Miracle-dz/UniFAR-FTUs huggingface dataset:
Force development, energy state and ATP production of cardiac muscle from turtles and trout during normoxia and severe hypoxia

SUMMARY The effects of hypoxia on energy economy of cardiac muscle were compared between the hypoxia-tolerant freshwater turtle at 20°C and the hypoxia-sensitive rainbow trout at 15°C. Isolated ventricular preparations were left either at rest or stimulated at 30 min-1 to develop isometric twitch force. Under oxygenated conditions, twitch force and oxygen consumption were similar for the two species. Overall metabolism was reduced during severe hypoxia in both resting and stimulated preparations and under these conditions most of the ATP production was derived from anaerobic metabolism. During hypoxia, a metabolic depression of approximately 2/3 occurred for non-contractile processes in both turtle and trout preparations. During hypoxia, lactate production and residual oxygen consumption were similar in turtle and trout. Cellular energy state and phosphorylation potential decreased during severe hypoxia in both species and this reduction was more severe in preparations stimulated to contraction. However, in turtle ventricular preparations the energy state and phosphorylation potential stabilised at higher levels than in trout, and turtle preparations also maintained a higher twitch force throughout the hypoxic period. Moreover, twitch force relative to total ATP hydrolysis was markedly increased during hypoxia in turtle while this ratio was unchanged for trout. The main findings of this study are: (1) cellular energy liberation and the energy demand of non-contractile processes decreased to similar levels in hypoxic turtle and trout myocardium; (2) turtle myocardium maintained a substantially higher cellular energy state and twitch force development than trout myocardium during hypoxia and (3) the ratio of twitch force to ATP hydrolysis increased during hypoxia in turtle but was unchanged in trout. It is possible that this superior economy of the contracting turtle myocardium contributes to the remarkable hypoxia tolerance of freshwater turtles.
A design of smart docent service using hybrid recommenders

E-commerce companies such as Amazon.com and Netflix have been introducing recommendation techniques to attract their customers on their web sites. At Amazon.com, they use recommendation algorithms to personalize the online store for each customer. Many applications collect customer's interests to generate a list of recommended items. We introduce recommendation algorithms to smart docent service which guides the visitor considering his preference and other conditions such as his location in museums or art galleries. We use a hybrid approach which combines item-item collaborative filtering and demographical recommendation.
BER of multi-carrier CDMA in an indoor Rician fading channel

The paper analyzes a novel digital modulation technique called multi-carrier code division multiple access (MC-CDMA) in which data symbols are transmitted at multiple subcarriers where each subcarrier is modulated by a 1 or -1 based on a spreading code. Analytical results are presented on the performance of this modulation scheme in the downlink of an indoor wireless Rician fading channel. In addition, the performance of a controlled equalization technique that attempts to restore the orthogonality between users is evaluated. <<ETX>>
Sample Paper for Complex Systems

The abstract should summarize the content of the paper, including its context and conclusions. Make sure that it is possible to understand the abstract without having read the entire paper. Remember that many database systems will access the paper through the listed keywords and from the title and abstract. If possible, use the passive voice and avoid personal pronouns in the abstract (“X is shown,” rather than “we show X”), but not if it leads to awkward sentence constructions. Try to keep the abstract below 150 words in length (and shorter for short papers). Do not cite references or display equations in the abstract.
Forecasting the Real Prices of Crude Oil: A Dynamic Model Averaging Approach

Forecasting oil prices has been of great interests for macroeconomists in the recent years. Our article contributes to this strand of the literature by using a dynamic model averaging (DMA) method to improve forecasting accuracy of real oil prices. The advantage of DMA is that the method combines models in a dynamic way using two forgetting factors to approximate the evolution of model parameters and model switching probabilities, respectively. Our empirical results show that DMA generates more accurate forecasts than the no-change forecasts at the relatively longer horizons. At a horizon of 12 months, the reduction of mean squared prediction error is as high as 30% and the accuracy of directional forecasts increases as high as 71%. It is also found that DMA performs better than Bayesian model averaging, the commonly-used mean combination of forecasts, and more sophisticated individual models such as a time-varying dimension model for the horizons of 3 and 12 months.
High-Performance MPI Broadcast Algorithm for Grid Environments Utilizing Multi-lane NICs

