Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential biodistribution of oncolytic poxvirus administered systemically in an autochthonous model of hepatocellular carcinoma

Preclinical studies have demonstrated that, unlike oncolytic adenoviruses, oncolytic vaccinia viruses can reach implanted tumors upon systemic injection. However, the biodistribution of this oncolytic agent in in situ autochthonous tumor models remains poorly characterized. In the present study, we assessed this biodistribution in a model of mouse hepatocellular carcinoma (HCC) obtained after injection of the carcinogen diethylnitrosamine (DEN).
Whole Body Vibration Training - Improving Balance Control and Muscle Endurance

Exercise combined with whole body vibration (WBV) is becoming increasingly popular, although additional effects of WBV in comparison to conventional exercises are still discussed controversially in literature. Heterogeneous findings are attributed to large differences in the training designs between WBV and “control” groups in regard to training volume, load and type. In order to separate the additional effects of WBV from the overall adaptations due to the intervention, in this study, a four-week WBV training setup was compared to a matched intervention program with identical training parameters in both training settings except for the exposure to WBV. In a repeated-measures matched-subject design, 38 participants were assigned to either the WBV group (VIB) or the equivalent training group (CON). Training duration, number of sets, rest periods and task-specific instructions were matched between the groups. Balance, jump height and local static muscle endurance were assessed before and after the training period. The statistical analysis revealed significant interaction effects of group×time for balance and local static muscle endurance (p<0.05). Hence, WBV caused an additional effect on balance control (pre vs. post VIB +13%, p<0.05 and CON +6%, p = 0.33) and local static muscle endurance (pre vs. post VIB +36%, p<0.05 and CON +11%, p = 0.49). The effect on jump height remained insignificant (pre vs. post VIB +3%, p = 0.25 and CON ±0%, p = 0.82). This study provides evidence for the additional effects of WBV above conventional exercise alone. As far as balance and muscle endurance of the lower leg are concerned, a training program that includes WBV can provide supplementary benefits in young and well-trained adults compared to an equivalent program that does not include WBV.
Preanalytical requirements of urinalysis

Urine may be a waste product, but it contains an enormous amount of information. Well-standardized procedures for collection, transport, sample preparation and analysis should become the basis of an effective diagnostic strategy for urinalysis. As reproducibility of urinalysis has been greatly improved due to recent technological progress, preanalytical requirements of urinalysis have gained importance and have become stricter. Since the patients themselves often sample urine specimens, urinalysis is very susceptible to preanalytical issues. Various sampling methods and inappropriate specimen transport can cause important preanalytical errors. The use of preservatives may be helpful for particular analytes. Unfortunately, a universal preservative that allows a complete urinalysis does not (yet) exist. The preanalytical aspects are also of major importance for newer applications (e.g. metabolomics). The present review deals with the current preanalytical problems and requirements for the most common urinary analytes.
Exploring the Practical Limits of Cooperative Awareness in Vehicular Communications

We perform an extensive study of cooperative awareness in vehicular networks based on periodic message exchange. We start by analyzing measurements collected on four test sites across Europe. To measure cooperative awareness, we use three metrics: 1) neighborhood awareness ratio, 2) ratio of neighbors above range, and 3) packet delivery rate. Using the collected data, we define a simple model for calculating neighborhood awareness given a packet delivery ratio for an environment. Finally, we perform realistic large-scale simulations to explore the achievable performance of cooperative awareness under realistic transmit power and transmit rate constraints. Our measurements and simulation results show the following: 1) Above a certain threshold, there is little benefit in increasing the cooperative message rate to improve awareness; higher transmit power and fewer message transmissions are a better approach, since message delivery is dominated by shadowing; 2) The efficacy of cooperative awareness varies greatly in different environments on both large scale (e.g., 90% awareness is achievable up to 200 m in urban compared to 500 m in highway) and small scale (e.g., vehicles in nearby streets can have significantly different awareness levels); 3) Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications have distinct awareness patterns; 4) Each location has distinct transmit power that achieves high awareness; 5) Achieving high awareness levels results in increased reception of potentially unwanted messages; therefore, a balance needs to be found between awareness and interference, depending on the specific context. We hope that our results will serve as a starting point for designing more effective periodic message exchange services for cooperative awareness.
Genetic Variation in IGF2 and HTRA1 and Breast Cancer Risk among BRCA1 and BRCA2 Carriers

