Miracle-dz/UniFAR-FTUs huggingface dataset:
Pulmonary function, smoking cessation and 30 year mortality in middle aged Finnish men

BACKGROUND Although it is well known that impaired pulmonary function is a strong predictor of mortality and that smoking decreases pulmonary function, little is known about the long term effect of smoking cessation on mortality at different levels of pulmonary function. We have studied the impact of smoking cessation on mortality over the entire range of baseline pulmonary function. METHODS The study subjects consisted of men aged 40–59 at entry who were the Finnish participants in the Seven Countries Study during 1959–89. RESULTS In all the participants (n = 1582) impaired forced expiratory volume in 0.75 seconds (FEV0.75) was significantly associated with increased all cause mortality. When those who gave up smoking during the follow up period were compared with continuous smokers (n = 860) all cause mortality was found to be decreased among those who quit. The relative adjusted hazard (HR) was 0.71 (95% confidence interval 0.50 to 1.00). The median survival time in those who stopped smoking compared with those who continued to smoke from 1969 onwards was 7.65, 7.59, and 6.30 years longer in the lowest, middle and highest tertiles of adjusted FEV0.75 distribution, respectively. In those who gave up smoking, mortality from cardiovascular causes was significantly lower (HR 0.60 (95% CI 0.37 to 0.98)). CONCLUSIONS These findings suggest that smokers across the entire range of pulmonary function may increase their expectation of lifespan by giving up smoking.
GeneMarkS-2 : Raising Standards of Accuracy in Gene Recognition

Motivation: Ab initio gene prediction in prokaryotic genomes is supposed to be so accurate that RNASeq data are rarely produced to bring in an additional layer of evidence. In 2016 more than 60,000 prokaryotic genomes were re-annotated by the NCBI pipeline. Given the sheer volume of prokaryotic DNA data flowing from next generation sequencing facilities into genome databases, the annotation accuracy should be at the highest level possible. Still, the prevalence of horizontal gene transfer as well as ubiquitous leaderless transcription observed in prokaryotic species call for introducing more complex models of genes and regulatory regions than it was thought to be sufficient earlier. Results: We describe a new algorithm and software tool GeneMarkS-2. The new multi-model tool has an option to select parameters best matching local genomic GC content that may vary widely due to horizontal gene transfer. Genomes are automatically classified by the inferred types of organization of gene starts neighborhoods which evolution is directed by species specific transcription and translation mechanisms. A new motif search algorithm, LFinder, introduced to reach higher accuracy in detecting conserved motifs in regulatory regions upstream to predicted gene starts uses objective function depending on motif localization. In performance assessments made on test sets validated by proteomics experiments and other sources of evidence we have demonstrated superior accuracy of GeneMarkS-2 in comparison with other state-of-the-art gene prediction tools including GeneMarkS which “plus” version is currently used by the NCBI prokaryotic genome annotation pipeline. Availability: http://topaz.gatech.edu/GeneMark/genemarks2.cgi Contact: borodovsky@gatech.edu
FTARM: Fault Tolerant Asynchronous RISC Microprocessor Using Watchdog Module

This paper introduces a fault-tolerant asynchronous RISC microprocessor, called FTARM, which combines several error detection mechanisms such as watchdog module, to increase the fault coverage. With the best of our knowledge this is the first paper that tries to introduce watchdog module for asynchronous circuits based on Quasi Delay Insensitive (QDI) to detect faults. The FTARM is implemented using the Verilog. To evaluate the FTARM, different workloads were run on its implementation using the Verilog HDL. The evaluation is based on more than 2000 different transient and permanent single stuck-at-faults. The results show that more than 98% faults were detected. The Verilog model of FTARM is synthesized, where about 10% area overhead was observed.
The Narrow Escape Problem in a Flat Cylindrical Microdomain with Application to Diffusion in the Synaptic Cleft

The mean first passage time (MFPT) for a Brownian particle to reach a small target in cellular microdomains is a key parameter for chemical activation. Although asymptotic estimations of the MFPT are available for various geometries, these formula cannot be applied to degenerated structures where one dimension of is much smaller compared to the others. Here we study the narrow escape time (NET) problem for a Brownian particle to reach a small target located on the surface of a flat cylinder, where the cylinder height is comparable to the target size, and much smaller than the cylinder radius. When the cylinder is sealed, we estimate the MFPT for a Brownian particle to hit a small disk located centrally on the lower surface. For a laterally open cylinder, we estimate the conditional probability and the conditional MFPT to reach the small disk before exiting through the lateral opening. We apply our results to diffusion in the narrow synaptic cleft, and compute the fraction and the mean time for neurotransmitters to find their specific receptors located on the postsynaptic terminal. Finally, we confirm our formulas with Brownian simulations.
Determining the Probability of a False Positive When MatchingChains of Oriented

