Miracle-dz/UniFAR-FTUs huggingface dataset:
What are the most effective behaviour change techniques to promote physical activity and/or reduce sedentary behaviour in inactive adults? A systematic review protocol

Introduction Large proportions of the population are not meeting recommended levels of physical activity and have increasingly sedentary lifestyles. Low levels of physical activity are predictive of poor health outcomes and time spent sedentary is related to a host of risk factors independently of physical activity levels. Building an evidence base of the best approaches to intervene in the lifestyles of inactive individuals is crucial in preventing long-term disease, disability and higher mortality rates. Methods and analysis Systematic searches will be conducted on all relevant databases (eg, PubMed, Scopus, CINAHL, PsycINFO). Studies will be included if they assess interventions aimed at changing physical activity or sedentary behaviour levels in adults (over 18) who are inactive and do not suffer from chronic conditions. Studies must also be randomised controlled trials (RCTs), have a primary outcome of physical activity or sedentary behaviour, and measure outcomes at least 6 months after intervention completion. Studies will be coded using the Behaviour Change Technique (BCT) taxonomy v1 and Template for Intervention Description and Replication (TIDieR) guidelines. 2 reviewers will independently screen full-text articles and extract data on study characteristics, participants, BCTs, intervention features and outcome measures. Study quality will also be assessed independently by 2 reviewers using the Cochrane risk of bias tool. A meta-analysis will be considered if there is sufficient homogeneity across outcomes. GRADE criteria will be used to assess quality of evidence. Dissemination This will be the first review to systematically appraise interventions aimed at changing the physical activity or sedentary behaviour of inactive individuals using RCT designs with a 6-month follow-up post-intervention. This review will better inform intervention designers targeting inactive populations and inform the design of a future complex intervention. Review registration number This protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 17 October 2014 (registration number: CRD42014014321).
The Complementary Neighborhood Patterns and Methylation-to-Mutation Likelihood Structures of 15,110 Single-Nucleotide Polymorphisms in the Bovine Genome

Bayesian analysis was performed to examine the single-nucleotide polymorphism (SNPs) neighborhood patterns in cattle using 15,110 SNPs, each with a flanking sequence of 500 bp. Our analysis confirmed three well-known features reported in plants and/or other animals: (1) the transition is the most abundant type of SNPs, accounting for 69.8% in cattle; (2) the transversion occurs most frequently (38.56%) in cattle when the A + T content equals two at their immediate adjacent sites; and (3) C ↔ T and A ↔ G transitions have reverse complementary neighborhood patterns and so do A ↔ C and G ↔ T transversions. Our study also revealed several novel SNP neighborhood patterns that have not been reported previously. First, cattle and humans share an overall SNP pattern, indicating a common mutation system in mammals. Second, unlike C ↔ T/A ↔ G and A ↔ C/G ↔ T, the true neighborhood patterns for A ↔ T and C ↔ G might remain mysterious because the sense and antisense sequences flanking these mutations are not actually recognizable. Third, among the reclassified four types of SNPs, the neighborhood ratio between A + T and G + C was quite different. The ratio was lowest for C ↔ G, but increased for C ↔ T/A ↔ G, further for A ↔ C/G ↔ T, and the most for A ↔ T. Fourth, when two immediate adjacent sites provide structures for CpG, it significantly increased transitions compared to the structures without the CpG. Finally, unequal occurrence between A ↔ G and C ↔ T in five paired neighboring structures indicates that the methylation-induced deamination reactions were responsible for ∼20% of total transitions. In addition, conversion can occur at both CpG sites and non-CpG sites. Our study provides new insights into understanding molecular mechanisms of mutations and genome evolution.
An improved merge-split sampler for conjugate dirichlet process mixture models

