Miracle-dz/UniFAR-FTUs huggingface dataset:
Roles of Caloric Restriction, Ketogenic Diet and Intermittent Fasting during Initiation, Progression and Metastasis of Cancer in Animal Models: A Systematic Review and Meta-Analysis

Background The role of dietary restriction regimens such as caloric restriction, ketogenic diet and intermittent fasting in development of cancers has been detected via abundant preclinical experiments. However, the conclusions are controversial. We aim to review the relevant animal studies systematically and provide assistance for further clinical studies. Methods Literatures on associations between dietary restriction and cancer published in PubMed in recent twenty years were comprehensively searched. Animal model, tumor type, feeding regimen, study length, sample size, major outcome, conclusion, quality assessment score and the interferential step of cancer were extracted from each eligible study. We analyzed the tumor incidence rates from 21 studies about caloric restriction. Results Fifty-nine studies were involved in our system review. The involved studies explored roles of dietary restriction during initiation, progression and metastasis of cancer. About 90.9% of the relevant studies showed that caloric restriction plays an anti-cancer role, with the pooled OR (95%CI) of 0.20 (0.12, 0.34) relative to controls. Ketogenic diet was also positively associated with cancer, which was indicated by eight of the nine studies. However, 37.5% of the related studies obtained a negative conclusion that intermittent fasting was not significantly preventive against cancer. Conclusions Caloric restriction and ketogenic diet are effective against cancer in animal experiments while the role of intermittent fasting is doubtful and still needs exploration. More clinical experiments are needed and more suitable patterns for humans should be investigated.
RS-Seismic Processing and Web-Visualization

The University of Tromsø The Arctic University of Norway (uit) is conducting regular marine seismic acquisition cruises for scientific and educational purposes in the polar regions of the Norwegian Sea and beyond. Leading experts in the field currently employed by the Department of Geology at uit, have found the current seismic visualization tools lacking in several fields. The available seismic software provides a multitude of settings and filters to improve visualization, but has limitations when it comes to user friendliness, and lacks or has shortcomings for data interaction. Because of these limitations the scientists are still reverting back to using thermal paper plots for seismic data interaction. The thermal printers in the possession of uit are old, bulky and prone to mechanical failure, and are expensive to replace. The work done in this thesis attempts to address the needs of the Department of Geology, and as a response to these needs presents the system RS. A system to process, visualize and interact with both "live" and previously recorded seismic 2D data. RS provides processing and filtering of seismic data, and presents this data in a web based user interface, using an Open Source JavaScript Tile viewer to visualize the seismic data. RS also consists of among other: a Go1 two tiered backend system, a custom built tile maker, web sockets for bidirectional communication and NoSQL database storage. The goal of the system is to provide a new visualization tool to replace or supplement current visualization platforms. RS does this by presenting a visualization client which can run on any device with a modern browser, giving every authorized person on a seismic vessel the ability to view and interact with seismic data. To present seismic plots to the end user, RS uses binary seismic data acquired by existing seismic software and hardware. This data is a combination of the seismic data and a variety of metadata from sensors aboard the vessel. Data is filtered for noise, and cached before images are created. These images are served by a web server, and made available to the end user through his 1. http://golang.org/
Consensus Computation in Unreliable Networks: A System Theoretic Approach

