Miracle-dz/UniFAR-FTUs huggingface dataset:
Regional Myocardial Metabolic Rate of Oxygen Measured by O2-15 Inhalation and Positron Emission Tomography in Patients with Cardiomyopathy

Background Positron emission tomography (PET) metabolic studies have investigated the pathways involved in fatty acid, glucose, and oxidative metabolism in cardiomyopathy and the impairments that occur in the damaged myocardium, but none have provided absolute quantitative variables. Recently, quantitative measurements of the metabolic rate of oxygen (MMRO2) and oxygen extraction fraction (OEF) using O2-15–labeled oxygen gas have been validated in animals and healthy volunteers. The purposes of the current study were to measure MMRO2 and OEF in cardiomyopathy with left ventricular (LV) dysfunction. Methods The authors selected 25 study participants: 16 patients (10 with ischemic and 6 with dilated) cardiomyopathy with LV dysfunction, and 9 healthy volunteers. As evaluated by echocardiography, LV ejection fraction (LVEF) was decreased in patients (35% ± 9% vs. 65% ± 5%, P < 0.01). The PET protocol consisted of transmission, C O2-15 static, H2 O-15 dynamic, and O2-15 gas inhalation steady state scans. An entire myocardial region of interest was drawn to encompass the entire LV myocardium on three midventricular slices in each participant. Results Data showed in patients with dilated cardiomyopathy significant reductions of MMRO2 (0.051 ± 0.02 ml · min−1 · g−1 vs. 0.108 ± 0.02 ml · min−1 · g−1, P = 0.01) and OEF (0.55 ± 0.15 vs. 0.71 ± 0.08, P = 0.01) when compared with healthy volunteers. Furthermore, OEF decreased significantly in lateral and inferior walls. Significant correlations were observed among OEF and the rate-pressure product (RPP) (P = 0.02), LVEF (P < 0.001), MMRO2 and RPP (P = 0.04), and LVEF (P = 0.05). In patients with ischemic cardiomyopathy, MMRO2 was significantly reduced (0.039 ± 0.02 ml · min−1 · g−1 vs. 0.108 ± 0.02 ml · min−1 · g−1;P = 0.005) but not OEF (0.63 ± 0.2 vs 0.71 ± 0.08;P = NS), when compared with healthy volunteers. Significant correlations were observed among OEF and RPP (P = 0.03), LVEF (P = 0.002), MMRO2 and RPP (P < 0.01), and LVEF (P = 0.03). Conclusions These data show that O2-15 gas inhalation and PET allow myocardial MMRO2 and OEF to be measured in patients with cardiomyopathy.
Stress characterization of a ceramic/metal jointed interface by collimated X-ray beam radiography and scanning acoustic microscopy

The process of heating and then cooling dissimilar materials is well known to introduce considerable stress at and near the interface. In this article, first, the surface wave velocity distributions obtained with the V(z) curve technique were found to compare well with residual stress distribution obtained by the collimated X-ray diffraction. Second, a delamination was introduced at the interface. The V(z) curve technique was then used again to measure the surface acoustic wave velocity along the interface. The defective specimens showed significantly different patterns of surface acoustic wave velocities. Thus, this study presents useful guidelines in discriminating between sound and defective ceramic/metal joints by scanning acoustic microscopy.
Experiment study on utilizing convolutional neural networks to recognize historical Arabic handwritten text

Deep learning is a form of hierarchical learning, it consists of multiple layers of representations that gradually transform data into high level concepts. Deep learning has been providing the state of the art results for various computer vision problems. However, a typical deep leaning algorithm needs a large amount of data to train a deep model and guarantee the models ability to generalize. It is not easy to generate large labeled datasets and it is one of the main barriers to apply deep learning for many problems. Data augmentation schemes were introduced to overcome this limitation, by extending small available labeled datasets. In this work we experiment with extending a small labeled dataset of Arabic continuous subwords by an orders of magnitude. The labeled dataset, which consist of handwritten Arabic subwords is used to synthesize a large collection of labeled dataset. The synthesized subwords are based on one or multiple writing styles from the original labeled dataset. We also experiment with generating various printed forms of subwords. We include only Naskh font, as most of the Arabic historical manuscripts were written in this type of font. We train several convolutional neural networks using handwritten, printed and synthesized datasets and obtain encouraging results.
An Investigation on Wear of Shallow Cryogenic Treated Wire in Wedm

