Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiographic Prevalence of High‐risk Coronary Artery Disease in Patient Subsets (CASS)

The prevalence of coronary artery stenoses 2 70% or left main stenosis > 50% was evaluated in 20,391 patients who underwent angiography in the Coronary Artery Surgery Study from 1975-1979. After the patients with unstable angina or myocardial infarction were excluded, the disease prevalence in the 8157 patients with definite angina, probable angina, and nonspecific chest pain was 93%, 66% and 14% in men and 72%, 36% and 6% in women (p < 0.001). The age and sex of the patients and character of chest pain were important determinants of disease prevalence and severity. Left main or three-vessel coronary disease occurred in more than 50% of middle-aged men and older women with definite angina and in more than 50% of men who had probable angina and were older than 60 years of age. In contrast, left main coronary disease occurred in less than 2% of 1282 men and less than 1% of 1397 women with nonspecific chest pain regardless of age. In this latter patient subset, less than 5% of men and less than 1% of women in each decade under 60 years had left main or three-vessel coronary artery disease.Thus, high-risk coronary disease is common in middle-aged patients with definite angina and older patients with probable angina, but is rare in patients with nonspecific chest pain. Indications and guidelines for diagnostic noninvasive tests and coronary angiography could be based on these results.
THE EFFECT OF MOTOR SYMPTOM ONSET LATERALITY ON FACIAL EXPRESSIVITY IN PARKINSON ’ S DISEASE

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science THE EFFECT OF MOTOR SYMPTOM ONSET LATERALITY ON FACIAL EXPRESSIVITY IN PARKINSON’S DISEASE By Anne Noelle Nisenzon May 2008 Chair: Dawn Bowers Major: Psychology--Clinical and Health Psychology The purpose of the present study was to examine the effect of motor symptom laterality commonly found in Parkinson’s disease (PD) as well as mood and motor symptoms on different aspects of facial expressivity. Most patients with PD initially present with lateralized motor symptoms on one side of the body, left or right. This asymmetry usually persists throughout the course of the disease and reflects differential disruption of contralateral basal ganglia-frontal lobe dopaminergic systems. While a growing number of studies have reported that laterality of symptom onset may also influence non-motor symptoms of the disease (i.e., cognition), it remains unknown whether emotional processing is affected by symptom onset laterality. A cardinal symptom of PD is a “masked face,” or significantly diminished facial expressivity. Based on views regarding a right hemispheric specialization for emotional behavior, we hypothesized that patients with left-sided motor symptom onset (implicating greater right hemisphere involvement) would display greater impairments in facial expressivity than those with right sided symptom onset. We also predicted that mood and PD-specific motor symptoms would contribute to diminished facial expressivity. Twenty six patients with idiopathic PD (13 left symptom onset, 13 right onset) and 13 healthy controls were videotaped while posing facial expressions of fear, anger, and happiness.
Millimeter-wave delay spread measurement and simulation at LoS urban low-rise environments

Delay spread characteristics in the 28GHz band were measured at line of sight urban environments using narrow beamwidth antennas and simulated using ray-tracing with the same conditions. The channel sounder has 500MHz wide bandwidth at 28GHz, and using swept time delayed cross-correlation technique. The measurement environment is urban low-rise areas. The measurement and simulation results are compared to predict a delay spread with a line of sight condition.
Utilidade do Orçamento Empresarial sob a Ótica da Teoria da Dependência de Recursos Utility Budget Business the Perspective of Theory Dependency of Resources

The competitiveness imposes the need for efficient management tools that assist companies in formulating their strategies, minimize the uncertainties of dependence on environment and resources essential to organizational survival. The objective of the study is to evaluate the relationship of scarce resources, uncertainty exogenous and accentuation strategies with utility business budget. To achieve the study's results held a descriptive, through a survey, using a quantitative approach, with application of descriptive statistics and canonical correlation. The research sample comprised 48 industries located in the state of Santa Catarina. It is concluded that organizations widely use the budget before the planning reasons operational, performance evaluation, communication of objectives and targets and formulation of strategies. Firms indicate low dependence on key features, low uncertainty and high stress environment for strategies. Finally, we conclude that the usefulness of business budget is positively related to accentuation strategies.
Increased Expression of the Pro-Protein Convertase Furin Predicts Decreased Survival in Ovarian Cancer