Background:BRCA1 and BRCA2 mutation carriers have a lifetime breast cancer risk of 40% to 80%, suggesting the presence of risk modifiers. We previously identified significant associations in genetic variants in the insulin-like growth factor (IGF) signaling pathway. Here, we investigate additional IGF signaling genes as risk modifiers for breast cancer development in BRCA carriers. Methods: A cohort of 1,019 BRCA1 and 500 BRCA2 mutation carriers were genotyped for 99 single-nucleotide polymorphisms (SNP) in 13 genes. Proportional hazards regression was used to model time from birth to diagnosis of breast cancer for BRCA1 and BRCA2 carriers separately. For linkage disequilibrium (LD) blocks with multiple SNPs, an additive genetic model was used. For an SNP analysis, no additivity assumptions were made. Results: Significant associations were found between risk of breast cancer and LD blocks in IGF2 for BRCA1 and BRCA2 mutation carriers (global P values of 0.009 for BRCA1 and 0.007 for BRCA2), HTRA1 for BRCA1 carriers (global P value of 0.005), and MMP3 for BRCA2 carriers (global P = 0.0000007 for BRCA2). Conclusions: We identified significant associations of genetic variants involved in IGF signaling. With the known interaction of BRCA1 and IGF signaling and the loss of PTEN in a majority of BRCA1 tumors, this suggests that signaling through AKT may modify breast cancer risk in BRCA1 carriers. Impact: These results suggest potential avenues for future research targeting the IGF signaling pathway in modifying risk in BRCA1and BRCA2 mutation carriers. Cancer Epidemiol Biomarkers Prev; 20(8); 1690–702. ©2011 AACR.
A Hybrid Approach to Modified AODV Protocol to Detect and Avoid Wormhole Effected Path over MANET

In MANET mobile node is responsible for route establishment using wireless link where each node behave like both as a host and router. MANET encounter by number of security threat because of its open untrusted environment with little security arrangement, whether security over MANET is not to be enhance up to satisfactory level because of its characteristics. Among all of security threat worm hole is consider to be a very serious security threat over MANET. In worm hole two selfish node which is geographically very far away to each other, makes tunnel between each other to hide their actual location and try to believe that they are true neighbours and makes conversation through the wormhole tunnel. Recently research will focus over wormhole detection and avoiding path but existing technique having lower network overhead, lower battery power consumption in order to longer survival of network with fast response. This paper introduce a modified AODV protocol that detect and avoid wormhole path over MANET towards secure routing , which is based on an hybrid model that encapsulate location, neighbor node and hop count method.
Modelação hierárquica ou multinível. Uma metodologia estatística e um instrumento útil de pensamento na investigação em ciências do desporto

Hierarchical or multilevel modeling. A statistical methodology and a usefull thinking tool of research in sport sciences. The purpose of this paper is to present some fundamental aspects of multilevel or hierarchical modeling to researchers of Sport Sciences within the Portuguese speaking community. It is stressed the relevance of a major concern for considering the multilevel data characteristics presented in most of the research. It shows also the bias present in many conclusions for not considering the clustered nature of data sets researchers have at hand. We then present a didactic example that follows major issues of hierarchical modeling, and suggest some ideas for conducting research in this methodology.
In situ observation of the formation of Fe3O4 in Fe4N (001) due to electron irradiation.

gamma(')-Fe4N was subjected to electron irradiation with the dose rate of 6.8 x 10(23)e m(-2) s(-1) in a 400 kV transmission electron microscope. The first in situ observation of the formation of Fe3O4 (O) on Fe4N (gamma(')) with the orientation relationship of [100](O) parallel [100](gamma(')) and <001>(O) parallel <001>(gamma(')) has been made inside the microscope with the basic column vacuum of (5-6) x 10(-5) Pa. A mechanism is proposed involving the electron-stimulated dissociation of Fe-N chemical bonds, desorption of nitrogen from the surface, adsorption of oxygen to the surface, and the oxidization of excessive metallic iron on the surface.
A Real-Time Power Quality Disturbances Classification Using Hybrid Method Based on S-Transform and Dynamics

This paper proposes a real-time power quality disturbances (PQDs) classification by using a hybrid method (HM) based on S-transform (ST) and dynamics (Dyn). Classification accuracy and run time are mainly considered in our work. The HM firstly uses Dyn to identify the location of the signal components in the frequency spectrum yielded by Fourier transform, and uses inverse Fourier transform to only some of the signal components. Then features from Fourier transform, ST, and Dyn are selected, and a decision tree is used to classify the types of PQD. In order to reduce the influence of Heisenberg' s uncertainty, we proposed that different signal components are windowed by different Gaussian windows, which brings better adaption and flexibility. By the HM, run time of the application has been greatly reduced with satisfactory classification accuracy. Finally, a DSP-FPGA based hardware platform is adopted to test the run time and correctness of the proposed method under real standard signals. Field signal tests have also presented. Both simulations and experiments validate the feasibility of the new method.
Optimizing web traffic via the media scheduling problem

