Miracle-dz/UniFAR-FTUs huggingface dataset:
The genotoxic and antigenotoxic effects of Salvia fruticosa leaf extract in human blood lymphocytes

Abstract The aim of this study was to investigate the genotoxic and antigenotoxic effects of Salvia fruticosa (Sf) leaf extract with the absence and presence of S9 mix using sister chromatid exchange (SCE), chromosome aberration (CA) and micronucleus (MN) formation test systems in human peripheral blood lymphocytes (HPBLs) that were treated with 1.5-, 3.0- and 6.0-µL/mL concentrations for 24- and 48-hour treatment periods. The cytotoxicity of Sf leaf extract was also investigated by calculating the mitotic index (MI), proliferation index (PI) and nuclear division index (NDI). In the absence of S9 mix, Sf leaf extract alone increased SCE frequency at the 48-hour treatment period; however, it induced the CA and MN at all concentrations and at all treatment periods. Sf plus MMC (mitomycin C) synergically induced SCE and CA, except the highest concentration of Sf leaf extract and MMC on induction of SCE. In addition, Sf leaf extract induced the effect of MMC on MN frequency for 24 hours, but it significantly decreased the effect of MMC on MN frequency for the 48-hour treatment period. Sf leaf extract showed a cytotoxic effect by decreasing the MI; however, it did not decrease the PI and NDI. In the presence of S9 mix, Sf leaf extract did not increase the SCE, when compared to solvent control, whereas it reduced the effect of cyclophosphamide (Cyp). Sf leaf extract induced the CA and MN, but could not increase the effect of Cyp on CA and MN formation. Sf leaf extract had no cytotoxic effect; however, it induced the cytotoxicity of Cyp.
Numerical methods for data assimilation in weather forecasting

Data assimilation plays an important role in weather forecasting. The purpose of data assimilation is try to provide a more accurate atmospheric state for future forecast. Several existed methods currently used in this field fall into two categories: statistical data assimilation and variational data assimilation. This thesis focuses mainly on variational data assimilation. The original objective function of three dimensional data assimilation (3D-VAR) consists of two terms: the difference between the pervious forecast and analysis and the difference between the observations and analysis in observation space. Considering the inaccuracy of previous forecasting results, we replace the first term by the difference between the previous forecast gradients and analysis gradients. The associated data fitting term can be interpreted using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3D-VAR setting. In our approach, it is not necessary to estimate the background error covariance matrix and to deal with its inverse in the 3D-VAR algorithm. Indeed, the existence and uniqueness of the analysis solution of the proposed objective function are already established. Instead, the solution can be calculated using the conjugate gradient method iteratively. We present the experimental results based on WRF simulations. We show that the performance of this forecast gradient based DA model is better than that of 3D-VAR. Next, we propose another optimization method of variational data assimilation. Using the tensor completion in the cost function for the analysis, we replace the second term in the 3D-VAR cost function. This model is motivated by a small number of observations compared with the large portion of the grids. Applying the alternating direction method of multipliers to solve this optimization problem, we conduct numerical experiments on real data. The results show that this tensor completion based DA model is competitive in terms of prediction accuracy with 3DVAR and the forecast gradient based DA model. Then, 3D-VAR and the two model proposed above lack temporal information, we construct a third model in four-dimensional space. To include temporal information,
Introduction to the special issue on link mining

An emerging challenge for data mining is the problem of mining richly structured datasets, where the objects are linked in some way. Many real-world datasets describe a variety of entity types linked via multiple types of relations. These links provide additional context that can be helpful for many data mining tasks. Yet multi-relational data violates the traditional assumption of independent, identically distributed data instances that provides the basis for many statistical machine learning algorithms. Therefore, new approaches are needed that can exploit the dependencies across the attribute and link structure.
Effect of maternal confidence on breastfeeding duration: an application of breastfeeding self-efficacy theory.