The wire electrical discharge machining (WEDM) is widely used for machining workpieces having complex geometries. A wire electrode of diameter 0.25 mm is moved coaxially between two guides and electrical energy in the form of electrical pulses is applied to remove material from the workpiece. In this study, the effect of key process parameters on wire wear of shallow cryogenic treated brass wire electrode used in WEDM was investigated experimentally. Five process parameters, namely Type of wire, Pulse width, Time between two pulses, Wire tension and Servo reference voltage have been considered. Taguchi experimental design has been applied to investigate the optimal parameters for minimum wire wear ratio (WWR). The experimental results indicate that Type of wire; Pulse width and Time between two pulses have a significant effect on WWR. The analysis of variance of mean and analysis of variance of S/N ratio on data was performed to identify the significant factors in order to establish the relative significance of parameters for WWR. It is found experimentally that shallow cryogenic treatment decreases the WWR. Increasing pulse width increases the WWR, where as increasing Time between two pulses decreases WWR. The results have been validated by three confirmation runs for optimum WWR.
Striatal Dopamine D2/D3 Receptor Availability Is Associated with Executive Function in Healthy Controls but Not Methamphetamine Users

Background Dopamine D2/D3 receptor availability in the striatum has been linked with executive function in healthy individuals, and is below control levels among drug addicts, possibly contributing to diminished executive function in the latter group. This study tested for an association of striatal D2/D3 receptor availability with a measure of executive function among research participants who met DSM-IV criteria for methamphetamine dependence. Methods Methamphetamine users and non-user controls (n = 18 per group) completed the Wisconsin Card Sorting Test and positron emission tomography with [18F]fallypride. Results The methamphetamine users displayed significantly lower striatal D2/D3 receptor availability on average than controls after controlling for age and education (p = 0.008), but they did not register greater proportions of either perseverative or non-perseverative errors when controlling for education (both ps ≥ 0.622). The proportion of non-perseverative, but not perseverative, errors was negatively correlated with striatal D2/D3 receptor availability among controls (r = -0.588, p = 0.010), but not methamphetamine users (r = 0.281, p = 0.258), and the group-wise interaction was significant (p = 0.030). Conclusions These results suggest that cognitive flexibility, as measured by perseverative errors on the Wisconsin Card Sorting Test, is not determined by signaling through striatal D2/D3 receptors in healthy controls, and that in stimulant abusers, who have lower D2/D3 receptor availability, compensation can effectively maintain other executive functions, which are associated with D2/D3 receptor signaling in controls.
Topological Descriptors in Modeling Tumor Necrosis Factor alpha Inhibitory Activity of Xanthines, Pteridinediones and Related Compounds

Motivation. Inhibition of tumor necrosis factor alpha (TNF–D ) is an important strategy for the treatment of various inflammatory, infectious, immunological, or malignant diseases such as Crohn’s disease, rheumatoid arthritis and psoriasis. Therefore, the TNF–D inhibitory activity of xanthines, pteridinediones and related compounds has been analyzed with different topostructural and topochemical indices. Method. The quantitative structure–activity relationships (QSAR) study was performed using derivatives of xanthines, pteridinediones and related compounds with Dragon 3.0 structural descriptors. The relationship between inhibitory activity and various descriptors is established by step–wise multiple regression analysis using Systat 10.2 and Valstat. Results. The analyses have generated significant and predictive QSAR models. The values of statistical indices for QSAR with simple topological descriptors is R = 0.91, F = 19.92, SEE = 24 and R 2 CV = 0.68; for QSAR with more complex topological descriptors is R = 0.90, F = 25.74, SEE = 0.23 and R 2 CV = 0.67 and for QSAR with mixed classes of descriptors is R = 0.93, F = 25.93, SEE = 0.21 and R 2 CV = 0.73. Conclusions. These models suggest that Lopping centric index, fourth order bond information content of neighbourhood symmetry, polarizability and electronegativity weighted Geary graph spatial autocorrelation coefficient of the first lag, atomic masses weighted Moran graph spatial autocorrelation coefficient of the third lag, and average connectivity indexF 1 have a direct correlation with the biological activity. The average valence connectivity indexF 3 , electronegativity weighted Geary graph spatial autocorrelation coefficient of the seventh lag, electronegativity weighted Moran graph spatial autocorrelation coefficient of the fifth lag and number of substituted aromatic sp 2 carbon have an inverse correlation with the biological activity.
Modeling Spatio-Temporal Variability in Human-Robot Interaction with Probabilistic Movement Primitives

The task of physically assisting humans requires from robots the ability to adapt in many different ways: to changes in space of the human movement, to changes in the speed of the human, to changes in the environment, etc. This paper presents recent research on teaching robots how to interact with humans and to adapt to different circumstances. The approach presented here is based on Imitation Learning and Probabilistic Movement Representations. In particular, this paper explains the concept of a Mixture of Interaction Primitives to learn interactions from multiple unlabeled demonstrations and to deal with nonlinear correlations between the interacting partners. Furthermore, a method to compute reactions to human movements executed at different speeds is presented. A number of experiments with a lightweight robotic arm illustrate applications of the presented methods.
Culture of central nervous system neurons on electrospun polymer fiber-covered surfaces

