Miracle-dz/UniFAR-FTUs huggingface dataset:
TGF-b 1 as an Endogenous Defender Against Macrophage-Triggered Stromelysin Gene Expression in the Glomerulus 1

Recent investigation has indicated that TGFb1, the macrophage (Mf) deactivator, may attenuate Mf-mediated acute glomerular injury. Using stromelysin as an indicator, this study investigated whether and how endogenous TGFb1 modulates the glomerular cell activation triggered by Mf. Rat mesangial cells were stably transfected with a cDNA encoding the active form of TGFb1 and a cDNA coding for a dominant-negative mutant of the TGF-bR type II. Compared with mock-transfected cells, TGF-b1 transfectants exhibited blunted expression of stromelysin in response to the M f-derived, inflammatory cytokine IL-1b. In contrast, mesangial cells expressing the dominant-interfering TGFbR showed enhanced expression of stromelysin in response to IL-1 b, suggesting that endogenous TGFb functions as an autocrine inhibitor of the IL-1 response. In isolated, normal rat glomeruli, externally added TGF-b1 suppressed the induction of stromelysin by mediators that were elaborated by activated M f. Similarly, when isolated, nephritic glomeruli producing the active form of TGF-b1 were stimulated by IL-1b or M f-conditioned medium, the induction of stromelysin was dramatically suppressed as compared with normal glomeruli. To investigate whether endogenous TGF-b1 affects the glomerular cell activation triggered by Mf, a technique for adoptive Mf transfer was used. LPS-stimulated reporter M f were transferred into either normal rat glomeruli or nephritic glomeruli expressing active TGF-b1. In the normal glomeruli, stromelysin expression was markedly induced in resident cells after the transfer of activated Mf. This induction was substantially repressed in those glomeruli producing active TGFb1. These results reinforce the idea that TGFb1 is an endogenous defender that attenuates certain actions of infiltrating Mf in the glomerulus. The Journal of Immunology,1998, 160: 5163–5168.
Neural Substrates of Fluid Reasoning: An fMRI Study of Neocortical Activation during Performance of the Raven's Progressive Matrices Test

We examined brain activation, as measured by functional magnetic resonance imaging, during problem solving in seven young, healthy participants. Participants solved problems selected from the Raven's Progressive Matrices Test, a test known to predict performance on a wide range of reasoning tasks. In three conditions, participants solved problems requiring (1) analytic reasoning; (2) figural or visuospatial reasoning; or (3) simple pattern matching that served as a perceptual-motor control. Right frontal and bilateral parietal regions were activated more by figural than control problems. Bilateral frontal and left parietal, occipital, and temporal regions were activated more by analytic than figural problems. All of these regions were activated more by analytic than match problems. Many of these activations occurred in regions associated with working memory. Figural reasoning activated areas involved in spatial and object working memory. Analytic reasoning activated additional areas involved in verbal working memory and domain-independent associative and executive processes. These results suggest that fluid reasoning is mediated by a composite of working memory systems.
Safe Composit ion of Product Lines

Programs of a software product line can be synthesized by composing modules that implement features. Besides high-level domain constraints that govern the compatibility of features, there are also low-level implementation constraints: a feature module can reference classes that are defined in other feature modules. Safe composition is the guarantee that programs composed from feature modules are absent of references to undefined classes, methods, and variables. We show how safe composition can be guaranteed for AHEAD product lines using feature models and SAT solvers.
Brain Activation During Autobiographical Memory Retrieval with Special Reference to Default Mode Network

Recent neuroimaging studies have suggested that brain regions activated during retrieval of autobiographical memory (ABM) overlap with the default mode network (DMN), which shows greater activation during rest than cognitively demanding tasks and is considered to be involved in self-referential processing. However, detailed overlap and segregation between ABM and DMN remain unclear. This fMRI study focuses first on revealing components of the DMN which are related to ABM and those which are unrelated to ABM, and second on extracting the neural bases which are specifically devoted to ABM. Brain activities relative to rest during three tasks matched in task difficulty assessed by reaction time were investigated by fMRI; category cued recall from ABM, category cued recall from semantic memory, and number counting task. We delineated the overlap between the regions that showed less activation during semantic memory and number counting relative to rest, which correspond to the DMN, and the areas that showed greater or less activation during ABM relative to rest. ABM-specific activation was defined as the overlap between the contrast of ABM versus rest and the contrast of ABM versus semantic memory. The fMRI results showed that greater activation as well as less activation during ABM relative to rest overlapped considerably with the DMN, indicating that the DMN is segregated to the regions which are functionally related to ABM and the regions which are unrelated to ABM. ABM-specific activation was observed in the left-lateralized brain regions and most of them fell within the DMN.
On time series sensor data segmentation for fall and activity classification

