Miracle-dz/UniFAR-FTUs huggingface dataset:
Antioxidant Activity of Oat Proteins Derived Peptides in Stressed Hepatic HepG2 Cells

The purpose of this study was to determine, for the first time, antioxidant activities of seven peptides (P1–P7) derived from hydrolysis of oat proteins in a cellular model. In the oxygen radical absorbance capacity (ORAC) assay, it was found that P2 had the highest radical scavenging activity (0.67 ± 0.02 µM Trolox equivalent (TE)/µM peptide) followed by P5, P3, P6, P4, P1, and P7 whose activities were between 0.14–0.61 µM TE/µM). In the hepatic HepG2 cells, none of the peptides was cytotoxic at 20–300 µM. In addition to having the highest ORAC value, P2 was also the most protective (29% increase in cell viability) against 2,2′-azobis(2-methylpropionamidine) dihydrochloride -induced oxidative stress. P1, P6, and P7 protected at a lesser extent, with an 8%–21% increase viability of cells. The protection of cells was attributed to several factors including reduced production of intracellular reactive oxygen species, increased cellular glutathione, and increased activities of three main endogenous antioxidant enzymes.
A new classification and a two-parameter unification of BL Lacertae objects

A new continuous classification system for BL Lacertae (BL Lac) objects is proposed. The peak frequency of the synchrotron component in the $\nu L_{\nu}$ spectrum is used as a classifying parameter. The application of the accelerating inner jet model to complete BL Lac samples suggests that the range of the observed properties may be explained by adopting a description based on two parameters: the jet angle to the line of sight, and the relativistic electron kinetic luminosity.
Modeling the sensory roles of noradrenaline in action selection

Noradrenaline participates in the neuromodulation of brain activity to modify the trade-off between exploration and exploitation when sensory contingencies have changed. Accordingly, attentional models of noradrenaline acting on sensory representations have been proposed. In this paper, we explore another possible action of this neuromodulator in the decision making process and report simulation results that illustrate that its role is concerned with different aspects of sensory processing. This is made possible by the extension of a classical model of action selection, to render it able to detect and to adapt to sudden changes in sensory contingencies, which is a major characteristic of autonomous learning.
Developmental Origins of Health and Disease Effect of prenatal programming and postnatal rearing on glomerular filtration rate in adult rats

Lozano G, Elmaghrabi A, Salley J, Siddique K, Gattineni J, Baum M. Effect of prenatal programming and postnatal rearing on glomerular filtration rate in adult rats. Am J Physiol Renal Physiol 308: F411–F419, 2015. First published December 23, 2014; doi:10.1152/ajprenal.00593. 2014. —The present study examined whether a prenatal low-protein diet programs a decrease in glomerular filtration rate (GFR) and an increase in systolic blood pressure (BP). In addition, we examined whether altering the postnatal nutritional environment of nursing neonatal rats affected GFR and BP when rats were studied as adults. Pregnant rats were fed a normal (20%) protein diet or a low-protein diet (6%) during the last half of pregnancy until birth, when rats were fed a 20% protein diet. Mature adult rats from the prenatal low-protein group had systolic hypertension and a GFR of 0.38 0.03 versus 0.57 0.05 ml·min ·100 g body wt 1 in the 20% group (P 0.01). In cross-fostering experiments, mothers continued on the same prenatal diet until weaning. Prenatal 6% protein rats cross-fostered to a 20% mother on day 1 of life had a GFR of 0.53 0.05 ml·min ·100 g body wt , which was not different than the 20% group cross-fostered to a different 20% mother (0.45 0.04 ml·min ·100 g body wt ). BP in the 6% to 20% group was comparable with the 20% to 20% group. Offspring of rats fed either 20% or 6% protein diets during pregnancy and cross-fostered to a 6% mother had elevated BP but a comparable GFR normalized to body weight as the 20% to 20% control group. Thus, a prenatal low-protein diet causes hypertension and a reduction in GFR in mature adult offspring, which can be modified by postnatal rearing.
Streaming algorithms for embedding and computing edit distance in the low distance regime

The Hamming and the edit metrics are two common notions of measuring distances between pairs of strings x,y lying in the Boolean hypercube. The edit distance between x and y is defined as the minimum number of character insertion, deletion, and bit flips needed for converting x into y. Whereas, the Hamming distance between x and y is the number of bit flips needed for converting x to y. In this paper we study a randomized injective embedding of the edit distance into the Hamming distance with a small distortion. We show a randomized embedding with quadratic distortion. Namely, for any x,y satisfying that their edit distance equals k, the Hamming distance between the embedding of x and y is O(k2) with high probability. This improves over the distortion ratio of O( n * n) obtained by Jowhari (2012) for small values of k. Moreover, the embedding output size is linear in the input size and the embedding can be computed using a single pass over the input. We provide several applications for this embedding. Among our results we provide a one-pass (streaming) algorithm for edit distance running in space O(s) and computing edit distance exactly up-to distance s1/6. This algorithm is based on kernelization for edit distance that is of independent interest.
Fabrication of high frequency (25-75 MHz) single element ultrasonic transducers