BACKGROUND Although much research has focused on identifying factors that influence breastfeeding initiation and duration, many high-risk factors are nonmodifiable demographic variables. Predisposing factors for low breastfeeding duration rates that are amenable to supportive interventions should be identified. The purpose of this study was to assess the effect of maternal confidence (breastfeeding self-efficacy) on breastfeeding duration. METHOD A prospective survey was conducted with 300 women in the last trimester of pregnancy recruited from the antenatal clinic of a large metropolitan hospital in Brisbane, Australia. Telephone interviews were conducted at 1 week and 4 months postpartum to assess infant feeding methods and breastfeeding confidence using the Breastfeeding Self-Efficacy Scale. RESULTS Although 92 percent of participants initiated breastfeeding, by 4 months postpartum almost 40 percent discontinued and only 28.6 percent were breastfeeding exclusively; the most common reason for discontinuation was insufficient milk supply. Antenatal and 1-week Breastfeeding Self-Efficacy Scale scores were significantly related to breastfeeding outcomes at 1 week and 4 months. Mothers with high breastfeeding self-efficacy were significantly more likely to be breastfeeding, and doing so exclusively, at 1 week and 4 months postpartum than mothers with low breastfeeding self-efficacy. CONCLUSIONS Maternal breastfeeding self-efficacy is a significant predictor of breastfeeding duration and level. Integrating self-efficacy enhancing strategies may improve the quality of healthcare that healthcare professionals deliver and may increase a new mother's confidence in her ability to breastfeed, and to persevere if she does encounter difficulties.
A novel FEM-based dynamic framework for subdivision surfaces

Subdivision surfaces have been extensively used to model smooth shapes of arbitrary topology. Recursive subdivision on an userdefined initial control mesh generates a visually pleasing smooth surface in the limit. However, users have to carefully specify the initial mesh and/or painstakingly manipulate the control vertices at different levels of subdivision hierarchy to satisfy various functional and aesthetic requirements in the limit surface. This modeling drawback results from the lack of direct manipulation tools for the limit surface. In this paper, we integrate physicsbased modeling techniques with geometric subdivision methodology and present an unified approach for arbitrary subdivision schemes. Our dynamic framework permits users to directly manipulate the limit subdivision surface via physics-based ”force” tools. The key contribution of this unified approach is to formulate the smooth limit surface of any subdivision scheme as a single type of novel finite elements. The geometric and dynamic features of our subdivision-based finite elements depend on the subdivision scheme involved. We present our finite element method (FEM) and formulation for the modified butterfly and Catmull-Clark subdivision schemes, and further generalize our dynamic framework for any subdivision scheme. Our FEM-based approach significantly advances the state-of-the-art of physics-based geometric modeling because (1) our dynamic framework provides a universal physicsbased solution to any subdivision scheme beyond frequently-used and popular spline-like subdivision techniques; (2) we systematically devise a natural mechanism that allows users to intuitively deform any subdivision surface; (3) we represent the smooth limit surface of any subdivision scheme using a single type of novel subdivision-based finite elements. Our experiments demonstrate that the new unified FEM-based framework promises a greater potential of subdivision techniques for solid modeling, finite element analysis, and engineering design.
Molecular cloning of the human gene , CCG 2 , that complements the BHK-derived temperature-sensitive cell cycle mutant tsBN 63 : identity of CCG 2 with the human X chromosomal SCAR / RPS 4 X gene

1 Department of Molecular Biology, Graduate School of Medical Science, Kyushu University, Fukuoka, Maidashi 812, Japan ^Department of Microbiology, New York University School of Medicine, 550 First Avenue, New York, NY 10016, USA Department of Biology, Faculty of Science, Nagoya University, Chigusa-ku Nagoya, 464-01, Japan Department of Biochemistry and Molecular Biology, Walther Oncology Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA
Smart Coat with a Fully-Embedded Textile Antenna for IoT Applications

The Internet of Things (IoT) scenario is strongly related with the advance of the development of wireless sensor networks (WSN) and radio frequency identification (RFID) systems. Additionally, in the WSN context, for a continuous feed, the integration of textile antennas for energy harvesting into smart clothing is a particularly interesting solution when the replacement of batteries is not easy to practice, such as in wearable devices. This paper presents the E-Caption: Smart and Sustainable Coat. It has an embedded dual-band textile antenna for electromagnetic energy harvesting, operating at global system for mobile communication (GSM) 900 and digital cellular system (DCS) 1800 bands. This printed antenna is fully integrated, as its dielectric is the textile material composing the coat itself. The E-Caption illustrates the innovative concept of textile antennas that can be manipulated as simple emblems. Seven prototypes of these “emblem” antennas, manufactured by lamination and embroidering techniques are also presented. It is shown that the orientation of the conductive fabric does not influence the performance of the antenna. It is also shown that the direction and number of the stitches in the embroidery may influence the performance of the antenna. Moreover, the comparison of results obtained before and after the integration of the antenna into cloth shows the integration does not affect the behavior of the antenna.
An Improved AdaBoost Algorithm for Unbalanced Classification Data