This paper addresses the problem of ensuring trustworthy computation in a linear consensus network. A solution to this problem is relevant for several tasks in multi-agent systems including motion coordination, clock synchronization, and cooperative estimation. In a linear consensus network, we allow for the presence of misbehaving agents, whose behavior deviate from the nominal consensus evolution. We model misbehaviors as unknown and unmeasurable inputs affecting the network, and we cast the misbehavior detection and identification problem into an unknown-input system theoretic framework. We consider two extreme cases of misbehaving agents, namely faulty (non-colluding) and malicious (Byzantine) agents. First, we characterize the set of inputs that allow misbehaving agents to affect the consensus network while remaining undetected and/or unidentified from certain observing agents. Second, we provide worst-case bounds for the number of concurrent faulty or malicious agents that can be detected and identified. Precisely, the consensus network needs to be 2k+1 (resp. k+1) connected for k malicious (resp. faulty) agents to be generically detectable and identifiable by every well behaving agent. Third, we quantify the effect of undetectable inputs on the final consensus value. Fourth, we design three algorithms to detect and identify misbehaving agents. The first and the second algorithm apply fault detection techniques, and affords complete detection and identification if global knowledge of the network is available to each agent, at a high computational cost. The third algorithm is designed to exploit the presence in the network of weakly interconnected subparts, and provides local detection and identification of misbehaving agents whose behavior deviates more than a threshold, which is quantified in terms of the interconnection structure.
On how characteristics that hinder test data generation using symbolic execution combine: An analysis of the SF100 benchmark

Symbolic execution has made great strides in the last two decades. However, some characteristics of programs still pose significant challenges to test data generation techniques that use symbolic execution and constraint solving. These challenges present themselves in the form of path explosion, constraint complexity, dependency, and exception-dependent paths. The few studies conducted to date to examine the distribution of such characteristics only investigate them separately. We posit that understanding how the combination of these characteristics appear in real world software may provide a more in-depth knowledge of the limitations of current tools and test data generation techniques. Thus, we analyzed a benchmark comprising 100 open source Java programs to get some insight into the frequency of methods that have combinations of the studied characteristics and the distribution of methods whose path constraints share similar structures.
Fluorescent Protein Voltage Probes Derived from ArcLight that Respond to Membrane Voltage Changes with Fast Kinetics

We previously reported the discovery of a fluorescent protein voltage probe, ArcLight, and its derivatives that exhibit large changes in fluorescence intensity in response to changes of plasma membrane voltage. ArcLight allows the reliable detection of single action potentials and sub-threshold activities in individual neurons and dendrites. The response kinetics of ArcLight (τ1-on ~10 ms, τ2-on ~ 50 ms) are comparable with most published genetically-encoded voltage probes. However, probes using voltage-sensing domains other than that from the Ciona intestinalis voltage sensitive phosphatase exhibit faster kinetics. Here we report new versions of ArcLight, in which the Ciona voltage-sensing domain was replaced with those from chicken, zebrafish, frog, mouse or human. We found that the chicken and zebrafish-based ArcLight exhibit faster kinetics, with a time constant (τ) less than 6ms for a 100 mV depolarization. Although the response amplitude of these two probes (8-9%) is not as large as the Ciona-based ArcLight (~35%), they are better at reporting action potentials from cultured neurons at higher frequency. In contrast, probes based on frog, mouse and human voltage sensing domains were either slower than the Ciona-based ArcLight or had very small signals.
Plasma 25-hydroxyvitamin D levels, vitamin D intake, and pancreatic cancer risk or mortality: a meta-analysis

Background The associations between vitamin D status, including plasma 25-hydroxyvitamin D [25(OH)D] levels and vitamin D intake, and pancreatic cancer risk and mortality are inconsistent. The aims of this study are to evaluate the antitumor and therapeutic effects of vitamin D status for pancreatic cancer patients. Methods A literature search for relevant studies was conducted using PubMed and Embase databases. Risk ratio (RR), hazard ratio (HR), and 95% confidence interval (CI) were used as the effect measures. All statistical analyses were performed using Stata software 12.0. Results Our results indicated that high plasma 25(OH)D levels were inversely associated with pancreatic cancer mortality without significant heterogeneity (HR=0.81, 95% CI=0.68–0.96). However, high plasma 25(OH)D levels could not reduce pancreatic cancer risk (RR=1.02, 95% CI=0.66–1.57). Moreover, vitamin D intake was also not associated with pancreatic cancer risk (RR=1.11, 95% CI=0.67–1.86) Conclusions Our results indicate that high plasma 25(OH)D levels were significantly associated with improved survival in pancreatic cancer patients. However, there were no significant associations between vitamin D intake or plasma 25(OH)D levels and pancreatic cancer risk.
Towards Standardized Waveguide Sizes and Interfaces for Submillimeter Wavelengths