This paper gives a method for determining the probability of nding a false positive instance of an object in an image when matching is performed using chains of pixels with associated local information (such as orientation). We model the matching process between a chain of object pixels and the image as a Markov process , where each state represents either the presence or absence of a match for an object pixel in the image. This model yields the probability that particular position of an object model will result in a false positive. We use the probabilities from discrete positions of the model to estimate the probability of a false positive over the space of allowable transformations of the model. These techniques have been used to implement an adaptive recognition system, where the matching threshold is set such that the probability of a false positive is low.
An Energy and Application Scenario Aware Active RFID Protocol

The communication protocol used is a key issue in order to make the most of the advantages of active RFID technologies. In this paper we introduce a carrier sense medium access data communication protocol that dynamically adjusts its back-off algorithm to best suit the actual application at hand. Based on a simulation study of the effect on tag energy cost, read-out delay, and message throughput incurred by some typical back-off algorithms in a CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) active RFID protocol, we conclude that by dynamic tuning of the initial contention window size and back-off interval coefficient, tag energy consumption and read-out delay can be significantly lowered. We show that it is possible to decrease the energy consumption per tag payload delivery with more than 10 times, resulting in a 50% increase in tag battery lifetime. We also discuss the advantage of being able to predict the number of tags present at the RFID-reader as well as ways of doing it.
Metagenomic analysis of brain abscesses identifies specific bacterial associations.

BACKGROUND The bacterial flora involved in brain abscess is often complex. In a previous study, using a metagenomic approach based on 16S ribosomal DNA (rDNA) amplification, we demonstrated that the diversity of the microbial flora involved in these infections was underestimated. METHODS We performed a 16S rDNA-based metagenomic analysis of cerebral abscesses from patients diagnosed from 2006 through 2010. All bacteria present in brain abscess specimens were identified, in view of the clinical and epidemiological characteristics of the patients. RESULTS Fifty-one patients were included in our study. By detecting polymicrobial infections in 19 patients, our strategy was significantly more discriminatory and enabled the identification of a greater number of bacterial taxa than did culture and conventional 16S rDNA polymerase chain reaction (PCR) and sequencing, respectively (P < 10(-2)). Data mining discriminated 2 distinct bacterial populations in brain abscess from dental and sinusal origin. In addition, of the 80 detected bacterial species, we identified 44 bacteria that had never been found in brain abscess specimens, including 22 uncultured bacteria. These uncultured agents mostly originated from the buccal or sinusal floras (P < 10(-2)) and were found in polymicrobial specimens (P < 10(-2)). CONCLUSIONS Cloning and sequencing of PCR-amplified 16S rDNA is a highly valuable method to identify bacterial agents of brain abscesses.
Factors Contributing to the Biofilm-Deficient Phenotype of Staphylococcus aureus sarA Mutants

Mutation of sarA in Staphylococcus aureus results in a reduced capacity to form a biofilm, but the mechanistic basis for this remains unknown. Previous transcriptional profiling experiments identified a number of genes that are differentially expressed both in a biofilm and in a sarA mutant. This included genes involved in acid tolerance and the production of nucleolytic and proteolytic exoenzymes. Based on this we generated mutations in alsSD, nuc and sspA in the S. aureus clinical isolate UAMS-1 and its isogenic sarA mutant and assessed the impact on biofilm formation. Because expression of alsSD was increased in a biofilm but decreased in a sarA mutant, we also generated a plasmid construct that allowed expression of alsSD in a sarA mutant. Mutation of alsSD limited biofilm formation, but not to the degree observed with the corresponding sarA mutant, and restoration of alsSD expression did not restore the ability to form a biofilm. In contrast, concomitant mutation of sarA and nuc significantly enhanced biofilm formation by comparison to the sarA mutant. Although mutation of sspA had no significant impact on the ability of a sarA mutant to form a biofilm, a combination of protease inhibitors (E-64, 1-10-phenanthroline, and dichloroisocoumarin) that was shown to inhibit the production of multiple extracellular proteases without inhibiting growth was also shown to enhance the ability of a sarA mutant to form a biofilm. This effect was evident only when all three inhibitors were used concurrently. This suggests that the reduced capacity of a sarA mutant to form a biofilm involves extracellular proteases of all three classes (serine, cysteine and metalloproteases). Inclusion of protease inhibitors also enhanced biofilm formation in a sarA/nuc mutant, with the combined effect of mutating nuc and adding protease inhibitors resulting in a level of biofilm formation with the sarA mutant that approached that of the UAMS-1 parent strain. These results demonstrate that the inability of a sarA mutant to repress production of extracellular nuclease and multiple proteases have independent but cumulative effects that make a significant contribution to the biofilm-deficient phenotype of an S. aureus sarA mutant.
Vertical Redistribution of Oceanic Heat Content