The Gibbs sampler is the standard Markov chain Monte Carlo sampler for drawing samples from the posterior distribution of conjugate Dirichlet process mixture models. Researchers have noticed the Gibbs sampler’s tendency to get stuck in local modes and, thus, poorly explore the posterior distribution. Jain and Neal (2004) proposed a merge-split sampler in which a naive random split is sweetened by a series of restricted Gibbs scans, where the number of Gibbs scans is a tuning parameter that must be supplied by the user. In this work, I propose an alternative merge-split sampler borrowing ideas from sequential importance sampling. My sampler proposes splits by sequentially allocating observations to one of two split components using allocation probabilities that are conditional on previously allocated data. The algorithm does not require further sweetening and is, hence, computationally efficient. In addition, no tuning parameter needs to be chosen. While the conditional allocation of observations is similar to sequential importance sampling, the output from the sampler has the correct stationary distribution due to the use of the Metropolis-Hastings ratio. The computational efficiency of my sequentially-allocated merge-split (SAMS) sampler is compared to Jain and Neal’s sampler using various values for the tuning parameter. Comparisons are made in terms of autocorrelation times for four univariate summaries of the Markov chains taken at fixed time intervals. In four examples involving different models and datasets, I show that my merge-split sampler usually performs substantially better — in some cases, two to five times faster — than existing methods, and never performs worse.
Soy Protein With Isoflavones, but not an Isoflavone-Rich Supplement, Improves Arterial Low-Density Lipoprotein Metabolism and Atherogenesis

Objective—We sought to determine if arterial LDL metabolism contributes to the decreased atherosclerosis seen with soy and if isolated isoflavones would have similar effects. Methods and Results—Ovariectomized monkeys were fed an atherogenic diet for 20 weeks with a protein source of (1) casein/lactalbumin (CAS, n=20), (2) soy protein isolate (SOY, n=20), or (3) casein/lactalbumin with isolated soy isoflavones (ISO, n=17). Plasma lipoprotein concentrations were improved with SOY but not ISO. Arterial LDL metabolism was characterized with one subset (n=12/group) injected with dual-labeled tyramine-cellobiose (TC)-LDL (125I-TC-131I-LDL) 24 hours before necropsy to determine LDL degradation and accumulation, while another subset (n=8/group) was injected with 125I-TC-LDL 1 hour before necropsy to determine LDL permeability and delivery. Conclusions—Coronary artery LDL degradation was reduced by 50% (P =0.02) with SOY but not with ISO compared with CAS. Neither treatment altered arterial permeability. Reduced LDL degradation with SOY was due to decreased arterial LDL delivery (P =0.02). Carotid artery cholesterol ester was also decreased with SOY, but not with ISO. Plasma isoprostanes or plasma markers of inflammation did not differ among treatment groups. Thus, the decreased arterial LDL delivery and subsequent LDL degradation may explain, in part, the atheroprotective effects of soy.
Systemic and Mucosal Immune Reactivity upon Mycobacterium avium ssp. paratuberculosis Infection in Mice

Mycobacterium avium ssp. paratuberculosis (MAP) is the cause of Johne's disease, an inflammatory bowel disorder of ruminants. Due to the similar pathology, MAP was also suggested to cause Crohn's disease (CD). Despite of intensive research, this question is still not settled, possibly due to the lack of versatile mouse models. The aim of this study was to identify basic immunologic mechanisms in response to MAP infection. Immune compromised C57BL/6 Rag2 −/− mice were infected with MAP intraperitoneally. Such chronically infected mice were then reconstituted with CD4+ and CD8+ T cells 28 days after infection. A systemic inflammatory response, detected as enlargement of the spleen and granuloma formation in the liver, was observed in mice infected and reconstituted with CD4+ T cells. Whereby inflammation in infected and CD4+CD45RBhi T cell reconstituted animals was always higher than in the other groups. Reconstitution of infected animals with CD8+ T cells did not result in any inflammatory signs. Interestingly, various markers of inflammation were strongly up-regulated in the colon of infected mice reconstituted with CD4+CD45RBlo/int T cells. We propose, the usual non-colitogenic CD4+CD45RBlo/int T cells were converted into inflammatory T cells by the interaction with MAP. However, the power of such cells might be not sufficient for a fully established inflammatory response in the colon. Nevertheless, our model system appears to mirror aspects of an inflammatory bowel disease (IBD) like CD and Johne's diseases. Thus, it will provide an experimental platform on which further knowledge on IBD and the involvement of MAP in the induction of CD could be acquired.
A General Persistent Code Caching Framework for Dynamic Binary Translation (DBT)

