Miracle-dz/UniFAR-FTUs huggingface dataset:
Exosomes in the Diseased Brain: First Insights from In vivo Studies

Extracellular vesicles (EVs) are nanoscale size vesicles secreted by cells and are important mediators of intercellular communication and genetic exchange. Exosomes, EVs generated in endosomal multivesicular bodies, have been the focus of numerous publications as they have emerged as clinically valuable markers of disease states. Exosomes have been mostly studied from conditioned culture media and body fluids, with the difficulty of isolating exosomes from tissues having delayed their study in vivo. The implementation of a method designed to isolate exosomes from tissues, however, has yielded the first insights into characteristics of exosomes in the brain. It has been observed that brain exosomes from murine models of neurodegenerative diseases and human postmortem brains tend to mirror the protein content of the cells of origin, and interestingly, they are enriched with toxic proteins. Whether this enrichment with neurotoxic proteins is beneficial by relieving neurons of accumulated toxic material or detrimental to the brain by propagating pathogenicity throughout the brain remains to be answered. Here is summarized the first group of studies describing exosomes isolated from brain, results that demonstrate that exosomes in vivo reflect complex multicellular pathogenic processes in neurodegenerative disorders and the brain's response to injury and damage.
Mining learning groups' activities in forum-type tools

Mining data produced by students involved in communication through forum-like tools can help revealing aspects of their communication. In this paper we propose an approach to the construction of models to highlight structural properties of learning groups based on a relational perspective (analysis of chains of references) and the use of Social Network Analysis techniques. These models can be useful both for the tutor and the participants. We begin by introducing the overall approach, then we describe how the models are constructed and finally we present preliminary results from the integration of these ideas in a forum-type tool.
Effect of automotive electrical system changes on fuel consumption using incremental efficiency methodology

There has been a continuous increase in automotive electric power usage. Future projections show no sign of it decreasing. Therefore, the automotive industry has a need to either improve the current 12 Volt automotive electrical system or move to a higher voltage vehicle electric system. Both of these choices are likely to increase cost of the system. Performance improvements will be needed to justify the increased cost to the Original Equipment Manufacturer. This thesis is investigating the potential for fuel economy improvements and their associated economic advantages for different vehicle electric systems. The objective is to determine the effects on fuel consumption of electrical system choices under a variety of drive and load cycle circumstances. Incremental, or marginal, efficiencies will be used to determine the relationship between loads and fuel consumptions. ADVISOR, a model developed by the National Renewable Energy Lab, has been adapted for use in this application. This included the implementation of industry standard engine performance map and alternator efficiency map data in the ADVISOR model. Thesis Supervisor: Richard Roth Thesis Supervisor Title: Director, Materials Systems Laboratory Thesis Supervisor: Thomas A. Keim Thesis Supervisor Title: Principal Research Engineer and LEES Associate Director Thesis Supervisor: Gary DesGroseilliers Thesis Supervisor Title: Automotive Consortium Program Manager Thesis Supervisor: Frank R. Field III Thesis Supervisor Title: Senior Research Engineer, Associate Director of the Technology and Policy Program
What Motivates High School Students to Take Precautions against the Spread of Influenza? A Data Science Approach to Latent Modeling of Compliance with Preventative Practice

This study focuses on a central question: What key behavioral factors influence high school students’ compliance with preventative measures against the transmission of influenza? We use multilevel logistic regression to equate logit measures for eight precautions to students’ latent compliance levels on a common scale. Using linear regression, we explore the efficacy of knowledge of influenza, affective perceptions about influenza and its prevention, prior illness, and gender in predicting compliance. Hand washing and respiratory etiquette are the easiest precautions for students, and hand sanitizer use and keeping the hands away from the face are the most difficult. Perceptions of barriers against taking precautions and sense of social responsibility had the greatest influence on compliance.
Test Case Generation Based on Invariant Extraction