The vast amount of literature on human ambulation and Activities of Daily Living (ADL) events classification has highlighted significant details on most aspects of the research area including: monitoring techniques, Wearable Sensor-based Monitoring Device (WSMD) placement on human body parts, and ambulation and ADL data collection methods, among others. However literature has failed to highlight meaningful details on one of the most important aspects of such studies, sensor data segmentation for feature extraction. The choice of segmentation techniques is in general very important, because inappropriate segmentation will most likely result in features without discriminant power. No classifier of whatever sophistication will give meaningful results with features that have no discriminant power. The optimal segmentation technique has been empirically investigated using sensor data from a bi-axial accelerometer. Results of the empirical investigation are presented.
Random testing of C calling conventions

In a C compiler, function calls are difficult to implement correctly because they must respect a platform-specific calling convention. But they are governed by a simple invariant: parameters passed to a function must be received unaltered. A violation of this invariant signals an inconsistency in a compiler. We automatically test the consistency of C compilers using randomly generated programs. An inconsistency manifests itself as an assertion failure when compiling and running the generated code. The generation of programs is type-directed and can be controlled by the user with composable random generators in about 100 lines of Lua. Lua is a scripting language built into our testing tool that drives program generation. Random testing is fully automatic, requires no specification, yet is comparable in effectiveness with specification-based testing from prior work. Using this method, we uncovered 13 new bugs in mature open-source and commercial C compilers.
Phase II Study of Bevacizumab in Combination with Sorafenib in Recurrent Glioblastoma (N0776): A North Central Cancer Treatment Group Trial

Purpose: We hypothesized that vertical blockade of VEGF signaling by combining bevacizumab with sorafenib in patients with recurrent glioblastoma would result in a synergistic therapeutic effect. We also investigated whether VEGF, VEGFR2 and hypoxia-inducible factor-1α single-nucleotide polymorphisms (SNP), circulating biomarkers of angiogenesis, and MRI markers such as apparent diffusion coefficient (ADC) are correlated with treatment efficacy and/or toxicity. Experimental Design: Patients received bevacizumab (5 mg/kg every 2 weeks) with sorafenib (200 mg twice a day, weekly, days 1–5; group A). Due to toxicity, the starting sorafenib dose was subsequently modified to 200 mg every day (group B). Results: Fifty-four patients were enrolled: 19 patients in group A and 35 in group B. Objective response rate was 18.5% with median duration of 6.7 months (range 0.5–24.1 months). Six-month progression-free survival (PFS6) was 20.4% (11/54), and median overall survival (OS) was 5.6 months [95% confidence interval (CI), 4.7–8.2]; outcome was similar between the two dose groups. We identified SNPs in the VEGF and VEGFR2 promoter regions, which were associated with PFS6 (P < 0.022). Among molecular markers of angiogenesis, a higher log2 baseline level of stromal cell–derived factor-1 was associated with PFS6 success (P = 0.04). Circulating endothelial cells decreased during treatment with subsequent increase at disease progression (P = 0.022). Imaging analysis showed a trend associating ADC-L with poor outcome. Conclusions: The bevacizumab/sorafenib combination did not improve outcome of patients with recurrent glioblastoma versus historic bevacizumab-treated controls. Biologic markers of response and resistance to bevacizumab in gliomas were identified which merit prospective validation. Clin Cancer Res; 19(17); 4816–23. ©2013 AACR.
Diagnosis of heart disease patients using fuzzy classification technique

Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart patient. Various techniques in Data Mining have been applied to predict the heart disease patients. But, the uncertainty in data was not removed with the techniques available in data mining and implemented by various authors. To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty and fuzzified data was used to predict the heart disease patients. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum Euclidean distance Fuzzy K-NN classifier was designed to classify the training and testing data belonging to different classes. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.
Convex hull generation methods for polytopic representations of LPV models

The paper focuses on the control design of LPV and qLPV models via TS fuzzy model representation and Linear Matrix Inequality (LMI) based design under the Parallel Distributed Compensation control design framework. The related literature reports considerable research on how to manipulate with LMI's to achieve better control performance. This paper shows that this effort is not enough, the optimization of the control performance must include the convex hull manipulation on the TS fuzzy model beside manipulating with the LMIs. The LMI's guaranty the optimal solution for a given convex hull, but the convex hull representation is not invariant. Furthermore, the solutions by LMI's are very sensitive for the convex hull. The paper proposes a systematic concept for various convex hull manipulation method.
A method for specifying non-radiating, monochromatic, scalar sources and their fields

