72 h after transduction (48 h after removal of the BacMam virus). These BacMam-baculovirus-transduced mammalian cells expressing Pgp or ABCG2 were used for assessing the functional activity of these transporters. Crude membranes isolated from these cells were further used to study the activity of these transporters by biochemical techniques such as photo-cross-linking with transport substrate and adenosine triphosphatase assays. In addition, we show that the BacMam expression system can be exploited to coexpress both Pgp and ABCG2 in mammalian cells to determine their contribution to the transport of a common anticancer drug substrate. Collectively, these data demonstrate that the BacMam-baculovirus-based expression system can be used to simultaneously study the transport function and biochemical properties of ABC transporters.
Emotion and Virality: What Makes Online Content Go Viral?

Abstract "Companies are relying more and more on online communication to reach consumers. While some viral campaigns are tremendously successful, others remain far below expectations. But why are certain pieces of online content more viral than others? An analysis conducted on the New York Times’ most-emailed list, along with further experimental evidence, showed that positive content is more viral than negative content. However, the relationship between emotion and social transmission is more complex than valence alone. Virality is driven, in part, by activation and arousal. Content that evokes either high-arousal positive emotions (awe) or negative emotions (anger or anxiety) tends to be more viral. Content that evokes low arousal or deactivating emotions (e.g., sadness) tends to be less viral. These results were also true when examining how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content is featured). Taking the effect of emotions into account helps to design effective viral marketing campaigns. "
Introduction for the Special Issue on the Blood-Brain Barrier

The blood-brain barrier (BBB) serves to maintain brain homeostasis, which is crucial for the functionality of neuronal networks, and thus the regulation of body functions. It consists of a polarized monolayer of brain microvascular endothelial cells that separates the blood from the brain. The BBB tightly regulates the transport of substances between blood and brain, serving brain homeostasis but –at the same time- hindering the treatment of brain diseases by precluding the entry of therapeutics. An improved understanding of the natural transport pathways that exist at the BBB in both health and disease may help to design methods for the transfer of therapeutic and toxic compounds into and out of the brain, respectively, for the treatment of CNS diseases. For this purpose, in vitro BBB models that accurately represent the physiological situation in human are desired. Several lines of research need to converge in order to study and understand brain diseases, to develop proper in vitro BBB model systems, and to develop treatment strategies. This Special Issue of Tissue Barriers is dedicated to the Blood-Brain Barrier (BBB). It contains 11 reviews that altogether summarize and discuss recent advances in understanding normal structure, development and functions of the BBB and the mechanisms underlying disruption of this barrier in CNS diseases. In addition, potential therapeutic interventions and gateways for drug delivery into the brain, including strategies to bypass the BBB and strategies to cross the BBB, are discussed. Challenges in the development of physiologically relevant in vitro BBB model systems, including BBB-on-a-chip, and challenges in the imaging of nanoparticle transport at the BBB are revealed. The first article of this special issue, by Stamatovic et al., provides an in depth review of the molecular composition of the junctional protein complexes, i.e., tight junctions (TJs), adherens junctions (AJs) and gap junctions (GJs), that are present at the BBB, and how the redistribution of junctional proteins leads to BBB dysfunction. 1 Typically, redistribution of junctional proteins directly affects barrier integrity by enhancing paracellular leakage. Interestingly, the redistribution of junctional proteins may also lead to new functions such as in the case of JAM-A that, following its translocation to the luminal membrane of the BBB, functions as a receptor for leucocyte infiltration, thereby indirectly enhancing barrier permeability. An emerging field is the role of redistributed junctional proteins in cell signaling processes that may aggravate barrier dysfunction, although it should be noted that at present most evidence for this new hypothesis is obtained from studying epithelial and not endothelial cells.
Brain activity during driving with distraction: an immersive fMRI study