The design and fabrication of high frequency single element ultrasonic transducers present a multitude of challenges for the transducer engineer, from size constraints to electrical impedance matching. This paper discusses the trade-offs involved in procedures used to fabricate lithium niobate (LiNbO/sub 3/) and lead titanate (PbTiO/sub 3/) transducers in the 25 MHz to 75 MHz range. Transducers of varying dimensions were built according to an f-number range of 2-3.5. Desired focal depths were achieved with use of either an acoustic lens or a spherically focused piezoceramic. Silver epoxy backing with an acoustic impedance of approximately 5.9 MRayls was used in all designs. All transducers were designed around a 50/spl Omega/ send and receive circuit. Electrical tuning of the transducer to the receive circuitry was achieved by using an RF transformer and/or a length of coaxial cable. All transducers were tested in a pulse-echo arrangement using a Panametrics 5900PR pulser, a Wavetek function generator and a LeCroy digital oscilloscope. The bandwidth, insertion loss, and depth of focus were measured. Numerous transducers were fabricated with -6 dB bandwidths ranging from 40% to 74%, and two-way insertion loss values ranging from -14 dB to -28 dB.
Chi-squared Distance and Metamorphic Virus Detection Chi-squared Distance and Metamorphic Virus Detection Chi-squared Distance and Metamorphic Virus Detection

CHI-SQUARED DISTANCE AND METAMORPHIC VIRUS DETECTION by Annie H. Toderici Malware are programs that are designed with a malicious intent. Metamorphic malware change their internal structure each generation while still maintaining their original behavior. As metamorphic malware become more sophisticated, it is important to develop efficient and accurate detection techniques. Current commercial antivirus software generally try to scan for malware signatures within files and match them against a known set of signatures; therefore, they are not able to detect metamorphic malware that change their body from generation to generation, with each copy comprised of its own unique signature. Machine learning methods such as hidden Markov models (HMM) have shown promising results in detecting metamorphic malware. However, it is possible to exploit a weakness in HMMs and avoid detection by morphing and merging the malware with contents from normal files. As an alternative approach, we consider combining HMMs with the statistical framework of the chi-squared test to build a new detection method. This paper will present the experimental results of our proposed hybrid detector in metamorphic malware detection.
Knowledge-driven Information-Mining in Remote Sensing Image Archives

attributes Involving a significant amount of high-level reasoning about the meaning and purpose of the objects or scenes depicted (e.g. illegal plantation). They are outside the scope of the current research activity. Figure 2. Logical representation of data and derived attributes extraction of primitive features and the reduction of the resulting data into primitive feature clusters. The primitive features to be extracted need to be carefully selected, since they mainly determine the quality and capabilities of the resulting system. SAR and optical images, for example, will have to be handled differently and texture primitive features will have to be extracted at various resolution levels, since different textures can dominate at different scales. The steps necessary to properly extract primitive features in the EO context are shown in Table 2. The steps need to be repeated iteratively for each band of the image. Primitive feature extraction generates a huge amount of data, which cannot be handled in practice and therefore has to be compressed somehow. This process is represented in the left part of Figure 3, which depicts the result of the scanning of two images (or of two bands of the same image). Each pixel of the image will be located in n-dimensional space in the position determined by the values of the contributing primitive features (their units are non-commensurable, e.g. texture and spectral features). The pixels will tend to group themselves into specific regions of this space. Through clustering (right part of Fig. 3), the ‘clouds’ of image primitive features are replaced by parametric models of their groups, which can be represented in more compact forms. This reduces the precision of the system, similar to a quantisation process, but permits its practical use thanks to the huge data reduction obtained. The primitive features are compressed into clusters using the Kmeans approach. The clusters (condensed representation of primitive features) have no direct meaning, since they group points in an n-dimensional space of non-commensurable variables. Still they represent characteristics of the image seen as a multi-dimensional signal. It is possible to associate meaning with these clusters through training. A user can tell the system that a specific, weighted combination of some clusters represents a derived feature of the image. By making this association, it is possible to select all images in the database that have that specific combination and may therefore contain the feature that the user is searching for. This step is discussed below in more detail. Second step: information mining The second step in KIM is aimed at assigning physical meaning to the primitive features, i.e. remote-sensing image archives 29 Table 2. Steps for primitive feature extraction in the EO context
FLUENT PRIMARY PROGRESSIVE APHASIA : A PURE LANGUAGE DISORDER DISTINCT FROM SEMANTIC DEMENTIA ?