The performance of MPI collective operations, such as broadcast and reduction, is heavily affected by network topologies, especially in grid environments. Many techniques to construct efficient broadcast trees have been proposed for grids. On the other hand, recent high performance computing nodes are often equipped with multi-lane network interface cards (NICs), most previous collective communication methods fail to harness effectively. Our new broadcast algorithm for grid environments harnesses almost all downward and upward bandwidths of multi-lane NICs; A message to be broadcast is split into two pieces, which are broadcast along two independent binary trees in a pipelined fashion, and swapped between both trees. The salient feature of our algorithm is generality; it works effectively on both large clusters and grid environments. It can be also applied to nodes with a single NIC, by making multiple sockets share the NIC. Experimentations on a emulated network environment show that we achieve higher performance than traditional methods, regardless of network topologies or the message sizes.
Electroacupuncture Treatment Alleviates Central Poststroke Pain by Inhibiting Brain Neuronal Apoptosis and Aberrant Astrocyte Activation

Electroacupuncture (EA) is reported to effectively relieve the central poststroke pain (CPSP). However, the underlying mechanism remains unclear. The present study investigated the detailed mechanisms of action of EA treatment at different frequencies for CPSP. A CPSP model was established with a single collagenase injection to the left ventral posterolateral nucleus of the thalamus. The EA-treated groups then received EA treatment at frequency of 2, 2/15, or 15 Hz for 30 min daily for five days. The pain-related behavioral responses, neuronal apoptosis, glial activation, and the expression of pain signal transmission-related factors (β-catenin, COX-2, and NK-1R) were assessed using behavioral tests, Nissl staining, TUNEL staining, and immunohistochemical staining, respectively. The low-frequency EA treatment significantly (1) reduced brain tissue damage and hematoma sizes and (2) inhibited neuronal apoptosis, thereby exerting abirritative effects. Meanwhile, the high-frequency EA treatment induced a greater inhibition of the aberrant astrocyte activation, accompanied by the downregulation of the expressions of COX-2, β-catenin, and subsequently NK-1R, thereby alleviating inflammation and producing strong analgesic effects. Together, these findings suggest that CPSP is closely related to pathological changes of the neocortex and hippocampus. EA treatments at different frequencies may exert abirritative effects by inhibiting brain neuronal apoptosis and aberrant astrocyte activation in the brain.
CARMA Based MST Approximation for Multicast Provision in P2P Networks

The Minimum Spanning Tree (MST) problem is one of the most popular and important problems in the research area of distributed computing and networks. Contrary to the theoretical models where we usually have a global knowledge of all nodes and the corresponding distances for MST construction, in a realistic network (e.g., Internet) a node always has to rely on local knowledge only, that is it neither knows all other nodes nor exact distances between these nodes. In this paper we propose an approach for MST approximation based on local knowledge of a small subset of existing nodes by using the CARMA metric as a distance substitute. According to the evaluation results, our approach achieves a good MST approximation with respect to a communication cost and avoids extraneous communication needed for latency measurements.
Ranking document fragments in XML Retrieval : an empirical study

In the XML retrieval paradigm, document fragments may be returned as answers to a user query. This information being more specific than whole documents may therefore reduce the user effort for finding relevant information. However, since XML documents are composed of nested elements, many of which being possibly relevant to the user information need, retrieval systems must take care of the overlap between returned elements. In this paper, we consider this filtering problem. We first review related work from the IR literature. We then propose a framework for XML retrieval filtering and describe some ideas for this filtering process.
SCRUM : An extension pattern language for hyperproductive software development

The patterns of the SCRUM development method are presented as an extension pattern language to the existing organizational pattern languages. In the last few years, the SCRUM development method has rapidly gained recognition as an effective tool to hyper-productive software development. However, when SCRUM patterns are combined with other existing organizational patterns, they lead to highly adaptive, yet well-structured software development organizations. Also, decomposing SCRUM into patterns can guide adoption of only those parts of SCRUM that are applicable to a specific situation.
Prognostic role of C-reactive protein in gastric cancer: a meta-analysis.

BACKGROUND A number of studies have investigated the association between increased pretreatment serum C-reactive protein (CRP) levels and the prognosis of gastric cancer. However, due to the inconsistent results, whether the serum CRP level can be a prognostic factor in primary gastric cancer remains controversial. METHODS We searched Medline, PubMed, Embase and the Cochrane Central Register of Controlled Trials for relevant high-quality reports. A meta-analysis was carried out using the included studies to assess the association between pretreatment serum CRP level and overall survival (OS) in patients with gastric cancer. Correlation analyses were conducted to evaluate the relationship between serum CRP and tumor characteristics such as tumor node metastasis (TNM) stage and recurrence. RESULTS Twelve reports involving 2,597 patients with gastric cancer were included. Primary meta-analysis indicated a significant association between elevated CRP level and poor OS (HR 1.77, 95% CI 1.56-2.00). Subgroup analyses showed no single factor could alter the primary results when we divided the included studies by "number of patients", "max follow-up period", "TNM stage", "treatment" and "cut-off value". Correlation analyses showed that serum CRP level was significantly related to TNM stage (OR 2.96, 95% CI 2.22-3.93) and tumor recurrence (OR 1.81, 95% CI 1.21-2.71). CONCLUSIONS We demonstrated that increased pretreatment serum CRP level (≥10mg/L) was significantly associated with poor prognosis in gastric cancer patients, either in early or advanced stages.
Information influence measurement based on user quality and information attribute in microblogging

