Miracle-dz/UniFAR-FTUs huggingface dataset:
Soy, its components, and cancer prevention: a review of the in vitro, animal, and human data.

Introduction The incidence of breast, prostate, and colon cancer, among others, is lower in Asian countries than in the West. People living in China, Japan, and Korea, for example, are 4 to 10 times less likely to be diagnosed with and die from breast and prostate cancer than are people in the United States (1). Traditionally, migrant populations have been examined to determine how much of the difference between ethnic groups is due to genetic tendencies and how much is due to environmental factors. In one such study, cancer incidence was observed among Japanese peoples migrating to the United States (2). Within two generations of migration, cancer mortality rates increased to levels similar to the native Caucasian population. This suggests that environmental and behavioral factors may be more important than genetic factors in determining cancer frequency between populations. Based on epidemiological calculations by Doll and Peto (3, 4), as many as 75-80% of all fatal cancers in the United States are due to extrinsic factors and thus may be preventable. Potentially avoidable environmental factors contributing to cancer frequency include diet, smoking, alcohol consumption, reproductive behavior, infection, geophysical factors ineluding sunlight, and prolonged exposure to extrinsic agents such as fossil fuel combustion products, radioactive waste, dust and fumes, pesticide residues, and food additives (4). Diet, in particular, is one such factor that can vary significantly from country to country and has been estimated to account for up to 35% of all cancer rate differences (4). Although Western and Eastern diets vary widely, one striking distinction between them is the source of protein. Those in the West generally rely on animal protein, whereas Eastern diets emphasize bean, particularly soybean, as a protein source. Soybeans have long been a major component of the Eastem diet. Asians consume an average of 20-80 g of soyfoods per day, whereas Americans eat only 1-3 g daily (5). Commonly consumed Asian soy products include soymilk, tofu, miso, yuba, and tempeh. Moreover, extensive epidemiological, in vitro, and animal data collected over the previous several years suggest that soybean consumption reduces the risk of developing several types of cancer including breast, prostate,
Immunization of Mice with a TolA-Like Surface Protein of Trypanosoma cruzi Generates CD4+T-Cell-Dependent Parasiticidal Activity

ABSTRACT The gene family encoding a trypomastigote-specific protein restricted to the part of the flagellum in contact with the cell body of the trypomastigote form of Trypanosoma cruzi has been isolated, characterized, and expressed in a baculovirus expression system. The gene family contains three tandemly repeated members that have 97 to 100% sequence identity. The predicted protein encoded by the gene family has both significant amino acid sequence identity and other physical and biological features in common with the TolA proteins of Escherichia coli and Pseudomonas aeruginosa. Based on these similarities, we have designated this gene family tolT. Immunization of mice with recombinant TolT generates a population of CD4+ T lymphocytes that recognize T. cruzi-infected macrophages, resulting in the production of gamma interferon (IFN-γ), which leads to NO production and a 50 to 60% reduction in parasite numbers compared to that seen with infected macrophages incubated with naive T cells. This population of T cells also produces both IFN-γ and interleukin 2 (IL-2) but not IL-4 or IL-5 when incubated with spleen cells stimulated with TolT antigen, indicating that they are of the T-helper 1 type. T cells from mice chronically infected with T. cruzi also produce significant levels of IFN-γ when cocultured with macrophages and either TolT protein or paraflagellar rod protein, indicating that both of these flagellar proteins produce positive T-cell responses in mice chronically infected with T. cruzi.
Four-stage dissolved oxygen strategy based on multi-scale analysis for improving spinosad yield by Saccharopolyspora spinosa ATCC49460

Dissolved oxygen (DO) is an important influencing factor in the process of aerobic microbial fermentation. Spinosad is an aerobic microbial‐derived secondary metabolite. In our study, spinosad was used as an example to establish a DO strategy by multi‐scale analysis, which included a reactor, cell and gene scales. We changed DO conditions that are related to the characteristics of cell metabolism (glucose consumption rate, biomass accumulation and spinosad production). Consequently, cell growth was promoted by maintaining DO at 40% in the first 24 h and subsequently increasing DO to 50% in 24 h to 96 h. In an in‐depth analysis of the key enzyme genes (gtt, spn A, spn K and spn O), expression of spinosad and specific Adenosine Triphosphate (ATP), the spinosad yield was increased by regulating DO to 30% within 96 h to 192 h and then changing it to 25% in 192 h to 240 h. Under the four‐phase DO strategy, spinosad yield increased by 652.1%, 326.1%, 546.8%, and 781.4% compared with the yield obtained under constant DO control at 50%, 40%, 30%, and 20% respectively. The proposed method provides a novel way to develop a precise DO strategy for fermentation.
Knowledge Brokering and Organizational Innovation: Founder Imprinting Effects