Introduction: Non-invasive measurements of brain activity have an important role to play in understanding driving ability. The current study aimed to identify the neural underpinnings of human driving behavior by visualizing the areas of the brain involved in driving under different levels of demand, such as driving while distracted or making left turns at busy intersections. Materials and Methods: To capture brain activity during driving, we placed a driving simulator with a fully functional steering wheel and pedals in a 3.0 Tesla functional magnetic resonance imaging (fMRI) system. To identify the brain areas involved while performing different real-world driving maneuvers, participants completed tasks ranging from simple (right turns) to more complex (left turns at busy intersections). To assess the effects of driving while distracted, participants were asked to perform an auditory task while driving analogous to speaking on a hands-free device and driving. Results: A widely distributed brain network was identified, especially when making left turns at busy intersections compared to more simple driving tasks. During distracted driving, brain activation shifted dramatically from the posterior, visual and spatial areas to the prefrontal cortex. Conclusions: Our findings suggest that the distracted brain sacrificed areas in the posterior brain important for visual attention and alertness to recruit enough brain resources to perform a secondary, cognitive task. The present findings offer important new insights into the scientific understanding of the neuro-cognitive mechanisms of driving behavior and lay down an important foundation for future clinical research.
A Novel Technique for Call Graph Reduction for Bug Localization

era, software industries are competing for software quality which depends upon the sound software testing phase. To deliver the quality product the most challenging job for software industry is to localize bugs automatically and fix them before release. One of the techniques for automated bug localization is usage of call graph. Since size of the call graph generated is quite large, various call reduction approaches have been proposed. In this paper a novel approach for call graph reduction has been proposed where the size of the call graph is reduced without changing the basic structure and no major loss of the information is incurred. The output generated using the proposed methodology shows promising results.
Questionnaire Construction Manual. Annex: Literature Survey and Bibliography.

Abstract : A literature survey on questionnaire construction encompassed journals, books, and reports in psychology, sociology, education, and marketing, and documentation published by the Defense Department. The search yielded over 2,000 references, which are listed in the 279-page annotated bibliography. A synthesis of the findings is based on abstracts selected for their relevance from the references. Results of the search were the basis for developing a manual on questionnaire construction designed for Army personnel responsible for field evaluations.
Obtaining calibrated probability estimates from support vector classifiers : project proposal

In many supervised learning tasks a learned classifier automatically induces a ranking of test examples, making it possible to determine the relative likelihood that a given test example belongs to a certain class. However, for many applications this ranking is not sufficient, particularly when the classification decision is cost-sensitive. In this case, it is necessary to convert the outputs of the classifier into well-calibrated posterior probabilities. A recent paper that addresses this problem is [7], which introduces new methods for estimating the probabilities from naive Bayes and decision tree classifiers. The goal of this project is to replicate that work using Support Vector Machines (SVMs).
Temporal Discrete Cosine Transform : Towards Longer Term Temporal Features for Speaker Verification

In this paper, we propose the temporal discrete cosine transform (TDCT) feature for the speaker verification task. The TDCT feature captures temporal information from a longer time context beyond the conventional delta and double-delta coefficients. We evaluate the effectiveness of the TDCT feature on the NIST 2001, NIST 2004, and NIST 2005 speaker recognition benchmark corpora by using a standard GMM-UBM recognizer. We compare our results against the standard MFCC+Δ+ΔΔ front end, and with the shifted delta cepstrum (SDC) feature which is commonly used in the language identification task. The results indicate that the TDCT and SDC give similar accuracy, and that the TDCT feature outperforms MFCC+Δ+ΔΔ in most of the cases.
Interferon-gamma release assays versus tuberculin skin testing for detection of latent tuberculosis in chronic haemodialysis patients.