Website traffic varies through time in consistent and predictable ways, with highest traffic in the middle of the day. When providing media content to visitors, it is important to present repeat visitors with new content so that they keep coming back. In this paper we present an algorithm to balance the need to keep a website fresh with new content with the desire to present the best content to the most visitors at times of peak traffic. We formulate this as the media scheduling problem, where we attempt to maximize total clicks, given the overall traffic pattern and the time varying clickthrough rates of available media content. We present an efficient algorithm to perform this scheduling under certain conditions and apply this algorithm to real data obtained from server logs, showing evidence of significant improvements in traffic from our algorithmic schedules. Finally, we analyze the click data, presenting models for why and how the clickthrough rate for new content declines as it ages.
Isopotential Body Surface Mapping in Subjects of All Ages: Emphasis on Low-Level Potentials with Analysis of the Method

In this report we consider the body surface distribution of low-level potentials, particularly those of the U wave, the PR segment, and the ST segment in normal subjects. The long-term objective of this report is to study low-level body surface potentials, but adequate methods are required for that. Therefore, the primary emphasis in this report is artifacts that may occur in these potential distributions. Although receiving a secondary emphasis, the findings show that accurate low-level potential distributions can be recorded and they have interesting and distinctive features. The results present two features that are important in the construction of isopotential maps: 1) The normal distribution of the surface potentials characteristic of these signals, especially the U wave and the PR segment, and 2) the influence of subtracting or not subtracting these patterns in the process of constructing body surface maps for the entire cardiac cycle. An isopotential map method is described for identifying U waves. A reciprocal relationship was found between the normal body surface patterns of the U wave and the PR segment. If the period of the U wave was unfortunately chosen as the time for zero potential reference for all electrodes, the maps subsequently made for QRS and the ST segment were distorted on the anterior chest by an element of potential representing currents of terminal ventricular repolarization during overlapping T and U waves. The implication of these results is significant in that the timing of the reference baseline is critical in determining the pattern of the depicted map. These considerations are especially important in the interpretation of precordial and body surface maps recorded during exercise. The false patterns found can be quite misleading, since the baseline artifacts produce regional changes that may simulate patterns of abnormalities
Input-Dependency Analysis for Hard Real-Time Software

The execution time of software for hard real-time systems must be predictable. Further, safe and not overly pessimistic bounds for the worst-case execution time (WCET) must be computable. We conceived a programming strategy called WCET-oriented programming and a code transformation strategy, the single-path conversion, that aid programmers in producing code that meets these requirements. These strategies avoid respectively eliminate input-data dependencies in the code. The paper describes the formal analysis, based on abstract interpretation, that identifies input-data dependencies in the code and thus forms the basis for the strategies provided for hard real-time code development.
Causal cascade in the stock market from the infrared to the ultraviolet

Causal cascade in the stock market from the " infrared " to the " ultraviolet ". Modelling accurately financial price variations is an essential step underlying portfolio allocation optimization, derivative pricing and hedging, fund management and trading. The observed complex price fluctuations guide and constraint our theoretical understanding of agent interactions and of the organization of the market. The gaussian paradigm of independent normally distributed price increments [1, 2] has long been known to be incorrect with many attempts to improve it. Econometric nonlin-ear autoregressive models with conditional heteroskedasticity[3] (ARCH) and their generalizations [4] capture only imperfectly the volatility correlations and the fat tails of the probability distribution function (pdf) of price variations. Moreover, as far as changes in time scales are concerned, the so-called " aggregation " properties of these models are not easy to control. More recently, the leptokurticity of the full pdf was described by a truncated " additive " Lévy flight model[5, 6] (TLF). Alternatively, Ghashghaie et al.[7] proposed an analogy between price dynamics and hydrodynamic turbulence. In this letter, we use wavelets to decompose the volatility of intraday (S&P500) return data across scales. We show that when investigating two-points correlation functions of the volatility logarithms across different time scales, one reveals the existence of a causal information cascade
P2Y2 receptors activate neuroprotective mechanisms in astrocytic cells

Mechanical or ischemic trauma to the CNS causes the release of nucleotides and other neurotransmitters into the extracellular space. Nucleotides can activate nucleotide receptors that modulate the expression of genes implicated in cellular adaptive responses. In this investigation, we used human 1321N1 astrocytoma cells expressing a recombinant P2Y2 receptor to assess the role of this receptor in the regulation of anti‐apoptotic (bcl‐2 and bcl‐xl) and pro‐apoptotic (bax) gene expression. Acute treatment with the P2Y2 receptor agonist UTP up‐regulated bcl‐2 and bcl‐xl, and down‐regulated bax, gene expression. Activation of P2Y2 receptors was also coupled to the phosphorylation of cyclic AMP responsive element binding protein that positively regulates bcl‐2 and bcl‐xl gene expression. Cyclic AMP responsive element decoy oligonucleotides markedly attenuated the UTP‐induced increase in bcl‐2 and bcl‐xl mRNA levels. Activation of P2Y2 receptors induced the phosphorylation of the pro‐apoptotic factor Bad and caused a reduction in bax/bcl‐2 mRNA expression ratio. All these signaling pathways are known to be involved in cell survival mechanisms. Using cDNA microarray analysis and RT–PCR, P2Y2 receptors were found to up‐regulate the expression of genes for neurotrophins, neuropeptides and growth factors including nerve growth factor 2; neurotrophin 3; glia‐derived neurite‐promoting factor, as well as extracellular matrix proteins CD44 and fibronectin precursor – genes known to regulate neuroprotection. Consistent with this observation, conditioned media from UTP‐treated 1321N1 cells expressing P2Y2 receptors stimulated the outgrowth of neurites in PC‐12 cells. Taken together, our results suggest an important novel role for the P2Y2 receptor in survival and neuroprotective mechanisms under pathological conditions.
MPC and H-Infinity Based Feedback Control of Non-Linear Robotic Manipulator