This paper describes an activity that has begun recently to develop an international standard for rectangular metallic waveguides and their interfaces for frequencies of 110 GHz and above. The IEEE’s Microwave Theory and Techniques Society (MTT-S) is sponsoring the development of this standard. The MTT-S Standards Committee has set up the P1785 Working Group tasked with undertaking the work that is necessary to write this standard. This paper describes the work to date of this Working Group and the future activities that will be necessary to complete the standard.
Functional comparison of transactivation by human retrovirus rev and rex genes

The effect of rev-responsive element deletion on human immunodeficiency virus type 1 (HIV-1) and type 2 (HIV-2) gene expression was examined. The phenotypes of HIV-1 and HIV-2 provirus DNAs lacking the rev-responsive element, as determined by transfection experiments, were indistinguishable from those of virus DNAs carrying rev gene mutations. By using rev-response elements derived from these two viruses, we developed two monitoring systems to evaluate the functionality of HIV-1 rev, HIV-2 rev, and human T-lymphotropic virus type I rex. In both systems, HIV-1 rev and human T-lymphotropic virus type I rex transactivated HIV-2 very efficiently. On the contrary, HIV-2 rev and human T-lymphotropic virus type I rex were poor activators of HIV-1. No functional replacement of rex by HIV-2 rev was observed.
Functional Characterization of a Putative Glycine max ELF4 in Transgenic Arabidopsis and Its Role during Flowering Control

Flowering is an important trait in major crops like soybean due to its direct relation to grain production. The circadian clock mediates the perception of seasonal changes in day length and temperature to modulate flowering time. The circadian clock gene EARLY FLOWERING 4 (ELF4) was identified in Arabidopsis thaliana and is believed to play a key role in the integration of photoperiod, circadian regulation, and flowering. The molecular circuitry that comprises the circadian clock and flowering control in soybeans is just beginning to be understood. To date, insufficient information regarding the soybean negative flowering regulators exist, and the biological function of the soybean ELF4 (GmELF4) remains unknown. Here, we investigate the ELF4 family members in soybean and functionally characterize a GmELF4 homologous gene. The constitutive overexpression of GmELF4 delayed flowering in Arabidopsis, showing the ELF4 functional conservation among plants as part of the flowering control machinery. We also show that GmELF4 alters the expression of Arabidopsis key flowering time genes (AtCO and AtFT), and this down-regulation is the likely cause of flowering delay phenotypes. Furthermore, we identified the GmELF4 network genes to infer the participation of GmELF4 in soybeans. The data generated in this study provide original insights for comprehending the role of the soybean circadian clock ELF4 gene as a negative flowering controller.
Kinetic Analysis of the Role of Intersubunit Interactions in Human Immunodeficiency Virus Type 1 Capsid Protein Assembly In Vitro

ABSTRACT The human immunodeficiency virus type 1 (HIV-1) capsid protein (CA) plays a crucial role in both assembly and maturation of the virion. Numerous recent studies have focused on either the soluble form of CA or the polymer end product of in vitro CA assembly. The CA polymer, in particular, has been used to study CA-CA interactions because it is a good model for the CA interactions within the virion core. However, analysis of the process of in vitro CA assembly can yield valuable insights into CA-CA interactions and the mechanism of core assembly. We describe here a method for the analysis of CA assembly kinetics wherein the progress of assembly is monitored by using turbidity. At pH 7.0 the addition of either of the isolated CA domains (i.e., the N or the C domain) to an assembly reaction caused a decrease in the assembly rate by competing for binding to the full-length CA protein. At pH 8.0 the addition of the isolated C domain had a similar inhibitory affect on CA assembly. However, at pH 8.0 the isolated N domain had no affect on the rate of CA assembly but, when mixed with the C domain, it alleviated the C-domain inhibition. These data provide biochemical evidence for a pH-sensitive homotypic N-domain interaction, as well as for an N- and C-domain interaction.
Voted NER System using Appropriate Unlabeled Data

