Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential effects of metformin and troglitazone on cardiovascular risk factors in patients with type 2 diabetes.

OBJECTIVE Traditional cardiovascular risk factors (CVRF) only partly explain the excessive risk of cardiovascular disease in patients with type 2 diabetes. There is now an increasing appreciation for many novel CVRF that occur largely as a result of insulin resistance and hyperinsulinemia. Therefore, we investigated whether diabetes medications that vary in their mechanism of action and ability to reduce insulin resistance may differ in their effects on both traditional and novel CVRF. RESEARCH DESIGN AND METHODS We compared the addition of metformin or troglitazone therapy on CVRF in 22 subjects with type 2 diabetes who remained in poor glycemic control (with HbA1c >8.5%) while taking glyburide 10 mg twice daily. Subjects were initially randomized to either metformin 850 mg once daily or troglitazone 200 mg once daily. Both medications were then titrated upward as needed to achieve fasting plasma glucose <120 mg/dl. Measures of glucose control, insulin resistance, and CVRF (blood pressure, lipids, plasminogen activator inhibitor-1, C-reactive protein, fibrinogen, and small dense LDL) were assessed both before and after therapy. RESULTS After 4 months of treatment, both metformin and troglitazone led to similar decreases in fasting plasma glucose and HbA1c. The reduction in insulin resistance determined by hyperinsulinemic-euglycemic clamp was nearly twofold greater with troglitazone than metformin. Metformin did not induce significant changes in blood pressure, LDL cholesterol, LDL size, HDL cholesterol, triglycerides, or plasminogen activator inhibitor-1. However, C-reactive protein did decrease by 33% (6 +/- 1 to 4 +/- 1 mg/l; P < 0.01) [corrected]. Troglitazone therapy was associated with increases in LDL size (26.21 +/- 0.22 to 26.56 +/- 0.25 nm; P=0.04) and HDL cholesterol (33 +/- 3 to 36 +/- 3 mg/dl; P=0.05) and decreases in triglycerides (197 +/- 19 to 155 +/- 23 mg/dl; P=0.07) and C-reactive protein by 60% (8 +/- 3 to 3 +/- 1 mg/l, P < 0.01) [corrected]. CONCLUSIONS For patients with type 2 diabetes in whom maximal sulfonylurea therapy failed, the addition of the insulin sensitizer troglitazone seemed to have greater benefits on several traditional and novel CVRF than metformin therapy. These differences were not related to glycemic improvement but reflected, in part, the greater reduction in insulin resistance obtained with addition of troglitazone. These data suggest that medications that more effectively address this underlying metabolic defect may be more beneficial in reducing cardiovascular risk in type 2 diabetes.
Fast identification of gene clusters in prokaryotic genomes

The detection of gene clusters that are conserved in several genomes, in terms of gene proximity and gene content, have proved to be an invaluable tool in the comparative analysis of prokaryotic genomes. It has applications, for example, in predicting functional association between groups of genes or putative genome rearrangements. We propose an efficient algorithm for computing gene clusters, based on the hypothesis of the prevalence of short reversals in the evolution of prokaryotic genomes.
A SENTENCE-PITCH-CONTOUR GENERATION METHOD USING VQ/HMM FOR MANDARIN TEXT-TO-SPEECH

In this paper, a method with sentence-wide optimization consideration is proposed to generate a Mandarin sentence's pitch-contour. The developed model is called the sentence pitch-contour HMM (SPC-HMM) due to its use of VQ (vector quantization) and HMM (hidden Markov model). To construct an SPC-HMM, the pitch-contours of the syllables from each training sentence are normalized on both time and pitch-height first. The method for pitch-height normalization is effective and newly developed here. After normalization, the pitch-contour of each training syllable is vector quantized. Then, the quantization code and lexical tones of adjacent syllables are combined to define the observation symbol sequences for HMM training. In the synthesis phase, when given a sentence and its relevant text-analysis information, the most probable observation sequence is generated by finding the sentence-wide largest probability path with a dynamic-programming based algorithm. We had conducted practical perception tests. It is found that the speech synthesized by using the sentence pitch-contour generated from out method is slightly better than uttered by an ordinary speaker. Besides, the comprehensibility of the synthesized speech is also promoted.
Subcarrier SNR ML estimators and Cramér-Rao bounds in multicarrier transmission