Fluent primary progressive aphasia (fPPA) is very often confused with semantic dementia (SD), due to the ambiguity of the latter term. We present a case of fPPA with neuropsychological and neuroimaing data, not fulfilling the diagnostic criteria for SD. The language disturbance profile is presented, resembling transcortical sensory aphasia. The results of both standardized and experimental testing show speech comprehension disturbance with preserved fluency of speech as a core deficit. The specific character of fPPA is revealed by phonological and syntactic deficits, a modality effect in both oral and written naming, and preserved single-word comprehension in contrast to disturbed comprehension of complex sentences.
A New Incremental Optimization Algorithm for ML-Based Source Localization in Sensor Networks

A new incremental optimization algorithm called normalized incremental subgradient (NIS) algorithm is proposed in this letter, which can be used for distributed maximum likelihood estimation (MLE). Its convergence with a diminishing stepsize has been proved and analyzed theoretically. We then apply the NIS algorithm to the energy-based sensor network source localization problem where the decay factor of the energy decay model is unknown. Simulation results show it can achieve very high estimation performance, which is only somewhat lower than that of the centralized localization method based on global optimization techniques, but with hundreds of times lower computational complexity than the centralized method.
Losing Sight of the Forest for the Trees? Productive Capabilities and Gains from Trade as Drivers of Vertical Scope

Focusing on proving or disproving Transaction Cost Economics has led to a relative neglect of some key drivers of vertical scope, such as differences in productive capabilities (as opposed to capabilities of governance). We consider how productive capability differences can shape vertical scope through gains from trade. Using highly detailed data from the mortgage banking industry, we find productive capabilities to be a key determinant of the make-vs.-buy decision. Our analysis also suggests firms' attempts to leverage a comparative advantage can also lead to the use of mixed governance modes (both "make" and "buy" in a particular part of the value chain). We conclude that the distribution of productive capabilities along the value chain, catalyzed by transaction costs, ultimately drives vertical scope.
Anytime Concurrent Clustering of Multiple Streams with an Indexing Tree

With the advancement of data generation technologies such as sensor networks, multiple data streams are continuously generated. Clustering multiple data streams is challenging as the requirement of clustering at anytime becomes more critical. We aim to cluster multiple data streams concurrently and in this paper we report our work in progress. ClusTree is an anytime clustering algorithm for a single stream. It uses a hierarchical tree structure to index micro-clusters, which are summary statistics for streaming data objects. We design a dynamic, concurrent indexing tree structure that extends the ClusTree structure to achieve more granular micro clusters (summaries) of multiple streams at any time. We devised algorithms to search, expand and update the hierarchical tree structure of storing micro clusters concurrently, along with an algorithm for anytime concurrent clustering of multiple streams. As this is work in progress, we plan to test our proposed algorithms, on sensor data sets, and evaluate the space and time complexity of creating and accessing micro-clusters. We will also evaluate the quality of clustering in terms of number of created clusters and compare our technique with other approaches.
Notice of RetractionActive Internetware's Refining Design and Service Sequence's Combination and Evolution Based on Architecture-Centric Platform

Integrating active internetware's position and viewing architecture-centric platform, this paper analyzes aggregation evolution's drawback about state-space explosion of architecture's granularity, and completes corresponding expansion's evolution of granularities. Then, using refinement method and counterexample validation theorem, Architecture-centric Kripke service evolution structure is built and self-adaptive internetware's abstraction is processed, and designs autonomous criterion's strategy and sequence process, and finishes the formal effective strategy's verification, optimal topology's building of corresponding granularities about sequence services' process and active thought's logic, Furthermore, it is to enhance the overall degree of architecture-centric business flow's adaptation.
Optomechanical creation of magnetic fields for photons on a lattice

Recently, there has been growing interest in the creation of artificial magnetic fields for uncharged particles, such as cold atoms or photons. These efforts are partly motivated by the resulting desirable features, such as transport along edge states that is robust against backscattering. We analyze how the optomechanical interaction between photons and mechanical vibrations can be used to create artificial magnetic fields for photons on a lattice. The ingredients required are an optomechanical crystal, i.e., a free-standing photonic crystal with localized vibrational and optical modes, and two laser beams with the right pattern of phases. One of the two schemes analyzed here is based on optomechanical modulation of the links between optical modes, while the other is a lattice extension of optomechanical wavelength-conversion setups. We analyze both schemes theoretically and present numerical simulations of the resulting optical spectrum, photon transport in the presence of an artificial Lorentz force, edge states, and the photonic Aharonov–Bohm effect. We discuss the requirements for experimental realizations. Finally, we analyze the completely general situation of an optomechanical system subject to an arbitrary optical phase pattern and conclude that it is best described in terms of gauge fields acting in synthetic dimensions. In contrast to existing nonoptomechanical approaches, the schemes analyzed here are very versatile, since they can be controlled fully optically, allowing for time-dependent in situ tunability without the need for individual electrical addressing of localized optical modes.
Why nothing matters: the impact of zeroing