The theory and results concerning the class of spherically symmetric non-radiating sources and fields established in an earlier paper by Gamliel et al are generalized to the non-spherically symmetric case. A procedure is described for constructing bases for the spaces of all non-radiating sources and associated fields confined to a spherical volume. An example is presented illustrating the developed theory.
An Efficient Distributed Algorithm to Identify and Traceback DDoS Traffic

Distributed denial-of-service attack is one of the most pressing security problems that the Internet community needs to address. Two major requirements for effective traceback are (i) to quickly and accurately locate potential attackers and (ii) to filter attack packets so that a host can resume the normal service to legitimate clients. Most of the existing IP traceback techniques focus on tracking the location of attackers after-the-fact. In this work, we provide an efficient methodology for locating potential attackers who employ the flood-based attack. We propose a distributed algorithm so that a set of routers can correctly (in a distributed sense) gather statistics in a coordinated fashion and that a victim site can deduce the local traffic intensities of all these participating routers. We prove the correctness of our distributed algorithm, and given the collected statistics, we provide a method for the victim site to locate attackers who sent out dominating flows of packets. The proposed distributed traceback methodology can also complement and leverage on the existing ICMP traceback so that a more efficient and accurate traceback can be obtained. We carry out simulations to illustrate that the proposed methodology can locate the attackers in a short period of time. Moreover, the applications as well as the limitations of the proposed methodology are covered. We believe this work also provides the theoretical foundation on how to correctly and accurately perform distributed measurement and traffic estimation on the Internet.
Effect of In Vitro and In Vivo Anakinra on Cytokines Production in Schnitzler Syndrome

IL-1 receptor antagonist anakinra is usually highly efficient in Schnitzler syndrome (SS), a rare inflammatory condition associating urticaria, fever, and IgM monoclonal gammopathy. In this study, we aimed to assess lipopolysaccharide (LPS)-induced production of inflammatory cytokines by peripheral blood mononuclear cells (PBMCs) before and after 1 month of anakinra in patients with SS. LPS-induced production of IL-1β, IL-6 and TNFα was assessed by enzyme-linked immunosorbent assay with and without anakinra in vitro, and before and after 1 month (in vivo condition) of treatment in 2 patients with SS. Spontaneous production of IL-1β, IL-6 and TNF-α by PBMCs was similar in the patients and the healthy controls and was almost undetectable. Stimulation with LPS caused a higher release of cytokines from the patients than from the healthy controls. Before in vivo anakinra start, in vitro adjunction of anakinra reduced the high LPS-induced production of IL-1β and TNFα in both patients and of IL-6 in one patient. After 1 month of treatment with anakinra, while the patients had dramatically improved, there was also a marked reduction in LPS-induced cytokines production, which was almost normalized in one patient. This study shows an abnormal LPS-induced inflammatory cytokines production in SS, which can be decreased or even normalized by in vitro and in vivo anakinra.
Assessing the Influence of the Multisensory Atmosphere on the Taste of Vodka

A preliminary study designed to assess the impact of the multisensory atmosphere (involving variations in lighting and music) on people’s rating of unflavoured and flavoured (citron and raspberry) vodkas is reported. The auditory and visual attributes of the environment were changed as people tasted, and then rated, four unlabelled glasses of vodka (two unflavoured samples, one sample of citron-flavoured and one sample of raspberry-flavoured vodka). Due to the public nature of the event, all participants experienced the same order of auditory and visual changes at the same time. For flavoured vodkas, we saw significant correlations between atmosphere-vodka matching and both liking and fruitiness, and this was reinforced by results showing that those participants who tasted the vodkas in congruent atmospheric conditions (raspberry vodka in red lighting and sweet music, citron vodka in green lighting and sour music) gave significantly higher ratings of liking and fruitiness than did those participants who tasted the vodkas in atmospheric conditions that were incongruent. Specifically, the participants liked the raspberry-flavoured vodka significantly more, and rated it as tasting significantly fruitier, under red lighting while listening to sweet music as compared to under green lighting and listening to sour music. Meanwhile, the unflavoured vodka was liked less under green lighting while listening to the putatively sour music than under white lighting and no music. These results demonstrate how the multisensory attributes of the environment impact on people’s experience of both unflavoured and flavoured vodkas, even when they are not given any information about what they are tasting. Some of the real-world implications for bars (i.e., the “on trade”), experiential events, and other beverage businesses are discussed.
Graph Algebraic Interpretation of Trust Establishment in Autonomic Networks