In this paper, considering a multicarrier transmissions system, we propose two techniques of maximum-likelihood (ML) subcarrier signal-to-noise ratio (SNR) estimation, in both data-aided (DA) and non-data-aided (NDA) schemes, and derive the corresponding Crame¿r-Rao lower bounds (CRLBs). The channel gains and phases are assumed to be constant over the observation window and the received signal is assumed to be corrupted by additive white Gaussian noise (AWGN). The proposed SNR estimators both exploit the mutual information between the different subcarriers and reach the corresponding CRLBs, as shown by Monte-Carlo simulations.
Genetic Determinants of Pelvic Organ Prolapse among African American and Hispanic Women in the Women’s Health Initiative

Current evidence suggests a multifactorial etiology to pelvic organ prolapse (POP), including genetic predisposition. We conducted a genome-wide association study of POP in African American (AA) and Hispanic (HP) women from the Women’s Health Initiative Hormone Therapy study. Cases were defined as any POP (grades 1–3) or moderate/severe POP (grades 2–3), while controls had grade 0 POP. We performed race-specific multiple logistic regression analyses between SNPs imputed to 1000 genomes in relation to POP (grade 0 vs 1–3; grade 0 vs 2–3) adjusting for age at diagnosis, body mass index, parity, and genetic ancestry. There were 1274 controls and 1427 cases of any POP and 317 cases of moderate/severe POP. Although none of the analyses reached genome-wide significance (p<5x10-8), we noted variants in several loci that met p<10−6. In race-specific analysis of grade 0 vs 2–3, intronic SNPs in the CPE gene (rs28573326, OR:2.14; 95% CI 1.62–2.83; p = 1.0x10-7) were associated with POP in AAs, and SNPs in the gene AL132709.5 (rs1950626, OR:2.96; 95% CI 1.96–4.48, p = 2.6x10-7) were associated with POP in HPs. Inverse variance fixed-effect meta-analysis of the race-specific results showed suggestive signals for SNPs in the DPP6 gene (rs11243354, OR:1.36; p = 4.2x10-7) in the grade 0 vs 1–3 analyses and for SNPs around PGBD5 (rs740494, OR:2.17; p = 8.6x10-7) and SHC3 (rs2209875, OR:0.60; p = 9.3x10-7) in the grade 0 vs 2–3 analyses. While we did not identify genome-wide significant findings, we document several SNPs reaching suggestive statistical significance. Further interrogation of POP in larger minority samples is warranted.
Performance Evaluation of CMN for Mel-LPC based Speech Recognition in Different Noisy Environments

This study is intended to develop a noise robust distributed speech recognizer for real-world applications by employing Cepstral Mean Normalization (CMN) for robust feature extraction. The main focus of the work is to cope with different noisy environments. To realize this objective, MelLP based speech analysis has been used in speech coding on the linear frequency scale by applying a first-order all-pass filter instead of a unit delay. Mismatch between training and test phases is reduced through robust feature extraction by applying CMN on Mel-LP cepstral coefficients as an effort to reduce additive noise and channel distortion. The performance of the proposed system has been evaluated on test set A of Aurora-2 database which is a subset of TIDigits database contaminated by additive noises and channel effects. The experiment is conducted on four different noisy environments and the baseline performance, that is, for Mel-LPC the average word accuracy has found to be 59.05%. By applying the CMN on Mel-LP cepstral coefficients, the performance has been improved to 68.02%. It is found that CMN performs significantly better for different noisy environments.
Task Networks for Controlling Continuous Processes