AbstractEstimated values of recent oceanic heat uptake are on the order of a few tenths of a W m−2, and are a very small residual of air–sea exchanges, with annual average regional magnitudes of hundreds of W m−2. Using a dynamically consistent state estimate, the redistribution of heat within the ocean is calculated over a 20-yr period. The 20-yr mean vertical heat flux shows strong variations in both the lateral and vertical directions, consistent with the ocean being a dynamically active and spatially complex heat exchanger. Between mixing and advection, the two processes determining the vertical heat transport in the deep ocean, advection plays a more important role in setting the spatial patterns of vertical heat exchange and its temporal variations. The global integral of vertical heat flux shows an upward heat transport in the deep ocean, suggesting a cooling trend in the deep ocean. These results support an inference that the near-surface thermal properties of the ocean are a consequence, at least.
The Fractal Dimension Making Similarity Queries More Efficient

This paper presents a new algorithm to answer k -nearest neighbor queries called the Fractal k -Nearest Neighbor (k NNF ()). This algorithm takes advantage of the fractal dimension of the dataset under scan to estimate a suitable radius to shrinks a query that retrieves the k -nearest neighbors of a query object. k -NN() algorithms starts searching for elements at any distance from the query center, progressively reducing the allowed distance used to consider elements as worth to analyze. If a proper radius can be set to start the process, a significant reduction in the number of distance calculations can be achieved. The experiments performed with real and synthetic datasets over the access method Slim-tree, have shown that the efficiency of our approach makes the total processing time to drop up to 50%, while requires 25% less distance calculations.
Support QoS in Open Real-Time Systems

With the development of computer systems, the realtime systems with different kinds of real-time applications and non-real-time applications are becoming more and more popular. At the same time, more and more multimedia applications make real-time system to support QoS. Based on the real-time systems, the paper proposes a model of Linux-based open real-time systems with QoS support. Firstly, the paper analyzes real-time systems and QoS. Secondly, a windows-constrained CBS is proposed. Then it proposes the scheduling framework of Linux-based open real-time systems. Finally, the experiment results are showed.
How ‘buzz’ reduces uncertainty for new firm founders

To whom should potential new firm founders turn to for advice? This article identifies buzz as a mechanism to transfer the knowledge of who to turn to for advice. Previous work on buzz has linked it with clusters or compared local buzz with international pipelines of information. The interaction between place and business advice was examined in surveys of 599 new firm founders in England and 381 new firm founders in Catalonia (Spain). Our models exploit the denominator from a heteroskedastic probit to capture the local variation in uncertainty We show empirically how buzz can add to collective institutions outside of clusters. Our findings show that buzz influences the taking of advice and the uncertainty surrounding advice. Besides, there were strong impacts from policy on the take-up of advice in Catalonia, but it did not change the variations in uncertainty.
Use and Affordances of ICTs in Interorganizational Collaboration: An Exploratory Study of ICTs in Nonprofit Partnerships

Interorganizational collaboration relies on the use of information and communication technologies (ICTs). However, previous ICT research often takes place within a single organization, lacking insight into how ICTs sustain interorganizational structures. This study examined both the product categories and functional uses of ICTs for interorganizational collaboration, drawing from surveys among a random sample of 181 human services nonprofit organizations in the United States. Results showed that email, teleconference, and shared repositories (e.g., Dropbox) were most popular product types. Content analysis revealed that ICTs were used for coordination, information sharing, relational communication, and client management and ICT utilities varied based on collaboration types. Analyses also indicated that collaboration type, as opposed to organizational attributes (e.g., organizational capacity, resources, size), was related to the frequency of ICT use in nonprofit collaboration. Theoretical contributions to the study of ICTs, interorganizational collaboration, and management communication are discussed.
Real-Time PEV Charging/Discharging Coordination in Smart Distribution Systems

This paper proposes a novel online coordination method for the charging of plug-in electric vehicles (PEVs) in smart distribution networks. The goal of the proposed method is to optimally charge the PEVs in order to maximize the PEV owners' satisfaction and to minimize system operating costs without violating power system constraints. Unlike the solutions reported in the literature, the proposed charging architecture guarantees the feasibility of the charging decisions by means of a novel prediction unit that can forecast future PEVs power demand and through an innovative two-stage optimization unit that ensures effective charging coordination. Coordinated PEV discharging also enables improved utilization of power system resources. Simulation results for a typical distribution network are provided as a demonstration of the effectiveness of the proposed architecture.
Twin migration in Fe-based bcc crystals: theory and experiments

