Miracle-dz/UniFAR-FTUs huggingface dataset:
[PCR-RFLP and RAPD for typing neotropical Leishmania].

INTRODUCTION The analysis of the PCR-restriction fragment length polymorphism and random amplified polymorphic DNA have been useful tools for Leishmania identification. OBJECTIVES Molecular procedures were demonstrated for identification and typing of reference strains of New World Leishmania and their applicability was validated for clinical samples. MATERIALS AND METHODS DNA was extracted from 16 reference strains of Latin American Leishmania as well as from clinical samples of leishmaniasis patients. A sequence coding for cysteine proteinase B was amplified by PCR and subjected to restriction fragment length polymorphism analysis. The enzyme used was Taq1. For eight of the reference strains, the random amplified polymorphic desoxyribonucleic acid technique (RAPD) was applied. Band patterns for Leishmania species differentiation were established each each method. The sample size of the clinical sample was of 5. RESULTS PCR products of the cysteine proteinase B gene were obtained for L. braziliensis, L. peruviana, L. panamensis and L. guyanensis. For the other species, L. mexicana, L. amazonensis, L. garnhami, L. lainsoni, L. chagasi, L. naiffi, no amplification occurred. The patterns of restriction fragments revealed band patterns in common for L. peruviana, L. guyanensis and L. panamensis, whereas L. braziliensis had a distinctive pattern. When human samples were examined, amplification occurred for all cases, and the profiles corresponded to the common profile of L. peruviana, L. guyanensis and L. panamensis. The RAPD technique demonstrated reproducible and distinctive patterns for each of the 8 reference strains, L. mexicana, L. amazonensis, L. garnhami, L. lainsoni, L. chagasi, L. naiffi, making possible to differentiate all them. The advantages and limitations of each procedure are discussed. CONCLUSIONS The combination of RFP and RAPD methodologies provide useful tools to identify medical important species of Leishmania by recognizing DNA sequences characteristic of each species.
Attachment in Adolescence:

A fundamental question in the study of attachment is the explanation for differences in quality of attachment. In the present study, the Social Relations Model (SRM) was used to examine whether these differences in quality of attachment could be explained by (a) characteristics of the person (actor effect), (b) characteristics of the attachment figure (partner effect), (c) characteristics of the specific attachment relationship (relationship effect), or (d) characteristics of the family (family effect). In 288 families, two adolescent children and their parents reported on their attachment relationships with other family members. Results of SRM analyses showed that, in general, characteristics of the person reporting the attachment relationship and characteristics of the attachment relationship are the most important explanations for differences in quality of attachment, but the magnitudeof these effects tendsto vary depending on which family relationshipis considered.
Enhancing commercial grammar-based applications using robust approaches to speech understanding

This paper presents a series of measurements of the accuracy of speech understanding when grammar-based or robust approaches are used. The robust approaches considered here are based on statistical language models (SLMs) with the interpretation being carried out by phrase-spotting or robust parsing methods. We propose a simple process to leverage existing grammars and logged utterances to upgrade grammar-based applications to become more robust to out-of-coverage inputs. All experiments herein are run on data collected from deployed directed dialog applications and show that SLM-based techniques outperform grammar-based ones without requiring any change in the application logic.
Subcortical connectivity in dementia with Lewy bodies and Alzheimer's disease.

BACKGROUND Resting-state functional magnetic resonance imaging (fMRI) can be used to measure correlations in spontaneous low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signal which represent functional connectivity between key brain areas. AIMS To investigate functional connectivity with regions hypothesised to be differentially affected in dementia with Lewy bodies (DLB) compared with Alzheimer's disease and controls. METHOD Fifteen participants with probable DLB, 16 with probable Alzheimer's disease and 16 controls were scanned in the resting-state using a 3T scanner. The BOLD signal time-series of fluctuations in seed regions were correlated with all other voxels to measure functional connectivity. RESULTS Participants with DLB and Alzheimer's disease showed greater caudate and thalamic connectivity compared with controls. Those with DLB showed greater putamen connectivity compared with those with Alzheimer's disease and the controls. No regions showed less connectivity in DLB or Alzheimer's disease v. controls, or in DLB v. Alzheimer's disease. CONCLUSIONS Altered connectivity in DLB and Alzheimer's disease provides new insights into the neurobiology of these disorders and may aid in earlier diagnosis.
Improving focus targeting in interactive fisheye views

Fisheye views allow people to see both a focus region and the surrounding context in the same window. However, the magnification effects of the fisheye lens can cause several problems for users. One of these is focus-targeting, where a user moves the focus to a new location. Magnification makes focus-targeting difficult because objects appear to move as the focus point approaches them. This paper examines how the distortion of a fisheye view affects focus-targeting performance, and present a technique called speed-coupled flattening (SCF) as a way to improve focus targeting in distortion-oriented views. SCF dynamically reduces the distortion level of a fisheye based on pointer velocity and acceleration. In an experiment, the technique resulted in significant reductions in both targeting time and targeting errors. By adjusting distortion based on the user's activity, we can improve usability without requiring the user to manipulate any additional view controls
Decrease in Muscle Contraction Time Complements Neural Maturation in the Development of Dynamic Manipulation