This paper describes an extension to the RAP system task-net semantics and representation language to enable the effective control of continuous processes. The representation addresses the problems of synchronizing plan expansion with events in the world, coping with multiple, non-deterministic task outcomes, and the description of a simple form of clean-up task. It is also pointed out that success and failure need no special place in a task network representation. Success and failure are really messages about the execution system's knowledge and do not explicitly define that system's flow of control.
A Navigation Algorithm for Pedestrian Simulation in Dynamic Environments

This paper will introduce a novel online path planning method that mimics the human sense of navigation for finding a path between the current location (the ‘origin’) and the destination point. Most pedestrian simulation systems pre-compute a minimum cost (e.g. shortest length) path. Such an approach implies that the simulated agents have perfect knowledge of the infrastructure. Moreover, pre-computing the path can be numerically infeasible in situations where some of the obstacles are of dynamic nature, e.g. doors that might be closed or open, escalators which might operate or be under repair or obscure regions of parks which pedestrians choose not to frequent at night time. For such dynamic environments, the shortest path needs to be pre-computed for every, out of many, possible combination. The algorithm proposed in this paper assumes that the modeled agent may or may not have any prior knowledge of the infrastructure but is able to locate the direction of the destination at all times. The proposed navigation algorithm is an iterative procedure to maximize the values of smoothed distance and directional function from the destination to the origin. Such an algorithm is guaranteed to find a path from the origin to the destination if such a path exists at all. The proposed method has been implemented in a pedestrian multi agent simulation and has been tested on scenarios with different complexities.
Binarization of medical images based on the recursive application of mean shift filtering : Another algorithm

Binarization is often recognized to be one of the most important steps in most high-level image analysis systems, particularly for object recognition. Its precise functioning highly determines the performance of the entire system. According to many researchers, segmentation finishes when the observer’s goal is satisfied. Experience has shown that the most effective methods continue to be the iterative ones. However, a problem with these algorithms is the stopping criterion. In this work, entropy is used as the stopping criterion when segmenting an image by recursively applying mean shift filtering. Of this way, a new algorithm is introduced for the binarization of medical images, where the binarization is carried out after the segmented image was obtained. The good performance of the proposed method; that is, the good quality of the binarization, is illustrated with several experimental results. In this paper a comparison was carried out among the obtained results with this new algorithm with respect to another developed by the author and collaborators previously and also with Otsu’s method.
A reliable mobile agents architecture

This paper describes the design of a novel mobile agent system that supports the flexible and reliable interaction of autonomous components in an object-oriented distributed system. It discusses the object-oriented design of the overall system together with a number of important components in terms of the most important design decisions. A detailed description of the reliability model is given in terms of the failure hypothesis and the related recovery protocols. Special emphasis is given to the generality and efficiency of the implementation and a number of preliminary experiences are described.
ANSIEDADE MATERNA PUERPERAL E RISCO PARA ALTERAÇÕES NO DESENVOLVIMENTO INFANTIL

Esta pesquisa teve como objetivos analisar comparativamente as associacoes entre a ausencia ou presenca de indices de risco ao desenvolvimento infantil e presenca ou ausencia de estado ansioso materno; analisar a relacao entre dificuldade na constituicao da experiencia da maternidade e a presenca de indices de risco ao desenvolvimento infantil, bem como a interferencia ou nao de variaveis socioeconomicas, demograficas, psicossociais e obstetricas. A metodologia aplicada foi do tipo quantitativa descritiva, com carater casual comparativo sobre as manifestacoes comportamentais do processo interativo mae-bebe. A amostra foi constituida de 182 diades maes-bebes, nascidos a termo ou pre-termo, em um hospital universitario da regiao central do Rio Grande do Sul, que estavam realizando triagem auditiva neonatal no periodo de marco a junho de 2010. Foi realizada uma entrevista, aplicada a escala de ansiedade de Beck (BAI) e o protocolo IRDIs e observada a interacao da diade mae-bebe. A analise estatistica deu-se pela aplicacao de testes nao-parametricos. Os resultados demonstraram que ha uma correlacao positiva entre presenca de risco ao desenvolvimento infantil e estado materno ansioso. Tambem verificou-se que a presenca de dificuldades na constituicao da experiencia da maternidade correlacionou-se estatisticamente com a presenca de risco ao desenvolvimento infantil. Contudo, o risco ao desenvolvimento infantil e multifatorial, sendo necessario verificar os aspectos constitucionais do bebe e relacionais com o ambiente, sobretudo a mae, para analisar e decidir acerca do encaminhamento clinico.
ConfiDNS: Leveraging Scale and History to Improve DNS Security