Background: Proprotein convertases (PCs) are serine proteases that after restricted proteolysis activate many proteins that play a crucial role in cancer such as metalloproteinases, growth factors and growth factor receptors, adhesion molecules, and angiogenic factors. Although the expression of several PCs is increased in many tumors, their expression in primary ovarian tumors has not been studied in detail. We sought to determine if there was an association between the expression of the ubiquitously expressed PCs, furin, PACE-4, PC-5 and PC-7, and ovarian tumor progression. Methods: We assessed their expression by RT-PCR, Real-time PCR, Western blot, and immunohistochemistry using cells derived from normal human ovarian surface epithelium (HOSE) and cancer cell lines as well as ovarian epithelial cancer specimens (45 RT-PCR/Real-time PCR, and 120 archival specimens for Immunohistochemistry). Results: We found that furin expression was restricted to the cancer cell lines. In contrast, PACE-4 and PC-7 showed expression only in normal HOSE cells lines. Furthermore, furin was predominantly expressed in primary tumors from patients who survived for less than five years. The other PCs are either expressed in the group of survivors (PC-7 and PACE4) or expressed in low amounts (PC-5). Conclusions: Our studies point to a clear relationship between furin and ovarian cancer. In addition, these results show that furin exhibits the closest association with ovarian cancer among the ubiquitously expressed PCs, arguing against the redundancy of these proteases. In summary, furin may constitute a marker for ovarian tumor progression and could contribute to predict the outcome of this disease.
Characterizing driving environments through Bluetooth discovery

Within the world of wireless technologies, Bluetooth has recently been at the forefront of innovation. It is becoming increasingly relevant for vehicles to become aware of their surroundings. Therefore, having knowledge of nearby Bluetooth devices, both inside and outside other vehicles, can provide the listening vehicles with enough data to learn about their environment. In this paper, we collect and analyze a dataset of Bluetooth Classic (BC) and Low Energy (BLE) discoveries. We evaluate their respective characteristics and ability to provide context-aware information from a vehicular perspective. By taking a look at data about the encountered devices, such as GPS location, quantity, quality of signal and device class information, we infer distinctive behaviors between BC and BLE relative to context and application. For this purpose, we propose a set a features to train a classifier for the recognition of different driving environments (i.e. road classes) from Bluetooth discovery data alone. Comparing the performance of our classifier with different sampling parameters, the presented results indicate that, with our feature selection, we are able to predict with reasonable confidence up to three classes (Highway, City, Extra-Urban) by using only discovery data and no geographical information. This outcome gives promising results targeted at low energy and privacy-friendly applications and can open up a wide range of research directions.
Transfer Hashing with Privileged Information

Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines.
MySQL and MATLAB Interfacing for Biometric Template Protection with Encryption

A new approach for biometric database management is presented with enhanced security features. This paper attempts to improve the security of biometric database system by interfacing with MySQL server. The signature templates are acquired for the database using digital pen tablet, encrypted using a simple pseudo noise based algorithm and then stored on the MySQL server. During the authentication phase, the signatures are brought from the database for feature extraction. After decryption, they are compared with query template. Since, the database is password protected, it provides double security against adversary attacks.
StAR-related lipid transfer domain protein 5 binds primary bile acids[S]

Steroidogenic acute regulatory-related lipid transfer (START) domain proteins are involved in the nonvesicular intracellular transport of lipids and sterols. The STARD1 (STARD1 and STARD3) and STARD4 subfamilies (STARD4–6) have an internal cavity large enough to accommodate sterols. To provide a deeper understanding on the structural biology of this domain, the binding of sterols to STARD5, a member of the STARD4 subfamily, was monitored. The SAR by NMR [1H-15N heteronuclear single-quantum coherence (HSQC)] approach, complemented by circular dichroism (CD) and isothermal titration calorimetry (ITC), was used. Titration of STARD5 with cholic (CA) and chenodeoxycholic acid (CDCA), ligands of the farnesoid X receptor (FXR), leads to drastic perturbation of the 1H-15N HSQC spectra and the identification of the residues in contact with those ligands. The most perturbed residues in presence of ligands are lining the internal cavity of the protein. Ka values of 1.8·10−4 M−1 and 6.3·104 M−1 were measured for CA and CDCA, respectively. This is the first report of a START domain protein in complex with a sterol ligand. Our original findings indicate that STARD5 may be involved in the transport of bile acids rather than cholesterol.
Relaxing queries to detect variants of design patterns

