Miracle-dz/UniFAR-FTUs huggingface dataset:
Granzyme M Directly Cleaves Inhibitor of Caspase-Activated DNase (CAD) to Unleash CAD Leading to DNA Fragmentation1

Granzyme (Gzm)M is constitutively highly expressed in NK cells that may play a critical role in NK cell-mediated cytolysis. However, the function of GzmM has been less defined. Just one report showed GzmM induces a caspase-independent death pathway. In this study, we demonstrate a protein transfection reagent Pro-Ject can efficiently transport GzmM into target cells. GzmM initiates caspase-dependent apoptosis with typical apoptotic nuclear morphology. GzmM induces DNA fragmentation, not DNA nicking. GzmM can directly degrade inhibitor of caspase-activated DNase to release the nuclease activity of caspase-activated DNase for damaging DNA. Furthermore, GzmM cleaves the DNA damage sensor enzyme poly(ADP-ribose) polymerase to prevent cellular DNA repair and force apoptosis.
Exploring big data in small forms: A multi-layered knowledge extraction of social networks

Big data poses great challenges for social network analysts in both the data volume and the latent dimensions hidden in the unstructured data. In this paper, we propose a comprehensive knowledge extraction approach for social networks to guide latent dimensions analysis. An improved hypergraph model of social behaviors was then proposed for conveniently conducting multi-faceted analytics in relationships inherent to social media. A real life case study based on Twitter's data was also presented to illustrate the multi-dimensional relations between users based on the categories they co-join and the tweets they co-spread with three orthogonal dimensions of affect analyzed simultaneously, i.e. valence, activation, and intention.
Performance of Spatial Multiplexing and Transmit Diversity in Multi-Polarized MIMO Transmissions with Receive Antenna Selection

In this work, we consider the use of multiple antenna signaling technologies, specifically space-time block coding (STBC) and spatial multiplexing (SM) schemes, in multiple-input multiple-output (MIMO) communication systems employing dual polarized antennas at both ends. The use of dual-polarized antennas theoretically double the number of virtual antennas used in the system. On the other hand, the correlation effects and the cross-polar discrimination (XPD) deteriorates the system performance. In our work, we consider these effects and present model of a 3× 3 system with triple-polarized antennas for both STBC and SM cases. We also present simulation results for both multi-antenna signaling techniques together with hybrid approaches under various XPD and correlation scenarios. The simulation results show a significant performance gain by joint utilization of space, time and polarization diversity in comparison to uni-polarized systems with the same number of antennas. We observed that dual and triple polarized systems have different performances at various channel scenarios. Antenna selection performs better for systems with space time coding and boosts the performance more in triple-polarized MIMO compared to its dual-polarized counterpart. Keywords-Antenna selection, XPD, space-time coding, spatial multiplexing, dual polarization, triple polarization, polarization diversity.
Determination of sugar content in whole Port Wine grape berries combining hyperspectral imaging with neural networks methodologies

The potential of hyperspectral imaging combined with machine learning algorithms to measure sugar content of whole grape berries is presented, as a starting point for developing generalized and flexible frameworks to estimate enological parameters in wine grape berries. In this context, to evaluate the generalization ability of the used machine learning procedure, two neural networks were trained with different training data to compare the performance of each one when tested with the same data set. Six whole grape berries were used for each sample to draw the hyperspectral spectrum in reflectance mode between 308 and 1028 nm. The sugar content was estimated from the spectra using feedforward multiplayer perceptrons in two different neural networks trained each one with a data set from a different year (2012 & 2013); the validation for both neural networks was done by n-fold cross-validation, and the test set used was from 2013. The test set revealed R2 values of 0.906 and RMSE of 1.165 °Brix for the neural network trained with 2012 data and R2 of 0.959 and RMSE of 1.026 °Brix for the 2013 training data neural network. The results obtained indicate that both neural networks present good results and that the 2012 training data neural network exhibits a good performance when compared with the other NN, suggesting that the approach is robust since a generalization (without further training) over years may be obtainable.
A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud

A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches.
ISO observations of a sample of Compact Steep Spectrum and GHz Peaked Spectrum radio galaxies