Developmental improvements in dynamic manipulation abilities are typically attributed to neural maturation, such as myelination of corticospinal pathways, neuronal pruning, and synaptogenesis. However the contributions from changes in the peripheral motor system are less well understood. Here we investigated whether there are developmental changes in muscle activation-contraction dynamics and whether these changes contribute to improvements in dynamic manipulation in humans. We compared pinch strength, dynamic manipulation ability, and contraction time of the first dorsal interosseous muscle in typically developing preadolescent, adolescent, and young adults. Both strength and dynamic manipulation ability increased with age (p < 0.0001 and p < 0.00001, respectively). Surprisingly, adults had a 33% lower muscle contraction time compared with preadolescents (p < 0.01), and contraction time showed a significant (p < 0.005) association with dynamic manipulation abilities. Whereas decreases in muscle contraction time during development have been reported in the animal literature, our finding, to our knowledge, is the first report of this phenomenon in humans and the first finding of its association with manipulation. Consequently, the changes in the muscle contractile properties could be an important complement to neural maturation in the development of dynamic manipulation. These findings have important implications for understanding central and peripheral contributors to deficits in manipulation in atypical development, such as in children with cerebral palsy.
ShareBook: An Application of Cross-Platform E-Book Viewer with Vector Graphic

Many handheld devices have been developed in the mobile industry. Most mobile devices in the market are carried Android operating system. Android also supports many other hardware components that increase mobile devices functionality. The operating system can be used for multimedia, touch screen, number of types of sensors and 2D/3D graphics. Due to open source license, many manufactures integrate their own production to Android system. Hence, this paper proposed a ShareBook software based on Android system. ShareBook integrates new types of sensor such as touch sensor, bending sensor and gyroscope to develop a flexible display platform with Android operating system. These sensors change user’s operation in general smart mobile devices with Android. Flexible displays bring great difference in interaction behavior between system software and end user. However, there are some other problems in traditional E-Book application. Size of screen on mobile devices is the first problem. Hardware manufactures produce 4-5 inches display on their mobile phone production, the display size is at most 10.1 inches on pad production. Some people hardly read contents on small size screen. So, ShareBook can easily transfer e-book viewer to larger size display such as TV or LCD display when user can reach the larger size display in their environment. Second problem is application’s performance efficiency. ShareBook integrates vector graphic library into Android and optimizes the performance of vector graphic library by Renderscript and GPGPU. ShareBook also combines cloud platform resource for image file transformation and storage resource extension.
Capital Market Conditions and the Financial and Real Implications of Cash Holdings

We investigate the financial and real implications of corporate cash holdings over different capital market conditions in the period 1972-2008. A recent theoretical work implies that the value of corporate liquidity varies with the supply of external capital and should be higher during market-wide liquidity shocks. We find that for an average firm a marginal dollar in cash holdings has the same value in high and low liquidity periods. However, the additional dollar of cash is more valuable for research-intensive firms, which are more likely to be shut out of capital markets during financial crises. We also study the real implications of cash holdings across the market-wide liquidity cycle. Our goal is to study whether and how cash reserves affect a firm‟s performance and investment relative to its industry rivals in regular and adverse market conditions. Our results indicate that having more cash relative to industry rivals allows a firm to achieve a higher rate of sales growth and spend more on capital and inventory expenditures. However, we do not find that larger cash reserves relative to industry rivals lead to more investment and better performance when capital market liquidity is low. Overall, we conclude that in a market downturn large holdings of cash do not give a firm a strategic advantage over its industry rivals.
A differentiation / enhancement edge detector and its properties

A differentiation/enhancement edge detector for noisy situations is proposed. The definition of the gradient has been initiated from the algorithm for finding a border in a binary picture. The advantages of this operator have been compared with those of other widely used operators. A measurement of error in extracting edges by thresholding the gradient has also been suggested, and for the detection of acceptable edges, the optimum threshold is chosen corresponding to the minima in the error function.
Comprehension Monitoring, memory, and study strategies of Good and Poor Readers

Comprehension and memory skills of fourth grade good and poor readers were compared in two studies. Their ability to monitor comprehension of difficult and anomalous information was measured in three ways; by spontaneous self-corrections during oral reading, by directed underlining of incomprehensible words and phrases, and by study behaviors. Poor readers engaged in significantly less monitoring on all three measures and this was correlated with poorer comprehension and recall scores. An additional metacognitive measure of perceived reading strategy effectiveness indicated that poor readers are often unaware of the negative influences of some strategies. The patterns of responses on the multiple measures suggest that poor readers may adopt decoding rather than meaning comprehension goals during reading and they are less accurate in applying monitoring skills towards resolving comprehension failures.
Tracking-by-synthesis using point features and pyramidal blurring