Software testing is an effective way to find software errors. Generating a good test suite is the key. A program invariant is a property that is true at a particular program point or points. The property could reflect the program's execution over a test suite. Based on this point, we integrate the random test case generation technique and the invariant extraction technique, achieving automatic test case generation and selection. With the same invariants, compared with the traditional random test case generation technique, the experimental results show that the approach this paper describes can generate a smaller test suite.
A Vision-Based Collision Warning System by Surrounding Vehicles Detection

To provide active notification and enhance drivers’ awareness of their surroundings, a vision-based collision warning system that detects and monitors surrounding vehicles is proposed in this paper. The main objective is to prevent possible vehicle collisions by monitoring the status of surrounding vehicles, including the distance to the other vehicles in front, behind, to the left and to the right sides. In addition, the proposed system collects and integrates this information to provide advisory warnings to drivers. To offer the correct notification, an algorithm based on features of edge and morphology to detect vehicles is also presented. The proposed system has been implemented in embedded systems and evaluated on real roads in various lighting and weather conditions. The experimental results indicate that the vehicle detection ratios were higher than 97% in the daytime, and appropriate for real road applications.
Misinformation and Motivated ReasoningResponses to Economic News in a Politicized Environment

Public opinion scholars have recently focused on understanding why surveys report such high levels of misinformation among otherwise knowledgeable and engaged partisans. In this paper, we take advantage of a natural experiment involving the October 2012 jobs report announcement to gain a more complete understanding of how individuals’ beliefs are influenced by new salient information in a politicized environment. We examine reactions by Republicans and Democrats on a factual question about the unemployment rate immediately before and after the announcement that unemployment had fallen below 8% for the first time during the Obama presidency. Using a variety of techniques, including response latency measures, we conclude that partisans did react to the jobs report by engaging in motivated reasoning, providing a clearer understanding of why individuals respond to factual questions in vastly different ways.
Sampling Visibility in Three-Space

We present a visibility algorithm that is based on sampling the scene for visible surfaces. The subset of lines for which the visibility is pre-calculated is defined by a two-plane parameterization. Each sample represents the portion of geometry that is visible for the lines through that sample’s region. Lumping geometry together in grid cells allows achieving visibility at a coarser level, and improves the solution. A rendering algorithm that uses this visibility pre-calculation is presented. Using this method, most hidden geometry is culled and a speedup in rendering time is obtained. Some problems remain, however, and are discussed here. Particularly, some cells are left unrendered because they are missed by the discrete sampling procedure, thus potentially creating holes in surfaces. Techniques to improve the solution are proposed. In particular, modifying the preand postfiltering algorithms used in creating/resampling the field helps remove some of the artifacts. Nonetheless, the algorithm remains useful for cases where only coarse visibility is needed. CR Categories: I.3. 5 [Computer Graphics]: Computational Geometry and Object Modeling surfaces and object representations.
Multiwavelength appearance of Vela Jr.: Is it up to expectations?

Vela Jr. is one of the youngest and likely nearest among the known galactic supernova remnants (SNRs). Discovered in 1997 it has been studied since then at quite a few wavelengths, that spread over almost 20 decades in energy. Here we present and discuss Vela Jr. properties revealed by these multiwavelength observations, and confront them with the SNR model expectations. Questions that remained unanswered at the time of publication of the paper by Iyudin et al. [25], e.g. what is the nature of the SNR’s proposed central compact source CXOU J085201.4-461753, and why is the ISM absorption column density apparently associated with RX J0852.0-4622 much greater than the typical column of the Vela SNR, can be addressed using latest radio and X-ray observations of Vela Jr. These, and other related questions are addressed in the following.
KK-Masses in Dipole Deformed Field Theories

We reconsider aspects of non-commutative dipole deformations of field theories. Among our findings there are hints to new phases with spontaneous breaking of translation invariance (stripe phases), similar to what happens in Moyal-deformed field theories. Furthermore, using zeta-function regularization, we calculate quantum corrections to KK-state masses. The corrections coming from non-planar diagrams show interesting but non-universal behaviour. Depending on the type of interaction the corrections can make the KK-states very heavy but also very light or even tachyonic. Finally we point out that the dipole deformation of QED is not renormalizable!
X-ray photoionized plasma diagnostics with Helium-like ions. Application to Warm Absorber-Emitter in Active Galactic Nuclei