We present results from observations obtained with ISOPHOT, on board the ISO satellite1, of a representative sample of seventeen CSS/GPS radio galaxies and of a control sample of sixteen extended radio galaxies spanning similar ranges in redshift (0.2 ≤ z ≤ 0.8) and radio luminosity (P2.7GHz ≥ 1026 W/Hz). The observations have been performed at λ = 60, 90, 174 and 200 μm. The original purpose of these observations was to check whether CSS/GPS sources are associated with very gas rich galaxies, as required by the scenario in which the growth of the radio source is inhibited by the dense medium of the host galaxy. Unfortunately the resulting performance of ISOPHOT was worse than expected. As a consequence, the detection limit at 60μm is similar to that obtained previously with IRAS but better than that at 90μm. Seven of the CSS/GPS sources have detections ≥ 3σ at one or more wavelengths, one of which is detected at ≥ 5σ. For the comparison sample five objects have detections ≥ 3σ one of which is at ≥ 5σ. By co–adding the data we have obtained average flux densities at the four wavelengths. We found no evidence that the FIR luminosities of the CSS/GPS sources are significantly different from those of the extended objects and therefore there is not any support for Send offprint requests to: F. Pozzi Correspondence to: cfanti@astbo1.bo. cnr.it 1 Based on observations with ISO, an ESA project with instruments funded by ESA Member States (especially the PI countries: France, Germany, the Netherlands and the United Kingdom) with the participation of ISAS and NASA. CSS/GPS sources being objects “frustrated” by an abnormally dense ambient medium. The two samples were then combined, providing FIR information on a new sample of radio galaxies at intermediate redshifts. We compare this information with what previously known from IRAS and discuss the average properties of radio galaxies in the redshift range 0.2 – 0.8. The FIR emission cannot be accounted for by extrapolation of the synchrotron radio spectrum and we attribute it to thermal dust emission. The average FIR luminosity is ≥ 6× 1011L . Over the observed frequency range the infrared spectrum can be described by a power law with spectral index α ' 1.0 ± 0.2. Assuming the emission to be due to dust, a range of temperatures is required, from ≥ 80 K to ≈ 25 K. The dust masses required to explain the FIR emission range from 5 × 105 M for the hotter component up to 2 × 108 M for the colder one. We present also observations on four nearby (z ≤ 0.1) GPS radio galaxies, two of which are detected at all four wavelengths.
Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.
Estimating body-fixed frame velocity and attitude from inertial measurements for a quadrotor vehicle

A key requirement for effective control of quadrotor vehicles is estimation of both attitude and linear velocity. Recent work has demonstrated that it is possible to measure horizontal velocities of a quadrotor vehicle from strap-down ac-celerometers along with a system model. In this paper we extend this to full body-fixed-frame velocity measurement by exploiting recent work in aerodynamic modeling of rotor performance and measurements of mechanical power supplied to the rotor hub. We use these measurements in a combined attitude and velocity nonlinear observer design to jointly estimate attitude and body-fixed-frame linear velocity. Almost global asymptotic stability of the resulting system is demonstrated using Lyapunov analysis of the resulting error system. In the current work, we ignore bias and leave it for future work. The performance of the observer is verified by simulation results.
K%-Fair scheduling: A flexible task scheduling strategy for balancing fairness and efficiency in MapReduce systems

MapReduce is an important programming paradigm on big data-intensive computing using share-nothing cluster containing ten of thousands of nodes, in which computing nodes also acts as storage nodes. Since tasks belonging to different jobs are physical executing entities scattered among the whole cluster, task scheduling plays a crucial role in MapReduce systems. For data consolidation and utilization, MapReduce cluster is usually used as an shared computing environment rather than several private clusters. Typical workloads consist of concurrent jobs, which include interactive jobs and batch jobs, so fairness is an important target in this scenario. On the other hand, efficiency is also an vital concern for cluster owner, data locality is used as a heuristic to achieve high efficiency. To achieve both goals is a huge challenge, requiring extensive research work. State of the art schedulers cannot well solve this problem. In this paper, we proposed K%-Fair scheduling, a flexible task scheduling strategy, based on multiple task queues on node level, according to fairness and data locality. Finally, we evaluate our scheduling on data locality and fairness among jobs, it improves data locality much more, in the same time, fairness is kept on nearly the same.
Motion extrapolation for video story planning

We create video scenes using existing videos. A panorama is generated from background video, with foreground objects removed by video inpainting technique. A video planning script is provided by the user on the panorama with accurate timing of actors. Actions of these actors are extensions of existing cyclic motions, such as walking, tracked and extrapolated using our motion analysis techniques. Although the types of video story generated are limited, however, it is possible to use the mechanism proposed to generate forgery videos. Interested readers should visit our tool demonstration and forgery videos at http://member.mine. tku.edu. tw/www/ACMMM08-VideoPlanning.
Life-Saving Super-Urgent Liver Transplantation with Replacement of Retrohepatic Vena Cava by Dacron Graft