Design patterns codify general solutions to frequently encountered design problems. They also facilitate writing robust and readable code. Their usage happens to be particularly profitable if the documentation of the resulting system is lost, inaccurate or out of date. In reverse engineering, detection of instances of design patterns is extremely helpful as it aids grasping high level design ideas. However, the actual instances of design patterns can diverge from their canonical textbook templates. Useful pattern detection tools should thus be able to identify not only orthodox implementations but also their disparate variants. In this paper, we present a method to generate queries to detect canonical instances of design patters. We formulate these queries so that they are language-agnostic. They precisely reflect the intents of the canonical implementations of design patterns. However, they abstract from any peculiarities of programming languages. Next, we show a systematic technique to relax these queries so that they also cover variant implementations of patterns. We discuss our proof-of-concept implementation of this approach in our prototype tool D-CUBED. Finally, we report the results of an experimental comparison of D-CUBED and state-of-the-art detectors.
Sensor fault detection for diesel engines

This paper deals with the problem of sensor fault detection for diesel engines. We are particularly interested to detect faults affecting the inlet and exhaust pressure sensors. First, this problem is reformulated as actuator fault detection problem by augmenting the system equations by an auxiliary state representing the dynamic behavior of the sensor fault. Then, a nonlinear unknown input observer (NIUO) is used for estimating simultaneously the original states and the sensor fault. The modified mean value theorem (MMVT) is applied to express the nonlinear error dynamics as a convex combination of known matrices with time varying coefficients. Sufficient conditions for the existence of the unknown input observer is presented. The observer gains are obtained by solving the linear matrix inequality (LMI). Performances of the proposed approach are shown through the application to a diesel engine model.
Impact and opportunities of medium-voltage DC grids in urban railway systems

Due to the increasing number of power-electronic components connected to the grid, medium-voltage dc (MVDC) grids are a promising alternative to established ac distribution grids. Within this work, the advantages and additional flexibility of substations for (urban) railways connected to future MVDC distribution grids are investigated. Possible topologies are presented and the efficiencies are compared.
Multi3: Multi-Sensory Perception System for Multi-Modal Child Interaction with Multiple Robots

Child-robot interaction is an interdisciplinary research area that has been attracting growing interest, primarily focusing on edutainment applications. A crucial factor to the successful deployment and wide adoption of such applications remains the robust perception of the child's multimodal actions, when interacting with the robot in a natural and untethered fashion. Since robotic sensory and perception capabilities are platform-dependent and most often rather limited, we propose a multiple Kinect-based system to perceive the child-robot interaction scene that is robot-independent and suitable for indoors interaction scenarios. The audio-visual input from the Kinect sensors is fed into speech, gesture, and action recognition modules, appropriately developed in this paper to address the challenging nature of child-robot interaction. For this purpose, data from multiple children are collected and used for module training or adaptation. Further, information from the multiple sensors is fused to enhance module performance. The perception system is integrated in a modular multi-robot architecture demonstrating its flexibility and scalability with different robotic platforms. The whole system, called Multi3, is evaluated, both objectively at the module level and subjectively in its entirety, under appropriate child-robot interaction scenarios containing several carefully designed games between children and robots.
Comparative Study on Feature Selection and Fusion Schemes for Emotion Recognition from Speech Santiago Planet and

— The automatic analysis of speech to detect affective states may improve the way users interact with electronic devices. However, the analysis only at the acoustic level could be not enough to determine the emotion of a user in a realistic scenario. In this paper we analyzed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of features. The acoustic set was reduced by a greedy procedure selecting the most relevant features to optimize the learning stage. We compared two versions of this greedy selection algorithm by performing the search of the relevant features forwards and backwards. We experimented with three classification approaches: Naïve-Bayes, a support vector machine and a logistic model tree, and two fusion schemes: decision-level fusion, merging the hard-decisions of the acoustic and linguistic classifiers by means of a decision tree; and feature-level fusion, concatenating both sets of features before the learning stage. Despite the low performance achieved by the linguistic data, a dramatic improvement was achieved after its combination with the acoustic information, improving the results achieved by this second modality on its own. The results achieved by the classifiers using the parameters merged at feature level outperformed the classification results of the decision-level fusion scheme, despite the simplicity of the scheme. Moreover, the extremely reduced set of acoustic features obtained by the greedy forward search selection algorithm improved the results provided by the full set.
Analysis of Various Deterioration Factors of Data Aggregation in Wireless Sensor