We present He-like line ratios (resonance, intercombination and forbidden lines) for totally and partially photoionized media. For solar plasmas, these line ratios are already widely used for density and temperature diagnostics of coronal (collisional) plasmas. In the case of totally and partially photoionized plasmas, He-like line ratios allow for the determination of the ionization processes involved in the plasma (photoionization with or without an additional collisional ionization process), as well as the density and the electronic temperature. obtain high spectral resolution and high sensitivity observations. ratios of these three components will be measurable for a large number of non-solar Narrow Line Region) in AGN (Seyferts type-1 and type-2, low- and high-redshift quasars.) will be an important key to obtaining strong constraints on uniﬁed schemes. We have calculated He-like line ratios, for Z =6, 7, 8, 10, 12 and 14, taking into account the upper level radiative cascades which we have computed for radiative and dielectronic recombinations and collisional excitation. The atomic data are tabulated over a wide range of temperatures in order to be used for interpreting a large variety of astrophysical plasmas.
HandsInAir: A Wearable System for Remote Collaboration

We present HandsInAir, a real-time collaborative wearable system for remote collaboration. The system is developed to support real world scenarios in which a remote mobile helper guides a local mobile worker performing a physical task. HandsInAir implements a novel approach to support mobility of remote collaborators. This approach allows the helper to perform gestures without having to touch tangible objects, requiring little environment support. The system consists of two parts: the helper part and the worker part. The two parts are connected via a wireless network and the collaboration partners communicate with each other via audio and visual links. In this paper, we review related work, describe technical implementation of the system and envision future work for further improvements.
Rapid , High-Level Targeting of Xenografts 3 ) Which ExhibitsHAntibody Fragment ( Single-Chain Fv-C Minibody : A Novel Engineered Anti-Carcinoembryonic Antigen

A novel engineered antibody fragment (VL-VH-CH3, or "minibody") with bivalent binding to carcinoembryonic antigen (CEA) was produced by genetic fusion of a T84.66 (anti-CEA) single-chain antibody (scFv) to the human IgGl CH3 domain. Two designs for the connecting peptide were evaluated. In the 184.66/212 LD ininÂ¡body,a two-amino acid linker (generated by fusion of restriction sites) was used to join VH and CH3. In the T84.66/212 Flex minibody, the human IgGl hinge plus an additional 10 residues were used as the connecting peptide. Size exclusion chromatography of purified minibodies demonstrated that both proteins had assembled into A/,. 80,000 dimers as expected. Furthermore, analysis by SDS-PAGE under nonreducing conditions was consistent with disulfide bond formation in the hinge of the TX4.66 Flex minibody. Purified minibodies retained high affinity for CEA (A\. 2 x IO9 M~') and demon strated bivalent binding to antigen. Tumor targeting properties were evaluated in vivo using athymic mice bearing LS174T human colon car cinoma xenografts. IUI-labeled T84.66 minibodies demonstrated rapid, high tumor uptake, reaching 17% injected dose/gram (%ID/g) for the LD minibody and 33%ID/g for the Flex minibody at 6 h following injection. Radioiodinated minibody also cleared rapidly from the circulation, yield ing high tumorblood uptake ratios: 44.5 at 24 h for the LD minibody and 64.9 at 48 h for the Flex minibody. Rapid localization by the T84.66/212 Flex minibody allowed imaging of xenografts at 4 and 19 h after admin istration.
Car accident cause analysis