We describe a modified technique of side-to-side cavo-cavostomy by Dacron interposition prosthesis during a super urgent liver transplantation. A liver graft from a deceased donor was immediately requested on a top priority basis as a consequence of massive bleeding during extended left hepatectomy for a huge hepatic haemangioma arising from the caudate lobe. Veno-venous bypass was employed during anhepatic phase but it was disconnected due to severe fibrinolysis and hypothermia. A porto-caval shunt was performed and the inferior vena cava outflow was restored by a Dacron interposition prosthesis. A liver graft from a deceased donor was available 16 hours later. Due to the shortness of the vena cava of the donor liver graft, the removal of the Dacron graft was impossible and a modified side-to-side cavo-cavostomy between the Dacron interposition graft and the vena cava of the donor liver was than performed. Liver transplantation was uneventful and the patient is doing well 25 months after the surgical procedure. Although the use of synthetic vascular prosthesis should usually be discouraged during organ transplantation, its exceptional use during liver transplantation is possible with long-term good results.
THE METHOD OF WAREHOUSE LOCATION SELECTION BASED ON GIS AND REMOTE SENSING IMAGES

The traditional P-median selecting location model of logistics is based on the statistical static model. These traditional mathematical models often don’t consider topography, transport conditions, slop etc. The distance between two nodes is often assumed for the straight line in this model, so the analytical result usually can not be used as a warehouse in actual applied. So the paper isn’t only takes a catena supermarket warehouse as an example and adopts the geographical information system (GIS) technology, spatial analysis methods and remote sensing images to establish warehouse selection model of logistics distribution, but also improves Pmedian selecting location model. At the same time, the most suitable warehouse location is determined by multi-standards. The method mainly includes three processes: building networks, handling remote sensing and overlapping networks to remote images. Since the networks’ distance is used in the model, the analysis result is more science and more close to reality. And the method can reduce blindness of choosing the warehouse location in catena manage, the customer is given some information of assistance decision. * Ji Huifeng: lecturer, email:jhf_sy@126.com; Xu Aigong: professor, doctorial tutor, email:eagxu@ntu.edu. sg
Regulation of pain sensitivity in experimental osteoarthritis by the endogenous peripheral opioid system.

OBJECTIVE OA is the most common joint disease, affecting 10-15% of people over 60 years of age. However, up to 40% of individuals with radiologic damage are asymptomatic. The purpose of this study was to assess the role of the endogenous opioid system in delaying the onset of pain in a murine model of osteoarthritis (OA). METHODS Osteoarthritis was induced by transection of the medial meniscotibial ligament. Pain was assessed by monitoring weight distribution and activity. At various times postsurgery, the opioid receptor antagonists naloxone or peripherally restricted naloxone methiodide were administered, and pain was assessed. Levels of the micro-opioid receptor were assessed in the nerves innervating the joint by real-time reverse transcription-polymerase chain reaction analysis. RESULTS As in human disease, significant joint damage occurred in mice before the onset of pain. To assess whether delayed pain was partly the result of increased endogenous opioid function, naloxone or naloxone methiodide was administered. Both opioid receptor antagonists led to pain onset 4 weeks earlier than in vehicle-treated mice, indicating a role of the peripheral opioid system in masking OA pain. The expression of the micro-opioid receptor in the peripheral nerves supplying the joint was transiently increased in naloxone-responsive mice. CONCLUSION These findings indicate that a temporal induction of micro-opioid receptors in the early stages of OA delays the onset of pain. This is of clinical relevance and may contribute to the assessment of patients presenting with pain late in the disease. Furthermore, it may point to a mechanism by which the body blocks pain perception in moderate states of tissue damage, allowing an increased chance of survival.
Using Tweaks to Design Fault Resistant Ciphers

Side channel analysis and active fault analysis are now major threats to even mathematically robust cryptographic algorithms that are otherwise resistant to classical cryptanalysis. This paper focuses on designing encryption schemes that use secret tweaks to achieve innate security against fault analysis. The paper examines linear and non-linear secret tweak based versions of a recently proposed block cipher DRECON. The paper demonstrates that while both versions are secure against classical DFA, the non-linear tweak based version provides greater fault coverage against stronger fault models. Experimental results obtained on a SASEBO GII platform have been presented to establish the fact that combined security by construction against both DPA and DFA makes DRECON a strong candidate for the design of secure cryptographic primitives.
Proinsulin/Insulin Autoantibodies Measured With Electrochemiluminescent Assay Are the Earliest Indicator of Prediabetic Islet Autoimmunity