We empirically examine the innovation consequences of organizational knowledge brokering, the ability to effectively apply knowledge from one technical domain to innovate in another. We investigate how organizational innovation outcomes vary by founders’ initial mode of venture ideation. We then compare how firms established with knowledge-brokering-based ideation differ in their methods of sustaining ongoing knowledge-brokering capacity compared with firms not established in such a manner. We do so by tracking all the start-up biotechnology firms founded to commercialize the then-emergent recombinant DNA technology (the sample of initial knowledge brokers) together with a contemporaneously founded sample of biotechnology firms that did not license the DNA technology (the sample of initial nonbrokers). Our results suggest that (a) ongoing knowledge brokering has an inverted U-shaped relationship with innovative performance in general; (b) initial knowledge brokers have a positive imprinting effect on thei.
Enhancing the Performance of Data Aggregation in Sensor Networks

Data aggregation is used for data-reduction in sensor networks and can dramatically reduce the amount of network communication, reduce their consumed energy and increase their throughput. But continuously computing data aggregates (such as MAX, MIN, AVG, SUM and COUNT) increases the likelihood of failures. These failures can be due to communication or node failures. Ignorance about failures can endanger the reliability of data aggregation computation. Existing approaches for detection of such failures, such as TAG and synopsis diffusion, have improved the reliability of the accuracy of computed aggregates, but suffer from weak performance on robustness of failures. These approaches have only used one member, i.e. parent node along a tree of nodes, for detecting failures. In contrast, in this paper we use the parent nodes as well for detecting failures. We propose an explicit approach that nearly eliminates the computational errors of aggregates that could arise due to communication or node faults. Evaluation results show that our proposed approach can improve the robustness of failures and the confidence on the correctness of computed aggregates
Endpoint prediction using motion kinematics

Recently proposed novel interaction techniques such as cursor jumping [1] and target expansion for tiled arrangements [13] are predicated on an ability to effectively estimate the endpoint of an input gesture prior to its completion. However, current endpoint estimation techniques lack the precision to make these interaction techniques possible. To address a recognized lack of effective endpoint prediction mechanisms, we propose a new technique for endpoint prediction that applies established laws of motion kinematics in a novel way to the identification of motion endpoint. The technique derives a model of speed over distance that permits extrapolation. We verify our model experimentally using stylus targeting tasks, and demonstrate that our endpoint prediction is almost twice as accurate as the previously tested technique [13] at points more than twice as distant from motion endpoint.
PET imaging using radiolabelled antibodies: future direction in tumor diagnosis and correlate applications

Immuno-positron emission tomography (PET) is a new imaging tool that finds application in diagnostic as well as in prognostic and therapeutic oncology. Monoclonal antibodies (mAbs) present the unique ability to selectively bind specific antigens produced by tumors, establishing the presence, location, and number of neoplastic foci. They are produced in unlimited quantities with absolute specificity using the hybridoma method. Treatment with mAbs can have different forms: they can be administered alone or can be combined with a radioactive molecule, which then delivers the radiation directly to the tumor cells. In immuno-PET, mAbs are labeled with various radionuclides and used in cancer patients who produce tumor-associated antigens. In this review we will discuss immuno-PET applications in tumor diseases, focusing on the technique, mAbs, positron emitter selection, and on its future applications.
Boundary-induced heterogeneous absorbing states

We study two different types of systems with many absorbing states (with and without a conservation law) and scrutinize the effect of walls/boundaries (either absorbing or reflecting) into them. In some cases, non-trivial structured absorbing configurations (characterized by a background field) develop around the wall. We study such structures using a mean-field approach as well as computer simulations. The main results are: i) for systems in the directed percolation class, a very fast (exponential) convergence of the background to its bulk value is observed; ii) for systems with a conservation law, power-law decaying landscapes are induced by both types of walls: while for absorbing walls this effect is already present in the mean-field approximation, for reflecting walls the structured background is a noise-induced effect. The landscapes are shown to converge to their asymptotic bulk values with an exponent equal to the inverse of the bulk correlation length exponent. Finally, the implications of these results in the context of self-organizing systems are discussed.
Charm and bottom production at particle colliders

