Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of the medial prefrontal cortex (cingulate gyrus) in the regulation of hypothalamic-pituitary-adrenal responses to stress

In the studies reported here we have examined the role of the medial prefrontal cortex (MpFC) in regulating hypothalamic-pituitary-adrenal (HPA) activity under basal and stressful conditions. In preliminary studies we characterized corticosteroid receptor binding in the rat MpFC. The results revealed high-affinity (Kd approximately 1 nM) binding with a moderate capacity (42.9 +/- 3 fmol/mg) for 3H- aldosterone (with a 50-fold excess of cold RU28362; mineralocorticoid receptor) and high-affinity (Kd approximately 0.5–1.0 nM) binding with higher capacity (183.2 +/- 22 fmol/mg) for 3H-RU 28362 (glucocorticoid receptor). Lesions of the MpFC (cingulate gyrus) significantly increased plasma levels of both adrenocorticotropin (ACTH) and corticosterone (CORT) in response to a 20 min restraint stress. The same lesions had no effect on hormone levels following a 2.5 min exposure to ether. Implants of crystalline CORT into the same region of the MpFC produced a significant decrease in plasma levels of both ACTH and CORT with restraint stress, but again, there was no effect with ether stress. Neither MpFC lesions nor CORT implants had any consistent effect on A.M. or P.M. levels of plasma ACTH or CORT. Manipulations of MpFC function were not associated with changes in the clearance rate for CORT or in corticosteroid receptor densities in the pituitary, hypothalamus, hippocampus, or amygdala. Taken together, these findings suggest that MpFC is a target site for the negative-feedback effects of glucocorticoids on stress-induced HPA activity, and that this effect is dependent upon the nature of the stress.
Genetic algorithm for pattern detection in NIALM systems

Nonintrusive appliance load monitoring systems (NIALM) require sufficient accurate total load data to separate the load into its major appliances. The most available solutions separate the whole electric energy consumption based on the measurement of all three voltages and currents. Aside from the cost for special measuring devices, the intrusion into the local installation is the main problem for reaching a high market distribution. The use of standard digital electricity meters could avoid this problem with loss of information in the measured data. This paper presents a new NIALM approach to analyse data, collected form a standard digital electricity meter. To disaggregate the consumption of the entire active power into its major electrical end uses, an algorithm consisting of fuzzy clustering methods, a genetic algorithm and a dynamic programming approach is presented.
The expanding world of hybrid perovskites: materials properties and emerging applications

Hybrid inorganic–organic perovskites have emerged over the last 5 years as a promising class of materials for optoelectronic applications. Most notably, their solar cells have achieved power conversion efficiencies above 20% in an unprecedented timeframe; however, many fundamental questions still remain about these materials. This Prospective Article reviews the procedures used to deposit hybrid perovskites and describes the resulting crystallographic and morphological structures. It further details the electrical and optical properties of perovskites and then concludes by highlighting a number of potential applications and the materials challenges that must be overcome before they can be realized.
From Compressed Sensing to Compressed Bit-Streams: Practical Encoders, Tractable Decoders

Compressed sensing is now established as an effective method for dimension reduction when the underlying signals are sparse or compressible with respect to some suitable basis or frame. One important, yet under-addressed problem regarding the compressive acquisition of analog signals is how to perform quantization. This is directly related to the important issues of how “compressed” compressed sensing is (in terms of the total number of bits one ends up using after acquiring the signal) and ultimately whether compressed sensing can be used to obtain compressed representations of suitable signals. In this paper, we propose a concrete and practicable method for performing “analog-to-information conversion”. Following a compressive signal acquisition stage, the proposed method consists of a quantization stage, based on $ \Sigma \Delta $ (sigma-delta) quantization, and a subsequent encoding (compression) stage that fits within the framework of compressed sensing seamlessly. We prove that, using this method, we can convert analog compressive samples to compressed digital bitstreams and decode using tractable algorithms based on convex optimization. We prove that the proposed analog-to-information converter (AIC) provides a nearly optimal encoding of sparse and compressible signals. Finally, we present numerical experiments illustrating the effectiveness of the proposed AIC.
Electronic trading and its implications for financial systems