Memory safety defends against inadvertent and malicious misuse of memory that may compromise program correctness and security. A critical element of memory safety is zero initialization. The direct cost of zero initialization is surprisingly high: up to 12.7%, with average costs ranging from 2.7 to 4.5% on a high performance virtual machine on IA32 architectures. Zero initialization also incurs indirect costs due to its memory bandwidth demands and cache displacement effects. Existing virtual machines either: a) minimize direct costs by zeroing in large blocks, or b) minimize indirect costs by zeroing in the allocation sequence, which reduces cache displacement and bandwidth. This paper evaluates the two widely used zero initialization designs, showing that they make different tradeoffs to achieve very similar performance. Our analysis inspires three better designs: (1) bulk zeroing with cache-bypassing (non-temporal) instructions to reduce the direct and indirect zeroing costs simultaneously, (2) concurrent non-temporal bulk zeroing that exploits parallel hardware to move work off the application's critical path, and (3) adaptive zeroing, which dynamically chooses between (1) and (2) based on available hardware parallelism. The new software strategies offer speedups sometimes greater than the direct overhead, improving total performance by 3% on average. Our findings invite additional optimizations and microarchitectural support.
Race, ethnicity, socioeconomic position, and quality of care for adults with diabetes enrolled in managed care: the Translating Research Into Action for Diabetes (TRIAD) study.

OBJECTIVE To examine racial/ethnic and socioeconomic variation in diabetes care in managed-care settings. RESEARCH DESIGN AND METHODS We studied 7,456 adults enrolled in health plans participating in the Translating Research Into Action for Diabetes study, a six-center cohort study of diabetes in managed care. Cross-sectional analyses using hierarchical regression models assessed processes of care (HbA(1c) [A1C], lipid, and proteinuria assessment; foot and dilated eye examinations; use or advice to use aspirin; and influenza vaccination) and intermediate health outcomes (A1C, LDL, and blood pressure control). RESULTS Most quality indicators and intermediate outcomes were comparable across race/ethnicity and socioeconomic position (SEP). Latinos and Asians/Pacific Islanders had similar or better processes and intermediate outcomes than whites with the exception of slightly higher A1C levels. Compared with whites, African Americans had lower rates of A1C and LDL measurement and influenza vaccination, higher rates of foot and dilated eye examinations, and the poorest blood pressure and lipid control. The main SEP difference was lower rates of dilated eye examinations among poorer and less educated individuals. In almost all instances, racial/ethnic minorities or low SEP participants with poor glycemic, blood pressure, and lipid control received similar or more appropriate intensification of therapy relative to whites or those with higher SEP. CONCLUSIONS In these managed-care settings, minority race/ethnicity was not consistently associated with worse processes or outcomes, and not all differences favored whites. The only notable SEP disparity was in rates of dilated eye examinations. Social disparities in health may be reduced in managed-care settings.
Baseline regularized sparse spatial filters

The common spatial pattern (CSP) method has large number of applications in brain machine interfaces (BMI) to extract features from the multichannel neural activity through a set of linear spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that renders unconstrained minimization possible via a new objective function with an approximated ℓ1 penalty. We apply our new algorithm with a baseline regularization to the ECoG data involving finger movements to gain stability with respect to the number of sparse channels.
Hiding a Semantic Class Hierarchy in a Markov Model

This paper introduces a new model of selectional preference induction. Unlike previous approaches we provide a stochastic generation model for the words that appear as arguments of a predicate. More speciically, we deene a hidden Markov model with the general shape of our semantic class hierarchy and we use an EM-like algorithm for parameter estimation. This model enables us to handle word sense ambiguity in the input in a principled fashion. In addition, it can be used to estimate a number of distributions such as P r(wordjpredicate), P r(wordjsemantic-class), P r(word-sensejword; predicate). These distributions can be used directly to help solve ambiguity resolution problems such as word sense disambiguation and syntactic structure dis-ambiguation. One can also derive a traditional list of semantic classes that capture the selectional preferences of a predicate for a complement. In addition, the P r(wordjpredicate) distribution can be seen as a very speciic language model, i.e. , a language model for the head of the argument of the predicate. This work is of general interest because it weds two important entities in natural language processing systems: hierarchical class structures and stochastic generation models.