BACKGROUND End stage renal disease increases the risk of reactivating latent tuberculosis (LTBI). Interferon-gamma release assays (IGRA) are an alternative to the tuberculin skin test (TST) for detecting LTBI. METHODS Sixty-two hemodialysis patients (46 male, 16 female, aged 65 +/- 15 years) from 3 hemodialysis facilities in the Geneva area were submitted to a TST, 2 IGRA (T-SPOT.TB and QuantiFERON Gold in tube: QFT), a chest radiography, and a questionnaire to record social status, country of birth, history of prior TST, tuberculosis (TB), BCG (Bacillus of Calmette-Guérin vaccine), and any cause of immuno-suppression. LTBI was defined as prior "at risk" contact with a case of contagious TB and/or a chest X-ray suggestive of prior TB infection. RESULTS Positivity rate was 19% for TST, 21% for QFT and 29% for T-SPOT-TB; 8% of QFT and 11% of T-SPOT-TB were indeterminate. Agreement between IGRA was fair (kappa = 0.60). After adjusting for age and BCG, OR (Odds Ratio) of having a positive QFT was 4.6-fold (p = 0.029) higher in patients with LTBI vs. those without LTBI. In contrast, no association was found between LTBI and having a positive T-SPOT.TB or a positive TST. As expected, there was a strong association between prior BCG vaccination and having a positive TST (OR 5.3, p = 0.017). QFT was the only test with a significant OR of having LTBI (adjusted OR: 4.4; 95%CI: 1.1 - 17.6; p = 0.034). Among 5 patients with definite prior TB, TST and T-SPOT.TB were positive in 1 and QFT, in 2. CONCLUSIONS In this population, QFT was superior to TST for detecting LTBI, but both IGRAs and TST have important limitations, and are unreliable for screening for LTBI.
Journal of Insect Science

A single gene (yes) controls pigmentation of eyes and scales in Heliothis virescens. Abstract A yellow-eyed mutant was discovered in a strain of Heliothis virescens, the tobacco budworm, that already exhibited a mutation for yellow scale, y. We investigated the inheritance of these visible mutations as candidate markers for transgenesis. Yellow eye was controlled by a single, recessive, autosomal factor, the same type of inheritance previously known for y. Presence of the recombinant mutants with yellow scales and wild type eyes in test crosses indicated independent segregation of genes for these traits. The recombinant class with wild type scales and yellow eyes was completely absent and there was a corresponding increase of the double mutant parental class having yellow scales and yellow eyes. These results indicated that a single factor for yellow eye also controlled yellow scales independently of y. This gene was named yes, for yellow eye and scale. We hypothesize that yes controls both eye and scale color through a deficiency in transport of pigment precursors in both the ommochrome and melanin pathways. The unlinked gene y likely controls an enzyme affecting the melanin pathway only. Both y and yes segregated independently of AceIn, acetylcholinesterase insensitivity, and sodium channel hscp, which are genes related to insecticide resistance. Abbreviation: hscp a gene coding for the Heliothis sodium ion channel protein AceIn a gene controlling decreased sensitivity of acetylcholinesterase to inhibition by propoxur or methyl paraoxon y a gene conferring yellow scale color ye a gene conferring yellow eye color and yellow scale color Yyes a strain of Heliothis virescens with yellow scales later selected for yellow eyes and scales. Dalzell98 a strain of Heliothis virescens collected in Dalzell, South Carolina in 1998 Ace-R a strain of Heliothis virescens fixed for AceIn
Inducing Proactive Control Shifts in the AX-CPT

The Dual Mechanisms of Control (DMC) account (Braver, 2012) proposes two distinct mechanisms of cognitive control, proactive and reactive. This account has been supported by a large number of studies using the AX-CPT paradigm that have demonstrated not only between-group differences, but also within-subjects variability in the use of the two control mechanisms. Yet there has been little investigation of task manipulations that can experimentally modulate the use of proactive control in healthy young adults; such manipulations could be useful to better understand the workings of cognitive control mechanisms. In the current study, a series of three experiments demonstrate how individuals can be systematically biased toward and away from the utilization of proactive control, via strategy training and no-go manipulations, respectively. These results provide increased support for the DMC framework, and provide a new basis from which to examine group-based differences and neural mechanisms underlying the two control modes.
Good clinical practice in clinical interventional studies