As the world population ages, the need for alternative therapies for neurodegenerative diseases is increasing. Neuronal tissue engineering is one of the most promising methods for creating in vitro disease models and therapies as well as regeneration of lost brain tissue due to neurodegenerative diseases. But due to inherent difficulties of culturing neurons and glia in vitro, studies in this field proceeds slowly. Our aim is to produce surfaces that nerve cells can attach and grow on in vitro. Electrospun chitosan fibers that are shown to be suitable for neurons to grow were produced in three different chemical compositions. New born hippocampal neurons and PC12 model cells were cultured on these surfaces and their interaction with the surfaces were investigated. The cells interact with electrospun fibers and these surfaces can be used for cell replacement in future clinical applications, providing new possibilities to treat human neurodegenerative diseases.
Hierarchical Large Margin Nearest Neighbor Classification

Distance metric learning has exhibited its great power to enhance performance in metric related pattern recognition tasks. The recent large margin nearest neighbor classification (LMNN) improves the performance of k-nearest neighbor classification by learning a global distance metric. However, it does not consider the locality of data distributions, which is crucial in determining a proper metric. In this paper, we propose a novel local distance metric learning method called hierarchical LMNN (HLMNN) which first builds a hierarchical structure by grouping data points according to the overlapping ratios defined by us and then learns distance metrics sequentially. Experimental results on real-world data sets including comparisons with the traditional k-nearest neighbor and the state-of-the-art LMNN show the effectiveness of the proposed HLMNN.
A Real-time Integrity Monitor for Xen Virtual Machine

File-system integrity tools (FIT) are commonly deployed to assist forensic investigation after security incidents and as host-based intrusion detections (HIDS) tool to detect unauthorized file-system changes. Basically all the current solutions employ the same tactic: the administrator specifies a list of critical files and directories that needs to be monitored, then uses the FIT to create a base-line database that tracks general parameters about these files. The FIT is then re-run periodically, and if it detects the modifications of the file-system against the information stored in the database, the report on the changed file is generated. However, this strategy is far from perfect: the intrusion detection cannot be done in real-time, which might render the whole scheme useless if the attacker can somehow take over the system with privileged access in the time between. The administrator also has a lot of problems to keep the database updating. Besides, he must do everything he can to protect the database and the FIT itself from compromising by the attacker, which is not an easy task especially if the attacker gains local access. This paper presents a novel approach to address the outstanding problems of the current FIT. We propose a design and implementation of a tool named XenRIM for Xen virtual machines. XenRIM can monitor and fires alarms on intrusion in real-time manner, and our approach does not require to create and update the database like in the legacy methods. As a result, XenRIM is almost effortless to deploy and maintain. Thanks to the advantage introduced by Xen, the detection polices are centralized in a secure virtual machine and resistant to tampering. Even better, if deployed strictly, this tool is able to function very stealthily to avoid the suspect of the attacker. Our experimental result demonstrates that XenRIM incurs very low performance overhead (less than 4%), which makes the solution attractive and practical for production systems
Multispecies focused ion beam lithography system and its applications

The authors present a focused ion beam lithography (IBL) instrument and its extension toward using different ion species beyond gallium. The base instrument utilizes a lithography architecture and an ion source and column dedicated to nanofabrication. This includes large area navigation and patterning by a laser interferometer stage, long-term beam to sample positional as well as beam current stability and automation capabilities. Since the ion type can have dramatic consequences on the resulting nanostructures, the authors have extended the gallium IBL tool's ion column and source toward the stable delivery of multiple species for a nanometer scale focused ion beam based on a liquid metal alloy ion source. The IBL system is equipped with an E × B mass filter capable of selecting different single and multiple charged ion species, simultaneously originating from the same source. The authors investigated different AuSi or AuGe based sources and in particular an ion source delivering Au, Si and Be focused io.
The Role and Quality of Software Safety in the NASA Constellation Program