This paper reports a voted Named Entity Recognition (NER) system with the use of appropriate unlabeled data. The proposed method is based on the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) and has been tested for Bengali. The system makes use of the language independent features in the form of different contextual and orthographic word level features along with the language dependent features extracted from the Part of Speech (POS) tagger and gazetteers. Context patterns generated from the unlabeled data using an active learning method have been used as the features in each of the classifiers. A semi-supervised method has been used to describe the measures to automatically select effective documents and sentences from unlabeled data. Finally, the models have been combined together into a final system by weighted voting technique. Experimental results show the effectiveness of the proposed approach with the overall Recall, Precision, and F-Score values of 93.81%, 92.18% and 92.98%, respectively. We have shown how the language dependent features can improve the system performance.
The Effects of Data Quality on Machine Learning Algorithms

Our research focuses on two goals. First, we seek to demonstrate that data quality is an important component of machine learning tools and that the quality of our data should be carefully considered when developing and using these tools. We believe that while the importance of data quality is now understood in the business community, where researchers have equated quality decision making to earnings, in the machine learning community this realization has not yet occurred. Therefore, we embark upon research into the effects of data quality upon these algorithms in an effort to demonstrate that data quality is a large factor in the outcomes of these algorithms and should be given more respect in their design. Second, after providing evidence that data quality is a large factor in these algorithms, we develop and test some preliminary methods which incorporate data quality assessments, thus creating more robust and useful algorithms. We believe these modifications and methods can have profound effects upon the usage of these machine learning algorithms in actual practice, particularly in the Law Enforcement community.
Using Dominance Chains to Detect Annotation Variants in Parsed Corpora

In this paper, some results on the detection of variation in annotation in parsed corpora or tree banks are presented. Tree banks are generally built by means of using both automatic tools (i.e., taggers and parsers) and human intervention. In this process, inconsistencies (and, thus, variation) in the annotation arise, caused by a number of factors, for instance, disagreement in interpretation, incomplete or unclear annotation guidelines, etc. In this study, the algorithm for automatic detection of variation proposed in [1] is evaluated against the Tycho Brahe Corpus (TBC, [2]) and compared to an alternative implementation where variants of annotation are characterized by means of "dominance chains". Experimental results demonstrate that the modified version has better relative precision and recall than the original method.
Congestion Network Problems and Related Games

This paper analyzes network problems with congestion effects from a cooperative game theoretic perspective. It is shown that for network problems with convex congestion costs, the corresponding games have a non-empty core. If congestion costs are concave, then the corresponding game has not necessarily core elements, but it is derived that, contrary to the convex congestion situation, there always exist optimal tree networks. Extensions of these results to a class of relaxed network problems and associated games are derived.
IL-4 Suppresses the Expression and the Replication of Hepatitis B Virus in the Hepatocellular Carcinoma Cell Line Hep3B 1

IL-4 has been known as a Th2 cytokine and can act on B cells, T cells, and monocytes. In this study we demonstrate that IL-4Rs are expressed on human hepatocellular carcinoma (HCC) cells. We found that IL-4 suppresses hepatitis B surface Ag (HBsAg) mRNA and HBsAg production in the Hep3B cell line, which contains an integrated hepatitis B virus (HBV) genome and constitutively secretes HBsAg. When Hep3B cells are further transfected with the plasmid pHBV3.6 that contains >1 U of HBV genome, IL-4 could suppress the production of all HBV RNA and secreted HBsAg and hepatitis B virus e Ag. Furthermore, an endogenous DNA polymerase activity assay shows a decrease in HBV DNA after IL-4 treatment. Using luciferase reporter assays we have demonstrated that IL-4 could suppress the activity of the surface promoter II and the core promotor (CP). To delineate how IL-4 suppressed the transcription of HBV genes, we have examined the effect of IL-4 on the expression of transcription factors that are known to bind to the core upstream regulatory sequence, which colocalizes with enhancer II of the HBV genome. Our results demonstrate that IL-4 suppresses the expression of C/EBPα. Furthermore, overexpression of C/EBPα blocked 43 and 30% of the IL-4-mediated suppression of CP activity and IL-4-induced suppression of pregenomic RNA, respectively. Finally, we have demonstrated that mutations affecting the C/EBPα-binding sites on core upstream regulatory sequence/enhancer II completely abolish the IL-4-mediated suppression of CP activity. Thus, down-regulation of C/EBPα may be involved in the anti-HBV effect of IL-4 in Hep3B cells.
The relationship between sleep quality with fatigue severity and academic performance of nursing students