Pressing demands of accuracy, repeatability, reliability and productivity in today's robotic applications has highlighted significance of modern control techniques. This research proposes two non-linear design techniques, Model Predictive Control (MPC) and H-infinity control for a six Degree Of Freedom (DOF) robotic arm. The derived dynamics of the manipulator based on Euler Lagrange formulation is used to design the control laws. To characterize and validate the performance, the developed laws are then simulated in MATLAB/Simulink. This paper also investigates the coupling effects between various joints and non-linear dynamics of the manipulator. The controller parameters are fine tuned for optimum response. The control is subjected to various test inputs for investigating trajectory tracking performance. The pilot study shows that the proposed control laws are effective for reducing settling time, overshoot and steady state error for various joints. Simulation results demonstrate that the reference motion trajectories can be followed with adequate accuracy.
Predicting positive and negative links in signed social networks by transfer learning

Different from a large body of research on social networks that has focused almost exclusively on positive relationships, we study signed social networks with both positive and negative links. Specifically, we focus on how to reliably and effectively predict the signs of links in a newly formed signed social network (called a target network). Since usually only a very small amount of edge sign information is available in such newly formed networks, this small quantity is not adequate to train a good classifier. To address this challenge, we need assistance from an existing, mature signed network (called a source network) which has abundant edge sign information. We adopt the transfer learning approach to leverage the edge sign information from the source network, which may have a different yet related joint distribution of the edge instances and their class labels. As there is no predefined feature vector for the edge instances in a signed network, we construct generalizable features that can transfer the topological knowledge from the source network to the target. With the extracted features, we adopt an AdaBoost-like transfer learning algorithm with instance weighting to utilize more useful training instances in the source network for model learning. Experimental results on three real large signed social networks demonstrate that our transfer learning algorithm can improve the prediction accuracy by 40% over baseline methods.
First order sliding fuzzy interval type-2 control applied for the steering vehicle control

This paper deals with an adaptive fuzzy type-2 sliding mode control for a steering vehicle control. Two fuzzy type-2 systems are used in order to obtain a global model and to derive an adaptation law control. In fact, to overcome the constraint on the knowledge of the vehicle model, a global model is deduced using type-2 fuzzy system. Then the obtained global T-2 fuzzy vehicle model is used to design a sliding mode steering vehicle control. Sufficient conditions of stability and asymptotic convergence of this controller are presented in Lyapunov sens. The adaptive type-2 fuzzy sliding mode control will be compared to the classical first order sliding mode control. Promising results have been obtained using real data acquired by a laboratory vehicle.
Learning to Freestyle: Hip Hop Challenge-Response Induction via Transduction Rule Segmentation

We present a novel model, Freestyle, that learns to improvise rhyming and fluent responses upon being challenged with a line of hip hop lyrics, by combining both bottomup token based rule induction and top-down rule segmentation strategies to learn a stochastic transduction grammar that simultaneously learns both phrasing and rhyming associations. In this attack on the woefully under-explored natural language genre of music lyrics, we exploit a strictly unsupervised transduction grammar induction approach. Our task is particularly ambitious in that no use of any a priori linguistic or phonetic information is allowed, even though the domain of hip hop lyrics is particularly noisy and unstructured. We evaluate the performance of the learned model against a model learned only using the more conventional bottom-up token based rule induction, and demonstrate the superiority of our combined token based and rule segmentation induction method toward generating higher quality improvised responses, measured on fluency and rhyming criteria as judged by human evaluators. To highlight some of the inherent challenges in adapting other algorithms to this novel task, we also compare the quality of the responses generated by our model to those generated by an out-ofthe-box phrase based SMT system. We tackle the challenge of selecting appropriate training data for our task via a dedicated rhyme scheme detection module, which is also acquired via unsupervised learning and report improved quality of the generated responses. Finally, we report results with Maghrebi French hip hop lyrics indicating that our model performs surprisingly well with no special adaptation to other languages.