High energy particle collisions involve complex physics, beyond analytical control. Instead, Monte Carlo event generators like Pythia have been used to simulate particle collisions since decades. A study of the production of heavy quarks (namely, charm and bottom) is presented for two different scenarios: lepton (e+e−) and hadron (pp) collisions. Electron- positron collisions at the Z0 resonance are simulated and the result used to study the secondary bb production rate (gbb) and the D∗± meson energy spectrum. Results are com- pared with experimental data. Three proposed modifications to the Pythia algorithm regarding the g → QQ branching are also tested. For the gbb, the default and one of the alternatives fit the experimental measurement. The study is inconclusive for the D∗± spectrum. Proton-proton collisions at typical LHC energies are also simulated; the correlations between produced B meson pairs are studied through the azimuthal angular separation, the relative rapidities and the R distance, according to the production mechanisms. The default and the alternative options are also compared in the hadronic case.
A Dynamic Game under Ambiguity: Contracting for Delegated Experimentation

Conventional (single-prior) Bayesian games of incomplete information are limited in their ability to capture the extent of informational asymmetry. In particular, they are not capable of representing complete ignorance of an uninformed player about an unknown parameter of the environment. Using a framework of contracting for delegated experimentation, we formulate and analyze a dynamic game of incomplete information that incorporates a multiple-prior belief system. Specifically, we consider a game with a principal contracting with an expert agent for his (observable) effort on a novel experiment – a Poisson process with unknown hazard rate. Although the expert agent has suffi cient knowledge to form a single prior over the hazard rate, the principal initially has complete ignorance and her ambiguous beliefs are represented by the set of all plausible prior distributions over the hazard rate. We propose a new equilibrium concept – Perfect Objectivist Equilibrium – in which the principal, who has ambiguity aversion, draws inference about the agent’s prior from the observed history of the game via maximum likelihood updating. The new equilibrium concept thus also embodies a novel model of learning under ambiguity in the context of a dynamic game. Although the game is rich in its contractual space and strategic interactions, the unique (Markov) equilibrium outcome is a remarkably simple pooling contract with appealing economic properties. In addition, the underlying Markov Perfect Objectivist Equilibria are all belief-free. These are in sharp contrast with the set of Markov Perfect Bayesian Equilibria, which not only hinge on subjective pretence of knowledge, but also predict multiple continuum of equilibrium outcomes. “Acknowledging what is known as known, what is not known as unknown, that is knowledge. ” —Analects of Confucius “What has now appeared is that the mathematical concept of probability is inadequate to express our mental confidence or diffi dence in making such inferences, and that the mathematical quantity which appears to be appropriate for measuring our order of preference among different possible populations does not in fact obey the laws of probability. To distinguish it from probability, I have used the term ‘likelihood’to designate this quantity. ” —Sir R. A. Fisher (1925)
Performance comparison of AODV and OLSR in VANETs urban environments under realistic mobility patterns

A Vehicular Ad Hoc Network (VANET) is an instance of MANETs that establishes wireless connections between cars. In VANETs, routing protocols and other techniques must be adapted to vehicular-specific capabilities and requirements. As many previous works have shown, routing performance is greatly dependent to the availability and stability of wireless links, which makes it a crucial parameter that should not be neglected in order to obtain accurate performance measurements in VANETs. Although routing protocols have already been analyzed and compared in the past, simulations and comparisons have almost always been done considering random motions. But could we assess that those results hold if performed using realistic urban vehicular motion patterns ? In this paper, we evaluate AODV and OLSR performance in realistic urban scenarios. We study those protocols under varying metrics such as node mobility and vehicle density, and with varying traffic rates. We show that clustering effects created by cars aggregating at intersections have remarkable impacts on evaluation and performance metrics. Our objective is to provide a qualitative assessment of the applicability of the protocols in different vehicular scenarios.
Discovery of the potential role of sensors in a Personal Emergency Response System: What can we learn from a single workshop?

Capturing knowledge from domain experts is important to effectively integrate novel technological support in existing care processes. In this paper, we present our experiences in using a specific type of workshop, which we identified as a decision-tree workshop, to determine the process and information exchange during the usage of a Personal Emergency Response System (PERS). We conducted the workshop with current and possible future users of a PERS system to investigate the potential of context- and social awareness for such a system. We discuss the workshop format as well as the results and reflection on this workshop.
Accuracy Enhancement of Fingerprint Indoor Positioning System

There are several researches have been conducted and different methods have been applied to extend the features of indoor location tracking using wireless devices. Fingerprint algorithm is one of the popular methods that have been used to track mobile nodes in indoor structure. This paper focuses on the enrichment of positioning accuracy of the Fingerprint algorithm. RSSI mechanism has been used on the Fingerprint Algorithm to encompass better performance for the accurate positioning. The paper has been proposed two different types of Fingerprint methods and their performance variants on different circumstances of indoor environment. The proposed Fingerprint Database mapping has been divided according to the RSSI data redundancy. The proposed Fingerprint methods has been successfully implemented and experimented on UTMMIMOS CoE lab. The experimented results of two Fingerprint methods has been analyzed and compared as well.
Strong Feature Sets from Small Samples