Good clinical practice (GCP) guidelines should always be implemented and obeyed in clinical interventional studies. In this mini-review, we will address several burning questions relating to GCP in a concise ‘frequently asked questions’ format. While compliance to current rules and regulations is our mission, we also wish to play devil's advocate attempting to translate the rules into sizeable chunks using a high dose of common sense.
Accident Black Spot Identification and Verification through Social Media

Twitter, the popular microblogging platform, has more than five hundred million registered users -- so called Tweeters. These Tweeters generate a large amount of information every day. This information, whilst noisy and diverse is so voluminous that it can provide a key source of information that can support a range of research activities. Transport and traffic accidents are the examples chosen here. At the same time, Melbourne has been named the world's most liveable city, but this city continues to face challenges caused by the continuous population growth and the issues facing the transport systems caused by increased traffic volumes. This has implications for road safety. The Victorian government has implemented a transportation accident black spot programme however this information is largely historic in nature and does not capture real time traffic accidents. We have designed a Cloud-based software system that correlates historic accident black spot information with Twitter and uses this to benchmark the accuracy of Twitter for identifying and verifying the location of traffic accident black spots. The ultimate goal is to use Twitter data to identify accidents in real-time with a measurable degree of confidence.
MSFS: Multiple Spatio-temporal Scales Traffic Forecasting in Mobile Cellular Network

With peta-bytes of data that are continuously collected about various aspects of the mobile networks, one of the main challenges when dealing with such data is performing accurate predictions in order to address a broad class of application problems, ranging from mobile network optimization to preventive maintenance. To this end, time series prediction has been widely addressed by statistics community. However, the performance of time series prediction is seriously affected by spatio-temporal context. In this paper, with spatio-temporal hierarchical constructions, we aggregate time series and propose a Multi-Scales Forecasting System (MSFS) to predict time series of different granularities. From the aspect of statistics, three traditional methods, Seasonal ARIMA (SARIMA), Hidden Markov Model (HMM) and Wavelet Neural Network (WNN), are applied to investigate accuracy and complexity of different spatio-temporal time series prediction. MSFS is evaluated with a real-word 3G dataset, and the experimental results show that time series in different spatio-temporal context have disparate characters. Through comparing the statistical results of each spatio-temporal scale, we analyze which granularity can be predicted and give a recommend proposal of forecasting method. Although this scheme is based on statistical results, it also opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.
Optimal User-Cell Association for Massive MIMO Wireless Networks

Massive MIMO is one of the most promising approaches for coping with the predicted wireless data traffic explosion. Future deployment scenarios will involve dense heterogeneous networks, comprised of massive MIMO base stations with different powers, numbers of antennas and multiplexing gain capabilities, and possibly highly nonhomogeneous user density (hot-spots). In such dense irregularly deployed networks, it will be important to have mechanisms for associating users to base stations so that the available wireless infrastructure is efficiently used. In this paper, we consider the optimal user-cell association problem for massive MIMO heterogeneous networks and illustrate how massive MIMO can also provide nontrivial advantages at the system level. Unlike previous treatments that rely on integer program problem formulations and their convex relaxations, the user-cell association problem is formulated directly as a convex network utility maximization and solved efficiently by a centralized subgradient algorithm. As we show, the globally optimal solution is physically realizable, in that there exists a sequence of integer-valued associations approaching arbitrarily closely the optimal fractional association. We also consider simple decentralized user-centric association schemes, where each user individually and selfishly connects to the base station with the highest promised throughput. Such user-centric schemes where users make local association decisions in a probabilistic manner can be viewed as games and are known to converge to Nash equilibria. Surprisingly, as we show, under certain conditions, the globally optimal solution is close to these Nash equilibria. Such decentralized approaches are, therefore, attractive not only for their simplicity, but also because they operate near the system social optimum. Our theoretical results are confirmed by extensive simulations with realistic LTE-like network parameters.