In microblogging, information influence is very hard to be measured. Because there are a lot of spam messages, and these messages were retweeted and commented by a lot of spam users in microblogging. Early methods for a message influence measurement mainly use message retweet and comment times. But they don't consider the quality of user, who takes part in the information diffusion. In this paper, we propose a novel measurement method based on user quality and information attribute. Experimental results show that our method can measure the influence more accurately and reduce the spam message influence in the dataset more effectively.
The Opposing Effect of Hedonic and Eudaimonic Happiness on Gene Expression is Correlated Noise

Background This paper presents a re-analysis of the gene set data from Fredrickson et al. 2013 and Fredrickson et al. 2015 which purportedly showed opposing effects of hedonic and eudaimonic happiness on the expression levels of a set of genes that have been correlated with social adversity. Fredrickson et al. 2015 used a linear model of fixed effects with correlated error (using GLS) to estimate the partial regression coefficients. Methods The standardized effects of hedonic and eudaimonic happiness on CTRA gene set expression estimated by GLS was compared to estimates using multivariate (OLS) linear models and generalized estimating equation (GEE) models. The OLS estimates were tested using a bootstrap t-test, O’Brien’s OLS test, a permutation t test, and the rotation z-test. The GEE estimates were tested using a Wald test with robust standard errors. The performance (type I, type II, and type M error) of all tests was investigated using a Monte Carlo simulation of data modeled after the 2015 dataset. Results Standardized OLS effects (mean partial regression coefficients) of Hedonia and Eudaimonia on gene expression levels are very small in both the 2013 and 2015 data, as well as the combined data.The p-values from all tests fail to reject any of the null models. The GEE estimates and tests are nearly identical to the OLS estimates and tests. By contrast, the GLS estimates are inconsistent between data sets, but in each dataset, at least one coefficient is large and highly statistically significant. The Monte Carlo simulation of error rates shows inflated type I error from the GLS test on data with a similar correlation structure to that in the 2015 dataset, and this error rate increases as the number of outcomes increases relative to the number of subjects. Bootstrap and permutation GLS distributions suggest that the GLS model not only results in downward biased standard errors but also inflated coefficients. Both distributions also show the expected, strong, negative correlation between the coefficients for Hedonia and Eudaimonia. Discussion The results fail to support opposing effects, or any detectable effect, of hedonic and eudaimonic well being on the pattern of gene expression. The apparently replicated pattern of hedonic and eudaimonic effects on gene expression is most parsimoniously explained as "correlated noise" due to the geometry of multiple regression. A linear mixed model for estimating fixed effects in designs with many repeated measures or outcomes should be used cautiously because of the potentially inflated type 1 and type M error.
Reverse Engineering Software-Models of Component-Based Systems

An increasing number of software systems is developed using component technologies such as COM, CORBA, or EJB. Still, there is a lack of support to reverse engineer such systems. Existing approaches claim reverse engineering of components, but do not support composite components. Also, external dependencies such as required interfaces are not made explicit. Furthermore, relaxed component definitions are used, and obtained components are thus indistinguishable from modules or classes. We present an iterative reverse engineering approach that follows the widely used definition of components by Szyperski. It enables third-party reuse of components by explicitly stating their interfaces and supports composition of components. Additionally, components that are reverse engineered with the approach allow reasoning on properties of software architectures at the model level. For the approach, source code metrics are combined to recognize components. We discuss the selection of source code metrics and their interdependencies, which were explicitly taken into account. An implementation of the approach was successfully validated within four case studies. Additionally, a fifth case study shows the scalability of the approach for an industrial-size system.
The Informal Sector in Developing Countries

This study documents four key facts about informal economic activities: (1) the size of the informal sector varies greatly across nations; (2) this size is strongly correlated with economic development, the tax burden, and the rule of law; (3) the informal sector emphasizes small-scale, self-financed and unskilled labour intensive economic activities; and (4), while financial markets are generally segmented along formal/informal lines in developing nations there is no compelling evidence that this is true for labour markets. We review the existing theoretical literature on the informal sector and describe a simple model with a tradeoff between tax evasion and access to formal sources of outside finance which is consistent with much of the existing evidence. Finally, the study discusses the challenges associated with measuring informal sector assets.
Novel Method of Apriori Algorithm using Top Down Approach