In this study, we examine software safety risk in the early design phase of the NASA Constellation spaceflight program. Obtaining an accurate, program-wide picture of software safety risk is difficult across multiple, independently-developing systems. We leverage one source of safety information, hazard analysis, to provide NASA quality assurance managers with information regarding the ongoing state of software safety across the program. The goal of this research is two-fold: 1) to quantify the relative importance of software with respect to system safety; and 2) to quantify the level of risk presented by software in the hazard analysis. We examined 154 hazard reports created during the preliminary design phase of three major flight hardware systems within the Constellation program. To quantify the importance of software, we collected metrics based on the number of software-related causes and controls of hazardous conditions. To quantify the level of risk presented by software, we created a metric scheme to measure the specificity of these software causes. We found that from 49-70% of hazardous conditions in the three systems could be caused by software or software was involved in the prevention of the hazardous condition. We also found that 12-17% of the 2013 hazard causes involved software, and that 23-29% of all causes had a software control. Furthermore, 10-12% of all controls were software-based. There is potential for inaccuracy in these counts, however, as software causes are not consistently scoped, and the presence of software in a cause or control is not always clear. The application of our software specificity metrics also identified risks in the hazard reporting process. In particular, we found a number of traceability risks in the hazard reports may impede verification of software and system safety.
Eigenvalue analysis of plasmonic waveguides in layered geometries

An eigenvalue analysis of plasmonic waveguides built in a general layered medium is introduced by combining the Multiple Multipole Program (MMP) with modified layered media Green's functions (LMGF). The new method, first, provides the possible locations of the eigenvalues on a given complex plane (called search functions in the MMP analysis), which provides a very useful information when locating the eigenvalues not only by MMP but also by other numerical methods. Special eigenvalue search and tracing routines are then used to determine the exact locations of the eigenvalues around the possible locations provided by the search functions. The details of the MMP and the modification on LMGF is introduced, together with various numerical examples demonstrating the efficiency of the method.
Tighter Integration of Rule-Based and Statistical MT in Serial System Combination

Recent papers have described machine translation (MT) based on an automatic post-editing or serial combination strategy whereby the input language is first translated into the target language by a rule-based MT (RBMT) system, then the target language output is automatically post-edited by a phrase-based statistical machine translation (SMT) system. This approach has been shown to improve MT quality over RBMT or SMT alone. In this previous work, there was a very loose coupling between the two systems: the SMT system only had access to the final 1-best translations from RBMT. Furthermore, the previous work involved European language pairs and relatively small training corpora. In this paper, we describe a more tightly integrated serial combination for the Chinese-to-English MT task. We will present experimental evaluation results on the 2008 NIST constrained data track where a significant gain in terms of both automatic and subjective metrics is achieved through the tighter coupling of the two systems.
An evaluation of 10 kits for determination of human choriogonadotropin in serum.

We compared 10 commercially available radioimmunoassay kits (American Diagnostics, Becton Dickinson, BioGenex, Clinical Assays, Hybritech, Leeco, Mallinckrodt, Microanalytic, Nuclear Medical Systems, and Radioassay Systems) for determination of human choriogonadotropin (hCG), using serum pools, hCG CR119 (NIH), 2nd I.S. (who), and 1st I.R. P. (who). Criteria were ease of performance, total assay time, sensitivity, potency, and parallelism as compared with reference standards and results for 15 serum pools. The Mallinckrodt kit exhibited the best overall performance, with good low-concentration sensitivity, parallelism with two of the three reference preparations, and good clinical correlation as compared with the reference kit from NIH. Because the antibodies used in the kits are occasionally changed by the manufacturers, these results are necessarily valid only for kits that include reagents identical to those in the kits that we tested.
Quantitative assessment of ultrastructure and light scatter in mouse corneal debridement wounds.

PURPOSE The mouse has become an important wound healing model with which to study corneal fibrosis, a frequent complication of refractive surgery. The aim of the current study was to quantify changes in stromal ultrastructure and light scatter that characterize fibrosis in mouse corneal debridement wounds. METHODS Epithelial debridement wounds, with and without removal of basement membrane, were produced in C57BL/6 mice. Corneal opacity was measured using optical coherence tomography, and collagen diameter and matrix order were quantified by x-ray scattering. Electron microscopy was used to visualize proteoglycans. Quantitative PCR (Q-PCR) measured mRNA transcript levels for several quiescent and fibrotic markers. RESULTS Epithelial debridement without basement membrane disruption produced a significant increase in matrix disorder at 8 weeks, but minimal corneal opacity. In contrast, basement membrane penetration led to increases in light scatter, matrix disorder, and collagen diameter, accompanied by the appearance of abnormally large proteoglycans in the subepithelial stroma. This group also demonstrated upregulation of several quiescent and fibrotic markers 2 to 4 weeks after wounding. CONCLUSIONS Fibrotic corneal wound healing in mice involves extensive changes to collagen and proteoglycan ultrastructure, consistent with deposition of opaque scar tissue. Epithelial basement membrane penetration is a deciding factor determining the degree of ultrastructural changes and resulting opacity.