Dynamic binary translation (DBT) translates binary code from one instruction set architecture (ISA) to another (same or different) ISA at runtime, which makes it very useful in many applications such as system virtualization, whole program analysis, system debugging, and system security. Many techniques have been proposed to improve the efficiency of DBT systems for long-running and loop-intensive applications. However, for applications with short running time or long-running but with few hot code regions such as JavaScript and C# applications in web services, such techniques have difficulty in amortizing the overhead incurred during binary translation. To reduce the translation overhead for such applications, this paper presents a general persistent code caching framework, which allows the reuse of translated binary code across different executions for the same or different applications. Compared to existing approaches, the proposed approach can seamlessly handle even dynamically generated code, which is very popular in script applications today. A prototype of the proposed framework has been implemented in an existing retargetable DBT system. Experimental results on a list of applications, including C/C++ and JavaScript, demonstrate that it can achieve 76.4% performance improvement on average compared to the original DBT system without helper threads for dynamic binary translation, and 9% performance improvement on average over the same DBT system with helper threads when code reuse is combined with help threads.
Planning and learning algorithms for routing in Disruption-Tolerant Networks

We give an overview of algorithms that we have been developing in the DARPA disruption-tolerant networking program, which aims at improving communication in networks with intermittent and episodic connectivity. Thanks to the use of network caching, this can be accomplished without the need for a simultaneous end-to-end path that is required by traditional Internet and mobile ad-hoc network (MANET) protocols. We employ a disciplined two-level approach that clearly distinguishes the dissemination of application content from the dissemination of network-related knowledge, each of which can be supported by different algorithms. Specifically, we present probabilisitc reflection, a single-message protocol enabling the dissemination of knowledge in strongly disrupted networks. For content dissemination, we present two approaches, namely a symbolic planning algorithm that exploits partially predictable temporal behavior, and a distributed and disruption-tolerant reinforcement learning algorithm that takes into account feedback about past performance.
Development of Pathological Diagnostics of Human Kidney Cancer by Multiple Staining Using New Fluorescent Fluolid Dyes

New fluorescent Fluolid dyes have advantages over others such as stability against heat, dryness, and excess light. Here, we performed simultaneous immunostaining of renal tumors, clear cell renal cell carcinoma (RCC), papillary RCC, chromophobe RCC, acquired cystic disease-associated RCC (ACD-RCC), and renal angiomyolipoma (AML), with primary antibodies against Kank1, cytokeratin 7 (CK7), and CD10, which were detected with secondary antibodies labeled with Fluolid-Orange, Fluolid-Green, and Alexa Fluor 647, respectively. Kank1 was stained in normal renal tubules, papillary RCC, and ACD-RCC, and weakly or negatively in all other tumors. CK7 was positive in normal renal tubules, papillary RCC, and ACD-RCC. In contrast, CD10 was expressed in renal tubules and clear cell RCC, papillary RCC, AML, and AC-RCC, and weakly in chromophobe RCC. These results may contribute to differentiating renal tumors and subtypes of RCCs. We also examined the stability of fluorescence and found that fluorescent images of Fluolid dyes were identical between a tissue section and the same section after it was stored for almost three years at room temperature. This indicates that tissue sections can be stored at room temperature for a relatively long time after they are stained with multiple fluorescent markers, which could open a door for pathological diagnostics.
Cache Pirating: Measuring the Curse of the Shared Cache