4 2. Abstract 2128 standardised face to face in-depth interviews were carried out by 28 psychologists in Austria. 852 accidents were reported (involving material damage and/or personal injury). The main contributory factor to the cause of these accidents was inattentiveness (36%). The main reason for being inattentive was thinking about other things while driving. 14% of all accidents were primarily caused by driving too fast for the specific situation. The main reason for driving too fast was stress, followed by a lack of critical thinking about the speed at that moment in time. Aggressive emotions, risk-taking behaviour etc. did not play a major role. Another 14% of all accidents were primarily caused by inexperience in such specific situations. This factor was found significantly more frequently amongst young drivers. 10% of all accidents were due to lack of proper safety margins to the vehicle in front – also mainly because of stress and lack of critical thinking. Lack of skill caused 7% of all accidents, fatigue was in 5% of all accidents the main contributory factor; alcohol and other reasons were responsible for a further 5%. The remaining risk of 9% were external factors which occurred suddenly and unexpectedly. Generally speaking, 42% of accidents were primarily caused by emotional states where the driver was not in balance within himself, e.g. stress, sorrow, having problems to solve, etc. It was observed that the accident risk of novice drivers declines continuously from the beginning of the driving career over the first 36 months. The risk of having an accident causing personal injury was 12 times higher for young car drivers, 3 times higher for older drivers (early 80s) and 11 times higher for even older drivers (late 80s), when compared to the average age group. These results were gathered from two separate data sources: first, 1000 telephone interviews based on annual kilometres driven and, second, the official road accident statistics for Austria in 2005 concerning personal injuries, as well as the statistics on the distribution of the Austrian population per age group. The majority of drivers (57%) favours the present speed limit of 130 km/h on Austrian motorways, women more than men. Priority for pedestrians at pedestrian / zebra crossings is favoured by 86% of car drivers. An automatic gear shift is only favoured by 20% of drivers. Women get upset by cars following them too closely, men rather because of cars driving …
Serotonin modulates locomotory behavior and coordinates egg-laying and movement in Caenorhabditis elegans.

Biogenic amines have been implicated in the modulation of neural circuits involved in diverse behaviors in a wide variety of organisms. In the nematode C. elegans, serotonin has been shown to modulate the temporal pattern of egg-laying behavior. Here we show that serotonergic neurotransmission is also required for modulation of the timing of behavioral events associated with locomotion and for coordinating locomotive behavior with egg-laying. Using an automated tracking system to record locomotory behavior over long time periods, we determined that both the direction and velocity of movement fluctuate in a stochastic pattern in wild-type worms. During periods of active egg-laying, the patterns of reversals and velocity were altered: velocity increased transiently before egg-laying events, while reversals increased in frequency following egg-laying events. The temporal coordination between egg-laying and locomotion was dependent on the serotonergic HSN egg-laying motorneurons as well as the decision-making AVF interneurons, which receive synaptic input from the HSNs. Serotonin-deficient mutants also failed to coordinate egg-laying and locomotion and exhibited an abnormally low overall reversal frequency. Thus, serotonin appears to function specifically to facilitate increased locomotion during periods of active egg-laying, and to function generally to modulate decision-making neurons that promote forward movement.
Towards a Parallelising COBOL Compiler Rizos Sakellariou

This paper briefly describes some of the fundamental issues in developing an automatic parallelising C OBOL compiler. The rationale for such a tool and its connection with research in scientific computing is described. This is followed by an account of the useful forms of parallelism to be found in a COBOL program and how they may be detected using dependence analysis. Finally, transformations to uncover parallelism and how this can be mapped onto a parallel architecture are outlined.
Organ-specific isogenic metastatic breast cancer cell lines exhibit distinct Raman spectral signatures and metabolomes

Molecular characterization of organ-specific metastatic lesions, which distinguish them from the primary tumor, will provide a better understanding of tissue specific adaptations that regulate metastatic progression. Using an orthotopic xenograft model, we have isolated isogenic metastatic human breast cancer cell lines directly from organ explants that are phenotypically distinct from the primary tumor cell line. Label-free Raman spectroscopy was used and informative spectral bands were ascertained as differentiators of organ-specific metastases as opposed to the presence of a single universal marker. Decision algorithms derived from the Raman spectra unambiguously identified these isogenic cell lines as unique biological entities – a finding reinforced through metabolomic analyses that indicated tissue of origin metabolite distinctions between the cell lines. Notably, complementarity of the metabolomics and Raman datasets was found. Our findings provide evidence that metastatic spread generates tissue-specific adaptations at the molecular level within cancer cells, which can be differentiated with Raman spectroscopy.
Design and Deployment of Enhanced VNode Infrastructure - Deeply Programmable Network Virtualization