The adoption of electronic trading systems has transformed the economic landscape of trading venues and is proving a force for change in market architecture and consequential trading possibilities. The term “electronic trading” is used in many ways. In this paper, it refers mainly to trading in wholesale financial markets (as opposed to e-commerce more generally see, for example, Long (2000) for a survey of the latter) and focuses on the central feature of electronic trading systems, automation of trade execution. Such systems usually also feature electronic order routing and dissemination of trade information and may link through to clearing and settlement. Electronic trading both removes geographical restraints and allows continuous multilateral interaction (whereas telephone trading allows only the former and floor trading only the latter). It allows much higher volumes of trades to be handled, and in customised ways that until recently would have been technically impossible or prohibitively expensive. This paper considers areas where these enabling effects have been particularly important in wholesale financial markets and how they raise wider, policy implications.
Emotion recognition in speech using MFCC and wavelet features

Recognition of emotions from speech is one of the most important sub domains in the field of affective computing. Six basic emotional states are considered for classification of emotions from speech in this work. In this work, features are extracted from audio characteristics of emotional speech by Mel-frequency Cepstral Coefficient (MFCC), and Subband based Cepstral Parameter (SBC) method. Further these features are classified using Gaussian Mixture Model (GMM). SAVEE audio database is used in this work for testing of Emotions. In the experimental results, SBC method out performs with 70% in recognition compared to 51% of recognition in MFCC algorithm.
Multiple cues on the physiochemical, mesenchymal, and intracellular trafficking interactions with nanocarriers to maximize tumor target efficiency

Over the past 60 years, numerous medical strategies have been employed to overcome neoplasms. In fact, with the exception of lung, bronchial, and pancreatic cancers, the 5-year survival rate of most cancers currently exceeds 70%. However, the quality of life of patients during chemotherapy remains unsatisfactory despite the increase in survival rate. The side effects of current chemotherapies stem from poor target efficiency at tumor sites due to the uncontrolled biodistribution of anticancer agents (ie, conventional or current approved nanodrugs). This review discusses the effective physiochemical factors for determining biodistribution of nanocarriers and, ultimately, increasing tumor-targeting probability by avoiding the reticuloendothelial system. Second, stem cell-conjugated nanotherapeutics was addressed to maximize the tumor searching ability and to inhibit tumor growth. Lastly, physicochemical material properties of anticancer nanodrugs were discussed for targeting cellular organelles with modulation of drug-release time. A better understanding of suggested topics will increase the tumor-targeting ability of anticancer drugs and, ultimately, promote the quality of life of cancer patients during chemotherapy.
A trace-based scenario database for high-level simulation of multimedia MP-SoCs

High-level simulation and design space exploration nowadays are key ingredients for system-level design of modern multimedia embedded systems. The majority of the work in this area evaluates systems under a single, fixed application workload. In reality, however, the application workload in such systems (i.e., the applications that are concurrently executing and contending for system resources), and therefore the intensity and nature of the application demands, can change dramatically over time. To facilitate the simulation and exploration of different workload scenarios, this paper presents the concept of a so-called scenario database, which has been integrated in our Sesame systemlevel simulation framework. This scenario database compactly stores application scenarios and allows for generating application workloads — in the form of event traces — belonging to the stored scenarios for the purpose of scenario-aware simulation in Sesame.
Strategic Allies and the Survival of Critical Media under Repressive Conditions: An Empirical Analysis of Local Mexican Press

Why do some newspapers remain critical in contexts of antipress repression while others do not? It has been argued that aggressions against journalists generate a deterrence effect on the watchdog role of the press. However, evidence of the press remaining critical in hostile environments appears to defy this claim. In this paper, I explore the relationship between violent government repression and the critical function of the press. Using an original data set of local Mexican newspapers from 2011 to 2013, I examine the direct effects of violence on front-page headlines. I find that while repression does deter critical coverage, the ultimate effect is contingent upon the configuration of strategic press allies in the locality, reviewed here as nongovernmental organizations, opposition parties, and newspaper publishers.
PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments

Most approaches to visual odometry estimates the camera motion based on point features, consequently, their performance deteriorates in low-textured scenes where it is difficult to find a reliable set of them. This paper extends a popular semi-direct approach to monocular visual odometry known as SVO [1] to work with line segments, hence obtaining a more robust system capable of dealing with both textured and structured environments. The proposed odometry system allows for the fast tracking of line segments since it eliminates the necessity of continuously extracting and matching features between subsequent frames. The method, of course, has a higher computational burden than the original SVO, but it still runs with frequencies of 60Hz on a personal computer while performing robustly in a wider variety of scenarios.
Chlorine Inactivation of Highly Pathogenic Avian Influenza Virus (H5N1)

To determine resistance of highly pathogenic avian influenza (H5N1) virus to chlorination, we exposed allantoic fluid containing 2 virus strains to chlorinated buffer at pH 7 and 8, at 5°C. Free chlorine concentrations typically used in drinking water treatment are sufficient to inactivate the virus by >3 orders of magnitude.
CALIBRATING COLUMN DENSITY TRACERS WITH GAMMA-RAY OBSERVATIONS OF THE ρ OPHIUCHI MOLECULAR CLOUD

Diffuse gamma-ray emission from interstellar clouds results largely from cosmic ray (CR) proton collisions with ambient gas, regardless of the gas state, temperature, or dust properties of the cloud. The interstellar medium is predominantly transparent to both CRs and gamma-rays, so GeV emission is a unique probe of the total gas column density. The gamma-ray emissivity of a cloud of known column density is then a measure of the impinging CR population and may be used to map the k-scale CR distribution in the Galaxy. To this end, we test a number of commonly used column density tracers to evaluate their effectiveness in modeling the GeV emission from the relatively quiescent, nearby ρ Ophiuchi molecular cloud. We confirm that both H i and an appropriate tracer are required to reproduce the total gas column densities probed by diffuse gamma-ray emisison. We find that the optical depth at 353 GHz ( ) from Planck best reproduces the gamma-ray data overall, based on the test statistic across the entire region of interest, but near-infrared stellar extinction also performs very well, with smaller spatial residuals in the densest parts of the cloud.
Central or distributed energy storage for processors with energy harvesting

We consider an interconnected distributed computer system with multiple computation centres (CC) that operate with energy harvesting to improve sustainability. The intermittent energy harvesting is matched with steady demand from the CCs using energy storage (ES), e.g. batteries. Based on energy leakage from batteries, and power losses over transmission lines, we examine whether a centralised or distributed ES system provides the solution that offers the smallest response time to a fixed workload of computer jobs using the Energy Packet Network (EPN) modelling paradigm.
A Study in Employing Rough Set Based Approach for Clustering on Categorical Time-Evolving Data

The proportionate increase in the size of the data with increase in space implies that clustering a very large data set becomes difficult and is a time consuming process. Sampling is one important technique to scale down the size of dataset and to improve the efficiency of clustering. After sampling, allocating unlabeled data point into proper cluster is difficult in the categorical domain and in real situations data changes over time. However, clustering this type of data not only decreases the quality of clusters and also disregards the expectation of users, who usually require recent clustering results. In both the cases mentioned above, one is of allocating unlabeled data point into proper clusters after the sampling and the other is of finding clustering results when data changes over time which is difficult in the categorical domain. In this paper, using node importance technique, a rough set based method proposed to label unlabeled data point and to find the next clustering result based on the previous clustering result.
Low Dose Focused Ultrasound Induces Enhanced Tumor Accumulation of Natural Killer Cells