For small samples, classifier design algorithms typically suffer from overfitting. Given a set of features, a classifier must be designed and its error estimated. For small samples, an error estimator may be unbiased but, owing to a large variance, often give very optimistic estimates. This paper proposes mitigating the small-sample problem by designing classifiers from a probability distribution resulting from spreading the mass of the sample points to make classification more difficult, while maintaining sample geometry. The algorithm is parameterized by the variance of the spreading distribution. By increasing the spread, the algorithm finds gene sets whose classification accuracy remains strong relative to greater spreading of the sample. The error gives a measure of the strength of the feature set as a function of the spread. The algorithm yields feature sets that can distinguish the two classes, not only for the sample data, but for distributions spread beyond the sample data. For linear classifiers, the topic of the present paper, the classifiers are derived analytically from the model, thereby providing an enormous savings in computation time. The algorithm is applied to cancer classification via cDNA microarrays. In particular, the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the algorithm is used to find gene sets whose expressions can be used to classify BRCA1 and BRCA2 tumors.
A fast projection technique and its applications to visualization of large data sets.

Large multivariate data sets still challenge visualization techniques and their interactive capabilities. In this paper we present a new faster formulation of a high precision projection technique that allows much larger data sets to be handled and interacted with. By using this novel approach, named Piecewise Least Square Projection (P-LSP), we provide new and effective ways of exploring the data set through its feature space. The paper shows the use of P-LSP to help data selection by the user during exploration using coherence amongst data items. Examples are shown for images and for volumes as large as 1,000,000 voxels, with as many as 14 variables per voxel, although the technique can also be used for any data set for which a reasonable feature space can be determined.
Estimating the Dependability's in Constructions

In this paper an approach for the throughput evaluation of the construction manufacturing systems is presented. The throughput is evaluated with a heap-based algorithm for the Petri nets model of the construction systems. The Petri nets model is a stochastic one, and the firing rate of the transitions are calculated with Markov chains models of the component subsystems of the manufacturing system. The advantages of this approach are: constructing a system level Markov chain (a complex task) is not required; it permits to evaluate transient and steady-state performance of alternative designs based on different availability of the system’s components; the heap based throughput algorithm is simpler than the traditional timed event graph version; it introduces the availability of the human factor in the theoretical model of a construction system.
A 30fps stereo matching processor based on belief propagation with disparity-parallel PE array architecture

In this paper, we propose a real-time stereo matching processor based on the belief propagation algorithm. Computationally complex message construction is accelerated by a disparity-parallel PE array architecture, which calculates messages for all disparity levels (1–32) in parallel. A tile-based belief propagation approach reduces the on-chip memory requirements by 95.4% compared to the previous works. In addition, a two-level on-chip buffer and memory access pipelining enable high PE utilization of 89%. As a result, the message construction rate of the PEs is increased by 6.45x compared to previous works. The fabricated processor in a 0.18um CMOS process achieves 30 fps performance for QVGA (320×240) video inputs at 200 MHz operating frequency.
Prefiltered single scattering

Volumetric light scattering is a complex phenomenon that is difficult to simulate in real time as light can be scattered towards the camera from everywhere in space. By assuming a single-scattering model, we can transform the usually-employed ray-marching into an efficient ray-independent texture filtering process. Our algorithm builds upon a rectified shadow map as input and we propose an efficient rectification scheme, which could be used by other approaches as well. The resulting scattering method is very fast and almost independent of the screen resolution, but it still produces near-reference results. These properties make it a good candidate for performance-critical applications, such as games.
Probabilistic chunk scheduling approach in parallel multiple-server DASH

Recently parallel Dynamic Adaptive Streaming over HTTP (DASH) has emerged as a promising way to supply higher bandwidth, connection diversity and reliability. However, it is still a big challenge to download chunks sequentially in parallel DASH due to heterogeneous and time-varying bandwidth of multiple servers. In this paper, we propose a novel probabilistic chunk scheduling approach considering time-varying bandwidth. Video chunks are scheduled to the servers which consume the least time while with the highest probability to complete downloading before the deadline. The proposed approach is formulated as a constrained optimization problem with the objective to minimize the total downloading time. Using the probabilistic model of time-varying bandwidth, we first estimate the probability of successful downloading chunks before the playback deadline. Then we estimate the download time of chunks. A near-optimal solution algorithm is designed which schedules chunks to the servers with minimal downloading time while the completion probability is under the constraint. Compared with the existing schemes, the experimental results demonstrate that our proposed scheme greatly increases the number of chunks that are received orderly.