In embedded systems, wireless sensor network is the interesting field which provides various applications such as battlefield surveillance, disaster management, habitat monitoring, home automation, health care systems etc. Wireless sensor network (WSN) handles a huge number of tiny, cost effective, low powered, autonomous devices called sensor nodes which can perform sensing, processing and communicating operations. WSN has many constraints like energy utilization, limited memory and power consumption. Data aggregation is an intelligent technique which accumulates data from disparate sources by using various aggregation techniques. One of the crucial techniques in WSN is data aggregation which greatly increases the energy efficiency and network lifetime by reducing the number of data transmissions. To perform data aggregation in an efficient manner, there are lot of issues are to be considered. In this paper several issues related with data aggregation are discussed and comparisons of various design issues of various data aggregation protocols are also addressed.
Community Forest Management and the Emergence of Multi-Scale Governance Institutions: Lessons for REDD+ Development from Mexico, Brazil and Bolivia

At their most local, initiatives to reduce emissions from deforestation and degradation (REDD) will depend on rural people to manage forest resources. Although the design of frameworks, mechanisms and arrangements, to implement REDD programs have received significant attention, it is not yet clear how REDD+ will function on the ground or how the participation of local populations will be assured. Community forest management (CFM) could be an option under REDD+ depending on how it is negotiated, largely because of the expectation that CFM could reduce emissions from deforestation and degradation. Examining institutional factors in the emergence of successful CFM systems and local forest enterprises could provide valuable lessons for REDD planners. We examine cases of CFM development in Mexico, Brazil and Bolivia, to assess the role of multi-scaled governance institutions in their development. Comparing and contrasting advanced CFM systems to regions where it is still emerging, we will show how the establishment of a local organizational base for communal resource management is crucial.
Whole Genome Sequencing of Hulunbuir Short-Tailed Sheep for Identifying Candidate Genes Related to the Short-Tail Phenotype

The Hulunbuir short-tailed sheep (Ovis aries) is a breed native to China, in which the short-tail phenotype is the result of artificial and natural selection favoring a specific set of genetic mutations. Here, we analyzed the genetic differences between short-tail and normal-tail phenotypes at the genomic level. Selection signals were identified in genome-wide sequences. From 16 sheep, we identified 72,101,346 single nucleotide polymorphisms. Selection signals were detected based on the fixation index and heterozygosity. Seven genomic regions under putative selection were identified, and these regions contained nine genes. Among these genes, T was the strongest candidate as T is related to vertebral development. In T, a nonsynonymous mutation at c.G334T resulted in p.G112W substitution. We inferred that the c.G334T mutation in T leads to functional changes in Brachyury—encoded by this gene—resulting in the short-tail phenotype. Our findings provide a valuable insight into the development of the short-tail phenotype in sheep and other short-tailed animals.
Money Measure Measure Distances in Images Using a US Dollar Bill

The goal of this project is to make it easier to measure objects in day to day life. This is normally difficult to do because a ruler or tape measure is required, which is difficult to come by in many situations. Money Measure works by allowing you to use a smartphone camera to measure things. By taking a photo of an object next to a common US dollar bill, Money Measure can measure the distance between any two points in the scene. The dollar bill is automatically detected and utilized as a reference to real world distances. Additionally, the image is analyzed such that the image can be taken from a nonperpendicular angle the measurement will still be accurate.
Separate responses of karyopherins to glucose and amino acid availability regulate nucleocytoplasmic transport

The subcellular distribution of yeast β-importins inverts upon acute glucose deprivation, likely due to collapse of the RanGTP nuclear–cytoplasmic gradient. This redistribution of β-importins likely results in rapid widespread alterations of the traffic of macromolecules between the nucleus and cytoplasm in response to glucose limitation.
How Word Meaning Influences Word Reading

Understanding how we read is a fundamental question for psychology, with critical implications for education. Studies of word reading tend to focus on the mappings between the written and spoken forms of words. In this article, we review evidence from developmental, neuroimaging, neuropsychological, and computational studies that show that knowledge of word meanings is inextricably involved in word reading. Consequently, models of reading must better specify the role of meaning in skilled reading and its acquisition. Further, our review paves the way for educationally realistic research to confirm whether explicit teaching of oral vocabulary improves word reading.