Natural killer (NK) cells play a vital antitumor role as part of the innate immune system. Efficacy of adoptive transfer of NK cells depends on their ability to recognize and target tumors. We investigated whether low dose focused ultrasound with microbubbles (ldbFUS) could facilitate the targeting and accumulation of NK cells in a mouse xenograft of human colorectal adenocarcinoma (carcinoembryonic antigen (CEA)-expressing LS-174T implanted in NOD.Cg-PrkdcscidIl2rgtm1Wjl/SzJ (NSG) mice) in the presence of an anti-CEA immunocytokine (ICK), hT84.66/M5A-IL-2 (M5A-IL-2). Human NK cells were labeled with an FDA-approved ultra-small superparamagnetic iron oxide particle, ferumoxytol. Simultaneous with the intravenous injection of microbubbles, focused ultrasound was applied to the tumor. In vivo longitudinal magnetic resonance imaging (MRI) identified enhanced accumulation of NK cells in the ensonified tumor, which was validated by endpoint histology. Significant accumulation of NK cells was observed up to 24 hrs at the tumor site when ensonified with 0.50 MPa peak acoustic pressure ldbFUS, whereas tumors treated with at 0.25 MPa showed no detectable NK cell accumulation. These clinically translatable results show that ldbFUS of the tumor mass can potentiate tumor homing of NK cells that can be evaluated non-invasively using MRI.
Glycoproteomic Study Reveals Altered Plasma Proteins Associated with HIV Elite Suppressors

HIV elite suppressors (ES) or controllers are individuals achieving control of viremia by their natural immunological mechanisms without highly active antiretroviral therapy (HAART). Study of the mechanisms responsible for the immunological suppression of viremia in ES may lead to the detection of individuals with ES and the effective control of HIV infection. We hypothesize that plasma glycoproteins play essential roles in the immune system of ES since plasma proteins are critical and highly relevant in anti-viral immunity and most plasma proteins are glycoproteins. To examine glycoproteins associated with ES, plasma samples from ES individuals (n=20), and from individuals on HAART (n=20), with AIDS (n=20), and no HIV infection (n=10) were analyzed by quantitative glycoproteomics. We found that a number of glycoproteins changed between ES versus HAART, AIDS and HIV- individuals. In sharp contrast, the level of plasma glycoproteins in the HAART cohort showed fewer changes compared with AIDS and HIV- individuals. These results showed that although both ES and HAART effectively suppress viremia, ES appeared to profoundly affect immunologically relevant glycoproteins in plasma as consequence of or support for anti-viral immunity. Bioinformatic analysis revealed that altered proteins in ES plasma were mainly associated with inflammation. This analysis suggests that overlapping, while distinguishable, glycoprotein profiles for inflammation and immune activation appeared to be present between ES and non-ES (HAART+AIDS) cohorts, indicating different triggers for inflammation and immune activation between natural and treatment-related viral suppression.
Evolution of a Java Just-in-time Compiler for Ia-32 Platforms

Java௣ has gained widespread popularity in the industry, and an efficient Java virtual machine (JVM௣) and just-in-time (JIT) compiler are crucial in providing high performance for Java applications. This paper describes the design and implementation of our JIT compiler for IA-32 platforms by focusing on the recent advances achieved in the past several years. We first present the dynamic optimization framework, which focuses the expensive optimization efforts only on performance-critical methods, thus helping to manage the total compilation overhead. We then describe the platform-independent features, which include the conversion from the stack-semantic Java bytecode into our register-based intermediate representation (IR) and a variety of aggressive optimizations applied to the IR. We also present some techniques specific to the IA-32 used to improve code quality, especially for the efficient use of the small number of registers on that platform. Using several industry-standard benchmark programs, the experimental results show that our approach offers high performance with low compilation overhead. Most of the techniques presented here are included in the IBM JIT compiler product, integrated into the IBM Development Kit for Microsoft Windows௡, Java Technology Edition Version 1.4. 0.
Ecology and Evolution of Swimming Performance in Fishes : Predicting Evolution with Biomechanics