We establish an overall energy expression to determine the twin migration stress in bcc metals. Twin migration succeeds twin nucleation often after a load drop, and a model to establish twin migration stress is of paramount importance. We compute the planar fault energy barriers and determine the elastic energies of twinning dislocations including the role of residual dislocations (br) and twin intersection types such as 1 1 0, 1 1 3 and 2 1 0. The energy expression derived provides the twin migration stress in relation to the twin nucleation stress with a ratio of 0.5–0.8 depending on the resultant residual burgers vector and the intersection types. Utilizing digital image correlation, it was possible to differentiate the twin nucleation and twin advancement events experimentally, and transmission electron microscopy observations provided further support to the modelling efforts. Overall, the methodology developed provides an enhanced understanding of twin progression in bcc metals, and most importantly the proposed model does not rely on empirical constants. We utilize Fe-50at. %Cr in our experiments, and subsequently predict the twin migration stress for pure Fe, and Fe-3at. %V from the literature showing excellent agreement with experiments.
The relationships between collectivist attitudes and elementary forms of human relations: Evidence from Estonia

This study examined the extent to which collectivist attitudes predict preference for the basic models of social relations. According to Fiske (1991), all social relations can be reduced to four basic forms – communal sharing (CS), authority ranking (AR), equality matching (EM), and market pricing (MP) – that guide people’s social initiatives and help them to understand and respond appropriately to the social actions of others. The preference for basic forms was studied using a set of questions about everyday hypothetical situations in which the four different models can be alternatively applied. We observed that EM is the dominant principle in distributing (received) benefits: everyone should get equal shares (regardless of their needs or position) and nobody should have an advantage over another. In those situations in which people were asked to contribute, the respondents were inclined to think in terms of cost and benefit, insisting that the contributions should be made according to MP. CS relationships were mostly limited to family-related situations. Although people scoring high on collectivism showed an inclination to use CS and avoid MP relationships more frequently in family situations, the association between the collectivist attitudes and elementary forms of sociality was fairly small. It is concluded that the relationships between the use of the relational models and the collectivist attitudes are modest.
Nemesis: Preventing Authentication & Access Control Vulnerabilities in Web Applications

This paper presents Nemesis, a novel methodology for mitigating authentication bypass and access control vulnerabilities in existing web applications. Authentication attacks occur when a web application authenticates users unsafely, granting access to web clients that lack the appropriate credentials. Access control attacks occur when an access control check in the web application is incorrect or missing, allowing users unauthorized access to privileged resources such as databases and files. Such attacks are becoming increasingly common, and have occurred in many high-profile applications, such as IIS [10] and WordPress [31], as well as 14% of surveyed web sites [30]. Nevertheless, none of the currently available tools can fully mitigate these attacks. Nemesis automatically determines when an application safely and correctly authenticates users, by using Dynamic Information Flow Tracking (DIFT) techniques to track the flow of user credentials through the application's language runtime. Nemesis combines authentication information with programmer-supplied access control rules on files and database entries to automatically ensure that only properly authenticated users are granted access to any privileged resources or data. A study of seven popular web applications demonstrates that a prototype of Nemesis is effective at mitigating attacks, requires little programmer effort, and imposes minimal runtime overhead. Finally, we show that Nemesis can also improve the precision of existing security tools, such as DIFT analyses for SQL injection prevention, by providing runtime information about user authentication.
Personalized recommendation strategies in mobile educational systems

Recent technological advancements shifted the trends of learning from e-learning to mobile learning, thus added new dimensions such as learning process can take place at anytime and anywhere. However this shifts faces some technological and design issues in m-learning and e-learning (i.e personalization). The factors that lead towards personalisation are the frequent growth of learning resources as well as differences in the characteristics of learners. Recently, recommender systems have been exploited as a new form of personalisation. This paper proposed a hybrid recommendation approach for mobile learning environment, based on identified user's learning style for providing more personalized recommendation. The reported results indicate significant differences in the performance of learners having personalized recommendations.
Tuning digital PID controllers for discrete-time system by Election Campaign Optimization algorithm

In this paper, Election Campaign Optimization algorithm is applied to solve the difficulty in PID control that the tuning parameters of PID controllers could not be determined well by conventional methods. Tuning PID controller parameters is a typical three variable optimization problem without constraint condition. An example is employed to test this method, It is found that the method could be find the best parameters that can bring on a optimal control performance, the simulation results is better than that in involved reference. This indicates that the tuning method is feasible.