Association Rule mining is one of the important and most popular data mining techniques. It extracts interesting correlations, frequent patterns and associations among sets of items in the transaction databases or other data repositories. Apriori algorithm is an influential algorithm for mining frequent itemsets for Boolean association rules. Firstly, the concept of association rules is introduced and the classic algorithms of association rule are analyzed. In Apriori algorithm, most time is consumed for scanning the database repeatedly. Therefore, the methods are presented about improving the Apriori algorithm efficiency, which reduces a lot of time of scanning database and shortens the computation time of the algorithm.
Modeling of miRNA and Drug Action in the EGFR Signaling Pathway

MicroRNAs have gained significant interest due to their widespread occurrence and diverse functions as regulatory molecules, which are essential for cell division, growth, development and apoptosis in eukaryotes. The epidermal growth factor receptor (EGFR) signaling pathway is one of the best investigated cellular signaling pathways regulating important cellular processes and its deregulation is associated with severe diseases, such as cancer. In this study, we introduce a systems biological model of the EGFR signaling pathway integrating validated miRNA-target information according to diverse studies, in order to demonstrate essential roles of miRNA within this pathway. The model consists of 1241 reactions and contains 241 miRNAs. We analyze the impact of 100 specific miRNA inhibitors (anit-miRNAs) on this pathway and propose that the embedded miRNA-network can help to identify new drug targets of the EGFR signaling pathway and thereby support the development of new therapeutic strategies against cancer.
Online handwritten devanagari stroke recognition using extended directional features

This paper describes a new feature set, called the extended directional features (EDF) for use in the recognition of online handwritten strokes. We use EDF specifically to recognize strokes that form a basis for producing Devanagari script, which is the most widely used Indian language script. It should be noted that stroke recognition in handwritten script is equivalent to phoneme recognition in speech signals and is generally very poor and of the order of 20% for singing voice. Experiments are conducted for the automatic recognition of isolated handwritten strokes. Initially we describe the proposed feature set, namely EDF and then show how this feature can be effectively utilized for writer independent script recognition through stroke recognition. Experimental results show that the extended directional feature set performs well with about 65+% stroke level recognition accuracy for writer independent data set.
Study of Voltammetric Determination of Carcinogenic 1-Nitropyrene and 1-Aminopyrene Using a Glassy Carbon Paste Electrode

Linear scan voltammetry (LSV), differential pulse voltammetry (DPV) and voltammetry with adsorptive accumulation (AAV) at a glassy carbon paste electrode (GCPE) based on mixing glassy carbon spherical microparticles with an organic pasting liquid were used for the determination of trace amounts of carcinogenic 1-nitropyrene (1-NP) (the limit of determination around 2.10-5 mol L-1 for LSV and 2.10-6 mol L-1 for DPV) and 1-aminopyrene (1-AP) (the limit of determination around 3.10-6 mol L-1 for LSV, 1.10-6 mol L-1 for DPV and 1.10-7 mol L-1 for AAV) using a Britton-Robinson buffer – methanol mixture as a base electrolyte. The main advantage of this new type of electrode is its full compatibility with media containing a high amount of organic solvent (methanol, acetonitrile)
Long-term effects of low-intensity exercise training on fat metabolism in weight-reduced obese men.

The aim of the present study was to investigate the effect of long-term continuation of low-intensity exercise training on weight maintenance, substrate metabolism, and beta-adrenergic-mediated fat oxidation in weight-reduced obese men. Preceding this part of the study, subjects lost 15 +/- 6 kg of body weight by energy restriction with or without low-intensity exercise training. Twenty-nine subjects (diet group, n = 15; diet + exercise group, n = 14) participated in the follow-up study of 40 weeks in which the former diet + exercise group continued their exercise training program. Pre- and postfollow-up, measurements of body weight, body composition, maximal aerobic capacity and substrate oxidation during rest, exercise, and recovery with or without infusion of the beta-adrenergic antagonist, propranolol (PRP), were performed. Over the follow-up period, body weight, fat mass, and fat free mass increased in both groups (P <.0001) without differences between groups. Attendance at exercise training sessions was negatively correlated with regain of body weight (r = -.6, P <.05). Relative fat oxidation, energy expenditure, and beta-adrenergic-mediated fat oxidation during rest, exercise, and recovery were maintained over the follow-up period in both groups. Continuation of low-intensity exercise training after weight reduction did not limit regain of body weight, unless exercise training was frequently performed. Relative (beta-adrenergic-mediated) fat oxidation and energy expenditure were maintained at postdiet level whether or not low-intensity exercise training was performed during follow-up.