While cooperative DNS resolver systems, such as Co-DNS, have demonstrated improved reliability and performance over standard approaches, their security has been weaker, since any corruption or misbehavior of a single resolver can easily propagate throughout the system. We address this weakness in a new system called ConfiDNS, which augments the cooperative lookup process with configurable policies that utilize multi-site agreement and per-site lookup histories. Not only does ConfiDNS provide better security than cooperative approaches, but for up to 99.8% of unique lookups, ConfiDNS exceeds the security of standard DNS resolvers. ConfiDNS provides these benefits while retaining the other benefits of Co-DNS, such as incremental deployability, improved performance, and higher reliability.
Internet addiction, behavioural aspects, and health related problems associated with it: a cross sectional study among engineering students of Jabalpur district

Background: With the rise of new generation gadgets, the risk of “internet addiction” is emerging as a significant behavioral addiction pandemic to be tackled worldwide. Internet addiction can be defined as an impulse disorder. Objective of the study was to determines the level of Internet addiction in Students. To identify the behavioural and any health-related problems associated with internet addiction among students. Methods: This study was a cross sectional study carried out in 7 different engineering colleges of Jabalpur city during the period of 1 July 2016 to 30 September 2016. Results: In present study the prevalence of internet addiction was found to be 74 %. 26 % of the participants were found with no internet addictions whereas 55 % and 17.67 % of the participants were found to be mild and moderate addicted whereas only 1.33 % of the participants were severely addicted to internet addiction. Participant who has internet addiction has 84.8 % of yelling and annoying behavior while 86.5 % participants with internet addiction have secretive and defensive behavior. Association was found to be highly significant (p = <0.001). Ill effects like problem related to eye have significant effect on the participants with internet addiction i.e. 66.17 %. Association of problem related to eye problems with internet addiction was significant with the p = 0.02. Conclusions: The problem is alarming and progressing to an addictive state in the near future Interventions like setting boundaries and detecting early warning signs of underlying psychopathology at the earliest are required.
Rare Disasters and Risk Sharing with Heterogeneous Beliefs

Risks of rare economic disasters can have large impact on asset prices. At the same time, difficulty in inference regarding both the likelihood and severity of disasters as well as agency problems can effectively lead to signiffcant disagreements among investors about disaster risk. We show that such disagreements generate strong risk sharing motives, such that just a small amount of optimists in the economy can significantly reduce the disaster risk premium. Our model highlights the "latent" nature of disaster risk: the disaster risk premium will likely be low and smooth during normal times, but can increase dramatically when the risk sharing capacity of the optimists is reduced, for example, following a disaster. The model also helps reconcile the difference in the amount of disaster risk implied by nancial markets and international macro data, and provides caution to the approach of extracting disaster probabilities from asset prices, which can disproportionately reflect the beliefs of a small group of optimists. Finally, our model predicts an inverse U-shaped relation between the equity premium and the size of the disaster insurance market.
Determination of the dielectric constant of materials from effective refractive index measurements

This paper presents an extension to the rectangular dielectric waveguide technique to obtain the permittivity of samples iteratively from the effective refractive index measurements by using the solution of the wave equation. The effective refractive index is presented for several samples in the 33 GHz to 50 GHz frequency range. The method is very fast and simple and provides an accuracy of better than 1% for the determination of permittivity of materials.
EMPOWERING APPROACHES BY CHINESE PARENTS OF SPECIAL NEED CHILDREN