We present a low-overhead method for accurately measuring application performance (CPI) and off-chip bandwidth (GB/s) as a function of available shared cache capacity. The method is implemented on real hardware, with no modifications to the application or operating system. We accomplish this by co-running a Pirate application that "steals" cache space with the Target application. By adjusting how much space the Pirate steals during the Target's execution, and using hardware performance counters to record the Target's performance, we can accurately and efficiently capture performance data for the Target application as a function of its available shared cache. At the same time we use performance counters to monitor the Pirate to ensure that it is successfully stealing the desired amount of cache. To evaluate this approach, we show that 1) the cache available to the Target behaves as expected, 2) the Pirate steals the desired amount of cache, and ) the Pirate does not bias the Target's performance. As a result, we are able to accurately measure the Target's performance while stealing up to an average of 6.8MB of the 8MB of cache on our Nehalem based test system with an average measurement overhead of only 5.5%.
An Electrical Model for the Fault Simulation of Small Delay Faults Caused by Crosstalk Aggravated Resistive Short Defects

In this paper a new electrical model is proposed to be used in fault size based fault simulation of crosstalk aggravated resistive short defects. The electrical behavior of the defect is first described and analyzed in details. Then an electrical model is proposed allowing to efficiently compute the critical resistance determining the range of detectable short resistance. The model is validated by comparison with SPICE simulations.
Oil Price Shocks and Aggregate Fluctuations

This paper analyzes the effects of oil price shocks on the characteristics of the business cycle and on welfare in a small open economy, such as in the case of the Spanish economy. The results show the ability of the model to reproduce the business cycle path of the Spanish economy, especially in those periods when shocks in the price of oil were most dramatic. Furthermore, the model reproduces other regularities of the Spanish business cycle. Finally, it is shown that the increases in the relative price oil had a negative and significant effect on welfare.
Awareness, Attitude, and Practice of Breast Cancer Screening Women, and the Associated Socio-Demographic Characteristics, in Northern Iran

Background: Breast cancer is the most common malignancy among women, with a high rate of mortality. The burden of disease and its serious outcome could be prevented by early detection. Objectives: Thus, the objective of this study was investigation the awareness, attitude and practice of breast cancer screening women. Patients and Methods: This cross-sectional study has conducted on 500 representative samples of women aged 20 - 65 years who referred to urban area of Babol health centers in 2012. The data have collected with standard questionnaire. The demographic data, the knowledge of risk factors and symptoms and sign, the health belief, and the practice of women in breast cancer screening have collected by interview. Results: The results show that only 14.8% of participants had a high level of knowledge for breast cancer risk factors and 33.8% at high level of awareness of symptoms and signs. The level of awareness has significantly associated with higher age (P = 0.03) and higher educational level (P = 0.04). Only 10.2% and 8.4% of women have performed breast self- examination (BSE) monthly and breast clinical examination (BCE) annually respectively. The practice of women with BSE has significantly associated with aging (P = 0.005), higher educational level (P = 0.007), higher knowledge (P = 0.001) and positive attitude toward perceived benefits and confidence and health motivation (P = 0.001). Conclusions: The low level of awareness and practice of breast cancer screening have found in this study, emphasized an extension of framework health educational program, particularly in younger and low educated women.
Dynamically Reconfigurable Data-intensive Service Composition

The distributed nature of services poses significant challenges to building robust service-based applications. A major aspect of this challenge is finding a model of service integration that promotes ease of dynamic reconfiguration, in response to internal and external stimuli. Centralized models of composition are not conducive for data-intensive applications such as those in the scientific domain. Decentralized compositions are more complicated to manage especially since no service has a global view of the interaction. In this paper we identify the requirements for dynamic reconfiguration of data-intensive composite services. A hybrid composition model that combines the attributes of centralization and decentralization is proposed. We argue that this model promotes dynamic reconfiguration of data-intensive service compositions.
Diagnosis of Severe Obstructive Sleep Apnea with Model Designed Using Genetic Algorithm and Ensemble Support Vector Machine