OBJECTIVE We evaluated a novel electrochemiluminescent assay for insulin/proinsulin autoantibodies (ECL-IAA) as a new marker of the onset of islet autoimmunity and as a predictor of type 1 diabetes. RESEARCH DESIGN AND METHODS The Diabetes Autoimmunity Study in the Young (DAISY) prospectively follows children at increased genetic risk for development of islet autoimmunity (defined as presence of autoantibodies to insulin, GAD65, IA-2, or zinc transporter 8 [ZnT8]) and type 1 diabetes (general population of children and first-degree relatives). Serial serum samples from subjects who progressed to type 1 diabetes and who had their first islet autoantibodies measured by age 18 months (N = 47) were tested using ECL-IAA. RESULTS Almost all prediabetic children tested positive for ECL-IAA (46 of 47, 98%) during follow-up. ECL-IAA was almost always the first autoantibody to appear (94% total; 21% very first [by itself]; 23% with only mIAA; 19% with another islet autoantibody [GAD or ZnT8]; and 30% with ≥2 other antibodies [mIAA, GAD, IA-2, or ZnT8]). Among the 46 subjects who were ECL-IAA positive, ECL-IAA antedated the onset of other islet autoantibodies by a mean of 2.3 years (range, 0.3–7.2 years). Both the age of appearance of autoantibody and IAA levels (but not GAD65, IA2, or ZnT8 levels) are major determinants of the age of diabetes onset. CONCLUSIONS This new ECL-IAA assay defines more precisely the onset of prediabetic autoimmunity and may help identify events triggering islet autoimmunity, as well as allow earlier intervention for type 1 diabetes. Nearly all young children progressing to diabetes are insulin autoantibody positive.
Extrinsic calibration of a fisheye camera and inertial measurement unit

Due to its robust performance and wide potential application, the fusion of visual and inertial sensors for motion estimation has attracted significant attention recently. For this fusion, extrinsic calibration is a prerequisite. But most existing methods need a special configuration in which the camera can observe the inertial sensor. In this paper we present an extrinsic calibration method for visual and inertial sensors in a general configuration in which the visual and inertial sensors can not observe each other. The proposed method introduces a total station as a reference. The total station can be adjusted to be parallel to the ground-plane to which the inertial sensor can provide the rotation. Then the rotation between the fisheye camera and the total station is deduced via a homography. Finally the rotation between the fisheye camera and the inertial measurement unit can be obtained. We implement the proposed method on our lunar rover prototype. Experiment results show the validity of the proposed method.
Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay

In this paper, a weighting-delay-based method is developed for the study of the stability problem of a class of recurrent neural networks (RNNs) with time-varying delay. Different from previous results, the delay interval [0, d(t)] is divided into some variable subintervals by employing weighting delays. Thus, new delay-dependent stability criteria for RNNs with time-varying delay are derived by applying this weighting-delay method, which are less conservative than previous results. The proposed stability criteria depend on the positions of weighting delays in the interval [0, d(t)], which can be denoted by the weighting-delay parameters. Different weighting-delay parameters lead to different stability margins for a given system. Thus, a solution based on optimization methods is further given to calculate the optimal weighting-delay parameters. Several examples are provided to verify the effectiveness of the proposed criteria.
Fast Nonrigid Image Registration using Salient Region Feature and Free Form Deformations

2 Introduction Image registration aims to spatially align one image to another and establish point-to-point correspondences between the images. It is an active and important topic of research in medical image analysis because of its numerous applications including change detection in longitudinal studies, multi-modal image fusion, motion correction in time series, image-guided intervention, anatomical atlas generation and group analysis. Registration happens at both global and local levels. Global registration methods recover parameters of a global transformation model, such as rigid and affine, to account for global differences such as translation, rotation, and scaling. In many cases of medical image registration, local registration that utilizes an elastic or non-rigid deformation model is also required to cope with local differences between the images. Such local differences are due to, for examples, scanner-induced deformations, movement and/or respiratory motion of the patient, surgical intervention, or different anatomy (e.g. in atlas generation). This chapter will review the fundamentals of medical image registration, and describe a novel efficient non-rigid image registration method. The method is based on finding salient region-feature correspondences between images and solving for parameters of a Free Form Deformations (FFD) model in closed form.