Trust in networks is the essential foundation for follow-on security mechanisms, such as key management and secure transmission. In this paper, we concentrate on trust establishment in self-organized, distributed and resourceconstraint networks. Such networks pose formidable challenges on trust establishment due to lack of infrastructure and centralized servers. We model our trust establishment strategy as a local voting scheme and discuss its long run behavior. More specifically, we investigate the dynamic evolution of trust within the network, i.e. how trust spreads among nodes, via analyzing its convergence behavior based on algebraic graph and Markov chain theories. As an important security concept, we also study the attack-resistant properties of our trust evaluation scheme in the presence of adversaries.
A Message-Passing Approach to Decentralized Parallel Machine Scheduling

This paper tackles the problem of parallelizing heterogeneous computational tasks across a number of computational nodes (aka agents) where each agent may not be able to perform all the tasks and may have different computational speeds. An equivalent problem can be found in operations research, and it is known as scheduling tasks on unrelated parallel machines (also known as R? Cmax). Given this equivalence observation, we present the spanning tree decentralized task distribution algorithm (ST-DTDA), the first decentralized solution to R? Cmax. ST-DTDA achieves decomposition by means of the min–max algorithm, a member of the generalized distributive law family, that performs inference by message-passing along the edges of a graphical model (known as a junction tree). Specifically, ST-DTDA uses min–max to optimally solve an approximation of the original R? Cmax problem that results from eliminating possible agent-task allocations until it is mapped into an acyclic structure. To eliminate those allocations that are least likely to have an impact on the solution quality, ST-DTDA uses a heuristic approach. Moreover, ST-DTDA provides a per-instance approximation ratio that guarantees that the makespan of its solution (optimal in the approximated R? Cmax problem) is not more than a factor ? times the makespan of the optimal of the original problem. In our empirical evaluation of ST-DTDA, we show that ST-DTDA, with a min-regret heuristic, converges to solutions that are between 78 and 95% optimal whilst providing approximation ratios lower than 3.
Modelling the epidemic spread of an H1N1 influenza outbreak in a rural university town

SUMMARY Knowledge of mechanisms of infection in vulnerable populations is needed in order to prepare for future outbreaks. Here, using a unique dataset collected during a 2009 outbreak of influenza A(H1N1)pdm09 in a university town, we evaluated mechanisms of infection and identified that an epidemiological model containing partial protection of susceptibles best describes H1N1 dynamics in a rural university environment. We found that the protected group was over 14 times less susceptible to H1N1 infection than unprotected susceptibles. Our estimates show that the basic reproductive rate, R 0, was 5·96 (95% confidence interval 5·83–6·61), and, importantly, R 0 could be decreased to below 1 and similar epidemics could be avoided by increasing the proportion of the initial protected group. Moreover, several weeks into the epidemic, this protected group generated more new infections than the unprotected susceptible group, and thus, such protected groups should be taken into account while studying influenza epidemics in similar settings.
CCR 20 th Anniversary Commentary : Bevacizumab in the Treatment of Glioblastoma — The Progress and the

Vredenburgh and colleagues conducted the first phase II study of bevacizumab plus irinotecan in recurrent malignant glioma, confirming the safety and efficacy of bevacizumab. This study, which was published in the February 15, 2007, issue of Clinical Cancer Research, was a stepping stone for subsequent research, leading to regulatory approval of bevacizumab for recurrent glioblastoma. Clin Cancer Res; 21(19); 4248–50. 2015 AACR. See related article by Vredenburgh et al., Clin Cancer Res 2007; 13(4) February 15, 2007;1253–9
Energy Efficiency in the Future Internet: A Survey of Existing Approaches and Trends in Energy-Aware Fixed Network Infrastructures

The concept of energy-efficient networking has begun to spread in the past few years, gaining increasing popularity. Besides the widespread sensitivity to ecological issues, such interest also stems from economic needs, since both energy costs and electrical requirements of telcos' and Internet Service Providers' infrastructures around the world show a continuously growing trend. In this respect, a common opinion among networking researchers is that the sole introduction of low consumption silicon technologies may not be enough to effectively curb energy requirements. Thus, for disruptively boosting the network energy efficiency, these hardware enhancements must be integrated with ad-hoc mechanisms that explicitly manage energy saving, by exploiting network-specific features. This paper aims at providing a twofold contribution to green networking. At first, we explore current perspectives in power consumption for next generation networks. Secondly, we provide a detailed survey on emerging technologies, projects, and work-in-progress standards, which can be adopted in networks and related infrastructures in order to reduce their carbon footprint. The considered approaches range from energy saving techniques for networked hosts, to technologies and mechanisms for designing next-generation and energy-aware networks and networking equipment.