AdaBoost algorithm is proved to be a very efficient classification method for the balanced dataset with all classes having similar proportions. However, in real application, it is quite common to have unbalanced dataset with a certain class of interest having very small size. It will be problematic since the algorithm might predict all the cases into majority classes without loss of overall accuracy. This paper proposes an improved AdaBoost algorithm called BABoost (Balanced AdaBoost), which gives higher weights to the misclassified examples from the minority class. Empirical results show that the new method decreases the prediction error of minority class significantly with increasing the prediction error of majority class a little bit. It can also produce higher values of margin which indicates a better classification method.
Regularity theory for fully nonlinear integro‐differential equations

We consider nonlinear integro‐differential equations like the ones that arise from stochastic control problems with purely jump Lévy processes. We obtain a nonlocal version of the ABP estimate, Harnack inequality, and interior C1, α regularity for general fully nonlinear integro‐differential equations. Our estimates remain uniform as the degree of the equation approaches 2, so they can be seen as a natural extension of the regularity theory for elliptic partial differential equations. © 2008 Wiley Periodicals, Inc.
Religious vs. Conventional Cognitive Behavioral Therapy for Major Depression in Persons With Chronic Medical Illness: A Pilot Randomized Trial

Abstract We examine the efficacy of conventional cognitive behavioral therapy (CCBT) versus religiously integrated CBT (RCBT) in persons with major depression and chronic medical illness. Participants were randomized to either CCBT (n = 67) or RCBT (n = 65). The intervention in both groups consisted of ten 50-minute sessions delivered remotely during 12 weeks (94% by telephone). Adherence to treatment was similar, except in more religious participants in whom adherence to RCBT was slightly greater (85.7% vs. 65.9%, p = 0.10). The intention-to-treat analysis at 12 weeks indicated no significant difference in outcome between the two groups (B = 0.33; SE, 1.80; p = 0.86). Response rates and remission rates were also similar. Overall religiosity interacted with treatment group (B = −0.10; SE, 0.05; p = 0.048), suggesting that RCBT was slightly more efficacious in the more religious participants. These preliminary findings suggest that CCBT and RCBT are equivalent treatments of major depression in persons with chronic medical illness. Efficacy, as well as adherence, may be affected by client religiosity.
University of São Paulo Luiz de Queiroz College of Agriculture How is forest restoration plantations' functioning affected by tree diversity?

How is forest restoration plantations' functioning affected by tree diversity? Tropical forests restoration is an important tool for climate change mitigation and biodiversity conservation. We can ally both of these elements, according to the biodiversity and ecosystem (BEF) functioning theory, which says that diversity enhances ecosystem functions, as primary productivity. Nevertheless, the greatest part of BEF studies up to very recently have focused on grasslands and not on as complex ecosystems as tropical forests. It is necessary to better understand aboveand below-ground processes through which biodiversity acts on ecosystem functions. This work aimed to investigate effects of tree richness on both aboveand below-ground ecological processes. It was based on two tropical forests undergoing restoration, in Sardinilla (Panama) and in Anhembi (Brazil). The former was especially designed for BEF studies and allowed to untangle effects of biodversity on ecosystem functions. The latter had more than a hundred species in plots and permitted investigation of the effects of high tree richness levels. In both Sardinilla and Anhembi, we investigated if tree richness levels affected an above-ground ecological process, light interception, and which mechanisms could be related to it. Richness could enhance light interception and mechanisms as spatial (horizontal and vertical) and temporal light distribution. It promoted both selection and complementarity effects. In Anhembi, we investigated if species richness influenced below-ground processes related to soil carbon stocks. Stand richness enhanced fine root production and stock. Effects of stand number of species on litter decomposition and stock were not linear. Richness of litter content, however, did not affect its decomposition rates. Number of stand species did not influence litter production. Differences of litter production, stock and fine root production among distinct richness levels did not change over the time. However, distribution of fine roots over the space, within different layers of soil, was affected by number of tree species. We concluded that even very high richness levels could not saturate some of the ecological processes studied. Diversity acted on both aboveand below-ground processes, in various and sometimes opposite ways, counting on multi-direction feedbacks. It is very important to understand these mechanisms in order to potencialize biodiversity convervation and carbon sequestration by tropical forest restoration. Future studies may focus on untangling effects of diversity on below-ground processes (which have not been exhaustively explored in research), on understanding how high diversity levels affects natural regeneration and on investigationg functional traits provided by different species.
Image Deblurring Regularized by Wavelet Probability Shrink