There is a large variation in the experience of caring for special need children. The objective of this study was to identify the empowering approaches of Chinese parents related to their care-giving experience.17 Chinese parents from 16 families whose children are attending a private school and a learning centre were interviewed in this study. A semi-structured face-to-face interview was used and responses were coded using grounded theory to identify themes of empowering approaches. Being cognitively and psycho-emotionally grounded enables Chinese parents to make sound decisions. The overwhelming needs related to caring for their special need child impel them to remain pragmatic when making appraisals. Their concern about the future of their child urges them to adopt a proactive approach. Interventional services aimed at helping Chinese parents to care for their special need child should include initiatives of empowering them to adopt positive approaches. Future research should look into the effectiveness of these approaches when adopted by novice parents.
Implicit Social Network Model for Predicting and Tracking the Location of Faults

In software testing and maintenance activities, the observed faults and bugs are reported in bug report managing systems (BRMS) for further analysis and repair. According to the information provided by bug reports, developers need to find out the location of these faults and fix them. However, bug locating usually involves intensively browsing back and forth through bug reports and software code and thus incurs unpredictable cost of labor and time. Hence, establishing a robust model to efficiently and effectively locate and track faults is crucial to facilitate software testing and maintenance. In our observation, some related bug locations are tightly associated with the implicit links among source files. In this paper, we present an implicit social network model using PageRank to establish a social network graph with the extracted links. When a new bug report arrives, the prediction model provides users with likely bug locations according to the implicit social network graph constructed from the co-cited source files. The proposed approach has been implemented in real-world software archives and can effectively predict correct bug locations.
VISAGE: A Support Vector Machine Approach to Group Dynamic Analysis

A group is defined as a collective entity usually consisting of two or more individuals each connected by social relationships. The term 'group dynamics' was originally coined by social psychologist Kurt Lewin to describe the positive and negative forces within groups of people. Its study is useful today in a wide variety of applications such as gaining a better understanding of decision making behavior or evaluating the health of workplace environments. Metrics need to be defined to measure the quality of relations in a group by performing an analysis on each individual member of the group. Previous research in the field performs this through the means of surveying (generally through questionnaires) each member of the group. In this project we propose a novel method to analyze group dynamics from a single static image by performing automated facial expression recognition.
Fusion of color images and LiDAR data for lane classification

Lane classification is a fundamental problem for autonomous driving and map-aided localization. Many existing algorithms rely on special designed 1D or 2D filters to extract features of lane markings from either color images or LiDAR data. However, these handcrafted features could not be robust under various driving and lighting conditions. In this paper, we propose a novel algorithm to fuse color images and LiDAR data together. Our algorithm consists of two stages. In the first stage, we segment road surfaces and register LiDAR data with the corresponding color images. In the second stage, we train convolutional neural networks (CNNs) to classify image patches into lane markings and non-markings. Comparing with the algorithms based on handcrafted features, our algorithm learns a set of kernels to extract and integrate features from two different modalities. The pixel-level classification rate in our experiments shows that our algorithm is robust to different conditions such as shadows and occlusions.
Ontology mapping for the interoperability problem in network management

Interoperability between different network management domains, heterogeneous devices, and various management systems is one of the main requirements for managing complex enterprise services. While substantial advances have been made in low-level device and data interoperability using common data formats and specifications such as simple network management protocol's (SNMP's) SMI and TMF's SID, various interoperability issues including semantic interoperability offer interesting research challenges. While semantic interoperability is a difficult problem in its own right, the semantic web that incorporates intelligent agents necessitates an interoperability solution requiring agents to communicate unambiguously and reason intelligently to perform cooperative management tasks. Agents need a formal representation of knowledge; an ontology is capable of modeling the rich semantics of the managed environment (and especially, relationships between managed entities) so that agents can act on them. This paper presents an ontology-driven approach for solving the semantic interoperability problem in the management of enterprise services, illustrated here with a router configuration management application.