Background & Aim : Increasing students’ academic stress especially during taking final exams may result in sleep disorders, fatigue, poor academic performance and low functional efficiency. The aim of this study was to determine the relationship between sleep quality with fatigue severity and academic performance of nursing students. Methods & Materials : In a cross-sectional study, the sleep quality and fatigue of nursing students (n=138) of AJA University of Medical Sciences in Tehran, Iran were assessed using Pittsburgh Sleep Quality Index and Fatigue Severity Scale within last week of exams of second semester in 2013. Subjects filled demographic questionnaires and the response rate was 86.8%. The data were analyzed using SPSS software with descriptive and analytic statistics including Chi square, Pearson and Spearman’s rho correlation coefficients. Results : Sleep quality in 64.4% of students was poor. About 77% of students suffered from fatigue in moderate to severe levels. The mean of total scores of lessens was 16.16. Chi-square test and Pearson correlation coefficient did not indicate significant relationship between sleep quality and age, gender, education semester and educational level, material status, habitancy location, caffeine consuming, smoking, experience of suffering from chronic and psychological diseases, working and also experience of major stress (p>0.05). Spearman’s rho correlation coefficient indicated significant relationship between sleep quality and fatigue severity (p≤0.0000), whereas Pearson correlation coefficient did not show significant relationship between sleep quality and academic performance and also between fatigue severity and academic performance (p>0.05). Conclusion : Prevalence of poor sleep quality and fatigue during final exams in nursing students is high. Considering importance of sleep quality and quantity and its effect on academic performances, creating suitable conditions for improving sleep quality is suggested.
eTrain: Making Wasted Energy Useful by Utilizing Heartbeats for Mobile Data Transmissions

With the rapid proliferation of smartphones, hundreds of millions of mobile users are attracted to Instant Messaging (IM) apps. While such apps have brought convenience to our life, it comes with the price of great energy consumption, as these apps keep sending heartbeat messages to the server periodically in order to maintain an always-online connection. These frequent and fragmented transmissions result in a considerable amount of energy waste. In this paper, we investigate the "cost and potential of heartbeats". We quantify power consumption of heartbeats of real-world IM apps through extensive measurements. The measurement results confirm that huge power consumption is induced by heartbeats. The goal of this paper is to save energy by turning the energy wastage of heartbeats into transmitting useful data. Thus, we develop eTrain, a transmission management system running on Android phones, which takes advantage of IM heartbeats (as trains) to piggyback aggregated delay-tolerant apps' data such as e-mail and Weibo (as cargoes) via an online transmission strategy, so as to minimize the cumulative tail energy without sacrificing user-specified deadlines. Compared to other existing works, eTrain can reduce more energy consumption under the same settings. Experiments conducted on smartphones show that eTrain can achieve 12%-33% energy saving in various application scenarios.
A geographic source routing protocol for traffic sensing in urban environment

Acquiring the real time traffic information is very important for users of vehicular ad hoc networks. This work proposed a novel multicasting protocol called geographic source routing to help drivers understand the current traffic conditions in order to avoid congestion and make optimal routing plans. By using modified directed broadcasting, the broadcast storm problem is eliminated. The influence of traffic regulations is also analyzed and a packet forwarding strategy is introduced to be integrated with the protocol. The simulation results show that this proposal can significantly improve the bandwidth utilization and reduce the packet delay.