An image deblurring algorithm based on wavelet probabiLity shrink regularization is proposed. Denoise and deblur were alternatively executed by least square approximate and probabiLity shrinkage. After several iterations of deblur, probabiLity shrink based on stationary wavelet transform (SWT) were used once for denoising. Experimental results show that proposed algorithm obtained better results on several benchmark images than classical regularization techniques such as wavelet soft shrink, TV, or even non-local TV proposed more recently.
Interpolating destin features for image classification

This paper presents a novel approach for image classification, by integrating advanced machine learning techniques and the concept of feature interpolation. In particular, a recently introduced learning architecture, the Deep Spatio-Temporal Inference Network (DeSTIN) [1], is employed to perform feature extraction for support vector machine (SVM) based image classification. The system is supported by use of a simple interpolation mechanism, which allows the improvement of the original low-dimensionality of feature sets to a significantly higher dimensionality with minimal computation. This in turn, improves the performance of SVM classifiers while reducing the computation otherwise required to generate directly measured features. The work is tested against the popular MNIST dataset of handwritten digits [2]. Experimental results indicate that the proposed approach is highly promising, with the integrated system generally outperforming that which makes use of pure DeSTIN as the feature extraction preprocessor to SVM classifiers.
Exploring Links Between Sensorimotor and Visuospatial Body Representations in Infancy

The aim of this study was to explore whether sensorimotor and visuospatial body representations interact in early development. Sixty-two infants between 6 and 15 months of age were tested for visuospatial body representations via a human body visual discrimination task. Their motor activity during the discrimination task, as well as motor development and imitation skill, were assessed as indexes of sensorimotor body representations. Based on research with adults, we predicted inter-correlations between performance on the visuospatial body task and infants' sensorimotor development; however, no significant associations were observed. Discussion speculates on developmental trajectories for these two distinct body representation systems.
Developing a Parking Monitoring System based on the analysis of images from an outdoor surveillance camera

In this paper, we describe a solution for monitoring of parking availability based on computer vision. It allows us to detect and track cars in a parking lot, while collected historical data helps us to predict availability status of parking during the day based on data mining techniques. Parking Monitoring System tracks availability based on an analysis of images from an outdoor surveillance camera and analyzes in real time the state of the parking complex. The system is developed to determine the number and location of available parking places and to inform the drivers. We provide an algorithm for image capture and analysis to recognize a car in a parking lot and to define parking spaces as either reserved or free.
Analysis of caesarean section rate according to modified Robson’s classification at tertiary care centre in Uttarakhand, India

Background: High caesarean section rate worldwide including India is matter of concern. The aim of this study is to analyse caesarean section rate at tertiary care centre according to Modified Robson’s classification.Methods: This retrospective study was conducted at Shri Guru Ram Rai Institute of Medical and Health Sciences (SGRRIMHS) and Shri Mahant Indiresh Hospital at Dehradun from April 2018 to September 2018. All women delivered during this period were classified according to modified Robson’s classification using their maternal characteristics and obstetric history. For each group, authors calculated the caesarean section rate within the group and its contribution to overall caesarean section rate.Results: Out of total 1302 women delivered, 395 underwent CS (30.3%). The major contribution to overall caesarean section rate was 33.4% by group 5 (Previous CS, singleton, cephalic, >37weeks) followed by 16.7% by group 1 (nullipara, singleton, cephalic, >37 weeks, spontaneous labour), 12.4% by group 3 ( multipara, singleton, cephalic, >37 weeks, spontaneous labour ). CS rates among various group ranges from 100% among women with abnormal lie (group 9) to 77.5% in nulliparous breech (group 6), 73.7% in previous CS (group 5) and least 11.2% in multipara induced or pre labour CS (group 4). Conclusions: Modified Robson classification is simple, systematic, reproducible and can be effectively utilized in analyzing delivering women. Major contribution to overall caesarean section is made by previous CS.
An algorithm for identifying regions of a DNA sequence that satisfy a content requirement

We present a dynamic programming algorithm for identifying regions of a DNA sequence that meet a user-specified compositional requirement. Applications of the algorithm include finding C + G-rich regions, locating TA + CG-deficient regions, identifying CpG islands, and finding regions rich in periodical three-base patterns. The algorithm has an advantage over the simple window method in that the algorithm shows the exact location of each identified region. The algorithm has been implemented as a portable C program called LCP (Local Content Program). LCP is extremely efficient in computer time and memory; it instantly locates all regions of a long DNA sequence meeting a given requirement. The LCP program was used to analyze the rabbit alpha-like globin gene cluster sequence.
User daily activity pattern learning: A multi-memory modeling approach

In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings and its performance is discussed in details.