Residing within the immense diversity of fishes on earth is an equally impressive array of locomotor abilities. Some fish continuously swim virtually their entire lives; some move primarily in brief bursts of rapid acceleration; some gracefully maneuver through spatially complex habitats; and some even walk on land. What are the evolutionary root causes of such diversity in swimming abilities? Has swimming performance largely been shaped by random factors, evolving at the whim of genetic drift? If not, then what ecological mechanisms might be responsible for the evolution of swimming performance? Here we investigate some of the major ecological factors that might have shaped the evolution of locomotor performance in fishes. Using an integrative approach, we 7
Statistical distribution of the convergence time of evolutionary algorithms for long-path problems

The behavior of a (1+1)-ES process on Rudolph's binary long k paths is investigated extensively in the asymptotic framework with respect to string length l. First, the case of k=l/sup /spl alpha// is addressed. For /spl alpha//spl ges/1/2, we prove that the long k path is a long path for the (1+1)-ES in the sense that the process follows the entire path with no shortcuts, resulting in an exponential expected convergence time. For /spl alpha/<1/2, the expected convergence time is also exponential, but some shortcuts occur in the meantime that speed up the process. Next, in the case of constant k, the statistical distribution of convergence time is calculated, and the influence of population size is investigated for different (/spl mu/+/spl lambda/)-ES. The histogram of the first hitting time of the solution shows an anomalous peak close to zero, which corresponds to an exceptional set of events that speed up the expected convergence time with a factor of l/sup 2/. A direct consequence of this exceptional set is that performing independent (1+1)-ES processes proves to be more advantageous than any population-based (/spl mu/+/spl lambda/)-ES.
In situ sensor-to-segment calibration for whole body motion capture

Sensor-to-segment calibration is a critical step for motion reconstruction from inertial and magnetic measurement units (IMMUs). In this paper, a novel sensor-to-segment calibration protocol is proposed. The protocol consists of three stages that allow for in situ calibration. After the sensor units are attached to the body, predefined postures and movements are used for sensor calibration. Acceleration and angular velocity measurements are used to estimate axes of functional frame (FF) by Principal Component Analysis (PCA). Finally, Levenberg-Marquardt optimization is used to identify rotation matrices between the expected FF and their estimations with respect to the sensor frame. Validation of the method demonstrates its practical value and how the proposed protocol reduces the extent of cross-talk for evaluating joint kinematics.
In Vivo Imaging of Proteolytic Activity in Atherosclerosis

Background—Atherosclerotic plaque rupture, the most important cause of acute cardiovascular incidents, has been strongly associated with vascular inflammation. On the basis of the hypothesis that the inflammatory response and proteolysis lead to plaque rupture, we have examined the role of cathepsin B as a model proteolytic enzyme. Methods and Results—Using western-type diet–fed apoE and apoE/endothelial NO synthase double knockout mice as models of atherosclerosis, we show (1) that cathepsin B is upregulated in atherosclerotic lesions characterized by high degrees of inflammation compared with normal aorta or silent lesions,(2) that intravenously injectable novel cathepsin B imaging beacons are highly activated within active atherosclerotic lesions and colocalize with cathepsin B immunoreactivity, and(3) that cathepsin B activity in atherosclerotic lesions can be imaged in whole animals by using a novel near-infrared tomographic imaging system. Conclusions—These studies indicate that cathepsin B, and potentially other proteases, may serve as a biomarker for vulnerable plaques when probed with beacons. The tomographic in vivo imaging method as well as catheter-based optical sensing methods could be readily adapted to screening and potentially to the molecular profiling of a number of proteases in vulnerable plaque in vivo.
Parameter Estimation for Constrained Context-Free Language Models

A new language model incorporating both N-gram and context-free ideas is proposed. This constrained context-free model is specified by a stochastic context-free prior distribution with N-gram frequency constraints. The resulting distribution is a Markov random field. Algorithms for sampling from this distribution and estimating the parameters of the model are presented.