Obstructive sleep apnea (OSA) is a general sleep disorder and is a significant cause of motor vehicle crashes and chronic diseases. The severity of the respiratory events is measured by the frequency and duration of apneas and hypopneas per hour of sleep, namely apnea-hypopnea index (AHI), using polysomnography (PSG). Suspected patients can be classified as normal (AHI<5), mild (5AHI<15), moderate (15AHI<30), and severe (AHI30). Although PSG is treated as the gold standard for the diagnosis of OSA, its shortcoming includes technical expertise is required and timely access is restricted. Thus, home pulse oximetry has been proposed as a valuable and effective tool for screening patients with OSA. Support vector machine (SVM) is believed to be more efficient than neural network and traditional statistical-based classifiers. Nonetheless, it is critical to determine suitable parameters to increase classification performance. Furthermore, an ensemble of SVM classifiers use multiple models to obtain better predictive accuracy and are more stable than models consist of a single model. Genetic algorithm (GA), on the other hand, is able to find optimal solution within an acceptable time, and is faster than dynamic programming with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, it was combined with ensemble SVM to design a clinical decision support system (CDSS) for the diagnosis of patients with severe OSA, and then followed by PSG to further discriminate normal, mild and moderate patients. The results show that ensemble SVM classifiers demonstrate better diagnosing performance than models consisting of a single SVM model and logistic regression analysis. Additionally, the oximetry/PSG diagnostic scheme was shown to have higher cost- effectiveness in the diagnosis of OSA patients with an average cost ratio of 0.66 and an average waiting time ratio of 0.40 compared to the traditional scheme with PSG examination only.
Research on prediction methods for load balancing based on self-adaptive and confirmation mechanism

Present load balancing prediction in distributed system is based on full trust in prediction. However such trust may risk wrong prediction and degrade the system efficiency. In this paper, we proposed a self-adaptive load prediction model with multiple algorithms and confirmation mechanism. Based on this model, combined with mobile agent technology, we implemented a software packet for dynamic prediction, with which we achieved a dynamic balance of the system.
Conformational Sampling of Druglike Molecules with MOE and Catalyst: Implications for Pharmacophore Modeling and Virtual Screening

Computational conformational sampling is integral to small molecule pharmaceutical research, for detailed conformational analysis and high-throughput 3D library enumeration. These two regimes were tested in details for the general-purpose modeling program MOE, using its three conformational sampling methods, i.e. systematic search, stochastic search, and Conformation Import. The tests include i) identification of the global energy minimum, ii) reproduction of the bioactive conformation, iii) measures of conformational coverage with 3D descriptors, and iv) compute times. The bioactive conformers are from a new set of 256 diverse, druglike, protein-bound ligands compiled and analyzed with particular care. The MOE results are compared to those obtained from the established program Catalyst. Key parameters controlling the conformational coverage were varied systematically. Coverage and diversity of the conformational space were characterized with unique pharmacophore triplets or quadruplets. Overall, the protocols in both MOE and Catalyst performed well for their intended tasks. MOE performed at least as well as Catalyst for high-throughput library generation and detailed conformational modeling. This work provides a guide and specific recommendations regarding the usage of conformational sampling tools in MOE.
Distributed Optimal Datacenter Bandwidth Allocation for Dynamic Adaptive Video Streaming

Video streaming systems such as YouTube and Netflix are usually supported by the content delivery networks and datacenters that can consume many megawatts of power. Most existing works independently study the issues of improving quality of experience (QoE) for viewers and reducing the cost and emissions associated with the enormous energy usage of datacenters. By contrast, this paper addresses them both, and jointly optimizes the QoE, the energy cost and emissions by intelligently allocating datacenter bandwidth among different client groups. Specially, we propose a distributed algorithm for achieving the optimal bandwidth allocation. The algorithm novelly decomposes the optimization process into separate ones, which are solved iteratively across datacenters and clients. We demonstrate its convergence by both theoretical proof and experimental validation. The experimental results show that the proposed algorithm converges very fast and achieves much better QoE-cost balance than existing approaches.