We introduce the design and deployment of the latest version of the VNode infrastructure, VNode-i. We present new extended VNode-i functions that offer high performance and provide convenient deep programmability to network developers. We extend resource abstraction to the transport network and achieve highly precise slice measurement for resource elasticity. We achieve precise resource isolation for VNode-i. We achieve coexistence of high performance and programmability. We also enhance AGW functions. In addition, we extend network virtualization from the core network to edge networks and terminals. In evaluation experiments, we deploy the enhanced VNode-i on the JGN-X testbed and evaluate its performance. We successfully create international federation slices across VNode-i, GENI, and Fed4FIRE. We also present experimental results on video streaming on a federated slice across VNode-i and GENI. Testbed experiments confirm the practicality of the enhanced VNode-i. key words: network virtualization, deep programmability, resource abstraction, resource isolation, testbed, international federation
Person specific characteristic analysis time domain techniques for ECG signals

This paper research Work investigates the feasibility of using the electrocardiogram (ECG) as a new biometric for human identification and verification. It is well known that the shapes of the ECG waveforms of different person are different but it is unclear whether such differences can be used identify different individuals. In this work we demonstrated successfully that it is possible to identify a specific person from group of persons. In order to Prove the ECG signals can be used for the biometric purposes The 5 seconds ECG data of 5 individuals are taken and shown how the both sessions ECG data of every individuals are having unique characteristics, while they are different characteristics for different subjects. This can be proved by drawing time domain, frequency domain plots for ECG signals of 5 individuals of different sessions this results shown that ECG is used for Biometric. After proving this we implemented a Time domain methods for identifications for ECG signals for Different ECG complexes. Results show that PQRST, QRS wave, P wave has much person specific information than T Wave.
ELECTROMYOGRAPHY GAIT TEST FOR PARKINSON DISEASE RECOGNITION USING ARTIFICIAL NEURAL NETWORK CLASSIFICATION IN INDONESIA

Diagnosa Parkinson Disease (PD) di Indonesia dilakukan secara klinis. Analisa langkah jalan dengan menggunakan sensor electromyography ( EMG ) menawarkan alternatif deteksi untuk PD. Sinyal EMG umumnya sulit dimengerti secara klinis. Penelitian ini menggunakan metode pengenalan pola sebagai alat untuk analisa sinyal EMG bagi subjek sehat dan subjek dengan PD. Sinyal EMG direkam dari 15 pasien dengan PD dan 8 subjek sehatdengan usia yang relatif muda saat subjek melakukan langkah jalan. Pengambilan data langkah jalan dilakukan di Rumah Sakit dr Kariadi Semarang, Jawa Tengah Indonesia. Dua belas buah fitur EMG digunakan untuk feature calculation pada sinyal EMG, delapan fitur adalah fitur yang berdasarkan domain waktu dan empat fitur berdasarkan domain frekuensi. Penelitian ini bertujuan untuk mengklasifikasikan dua kelas yaitu kelas untuk subjek sehat dan kelas untuk subjek PD menggunakan metode klasifikasi Artificial Neural Network (ANN) dengan jaringan feed forward dua layer. Jaringan menggunakan metode fungsi transfer log-sigmoid untuk hidden layer dan transfer fungsi softmax untuk output layer. ANN menggunakan 20 neuron pada hidden layer dan 2 neuron pada output layer. Metode training ANN menggunakan algoritma Levenberg-Marquardt dan penentuan nilai eror akurasi menggunakan metode mean square error (MSE). Berdasarkan hasil penghitungan, akurasi klasifikasi dua kelas untuk membedakan anatar subjek sehat dengan subjek PD sebesar 88.4%. Kata kunci : Artificial Neural Network (ANN), electromyography (EMG), gait test, Parkinson’s Disease.