Tracking-by-synthesis is a promising method for markerless vision-based camera tracking, particularly suitable for Augmented Reality applications. In particular, it is drift-free, viewpoint invariant and easy-to-combine with physical sensors such as GPS and inertial sensors. While edge features have been used succesfully within the tracking-by-synthesis framework, point features have, to our knowledge, still never been used. We believe that this is due to the fact that real-time corner detectors are generally weakly repeatable between a camera image and a rendered texture. In this paper, we compare the repeatability of commonly used FAST, Harris and SURF interest point detectors across view synthesis. We show that adding depth blur to the rendered texture can drastically improve the repeatability of FAST and Harris corner detectors (up to 100% in our experiments), which can be very helpful, e.g., to make tracking-by-synthesis running on mobile phones. We propose a method for simulating depth blur on the rendered images using a pre-calibrated depth response curve. In order to fulfil the performance requirements, a pyramidal approach is used based on the well-known MIP mapping technique. We also propose an original method for calibrating the depth response curve, which is suitable for any kind of focus lenses and comes for free in terms of programming effort, once the tracking-by-synthesis algorithm has been implemented.
So You Think You Are a Multitasker?: A Qualitative Study to Understand Multitasking

In the present day, the need and ability to multitask in or across devices is growing. However, these multitasking abilities are being introduced to devices without considering the effect they have on the cognitive workload of a user and its impact on one's productivity. Through our study described in this paper, we attempt to understand multitasking from a qualitative perspective by exploring the difference in multitasking behaviour of users when they deal with active and passive tasks. We also identify workarounds devised by users in order to minimize their cognitive workload while multitasking across apps on their mobile devices. Finally, we suggest certain guidelines that could be adopted by technology companies to aid users in effectively handling interruptions and switching across applications.
Early Intervention Programs for Children With Behavior Problems and At Risk for Developing Antisocial Behaviors: Evidence- and Research-Based Practices

The number of children who would be eligible for, and benefit from, early intervention programs has dramatically increased during the past several years. However, those programs would have to meet the U.S. Department of Education’s criteria for being evidence based. Some programs that have proven successful would not be eligible but rather be considered data based by educational researchers. The purpose of this article is to review the major early intervention programs for children at risk for developing conduct problems and to determine which ones could be considered evidence based, research based, or neither. Issues related to determining whether a program is evidence based versus data based are presented, along with those addressing program efficacy and access for eligible children.
Application Specific Low Leakage data Cache for embedded processors

Previous studies have suggested using drowsy caches to reduce leakage power in caches. Such studies often move an entire cache line in and out of the drowsy mode to reduce leakage power while maintaining performance. In this work we extend previous work and introduce Application Specific Low Leakage Cache (ASL) as an alternative power-aware data cache for embedded processors. ASL builds on the observation that often only one or two words of a cache line are accessed during long periods. Accordingly, we investigate a word-size granularity approach to drowsy caches. We introduce two ASL variations, i.e., B-ASL and P-ASL. In B-ASL we move all words in a cache line into the drowsy (low leakage) mode and wakeup only the words accessed. In P-ASL we make sure recently accessed words stay in the non-drowsy (high leakage) mode to maintain performance. We show that ASL can reduce leakage power in caches by 88% while paying an average performance cost of 0.7% compared to drowsy cache.
Learning to Count by Think Aloud Imitation

Although necessary, learning to discover new solutions is often long and difficult, even for supposedly simple tasks such as counting. On the other hand, learning by imitation provides a simple way to acquire knowledge by watching other agents do. In order to learn more complex tasks by imitation than mere sequences of actions, a Think Aloud protocol is introduced, with a new neuro-symbolic network. The latter uses time in the same way as in a Time Delay Neural Network, and is added basic first order logic capacities. Tested on a benchmark counting task, learning is very fast, generalization is accurate, whereas there is no initial bias toward counting.
Neural Machine Translation with Word Predictions

In the encoder-decoder architecture for neural machine translation (NMT), the hidden states of the recurrent structures in the encoder and decoder carry the crucial information about the sentence. These vectors are generated by parameters which are updated by back-propagation of translation errors through time.We argue that propagating errors through the end-to-end recurrent structures are not a direct way of control the hidden vectors. In this paper, we propose to use word predictions as a mechanism for direct supervision. More specifically, we require these vectors to be able to predict the vocabulary in target sentence. Our simple mechanism ensures better representations in the encoder and decoder without using any extra data or annotation. It is also helpful in reducing the target side vocabulary and improving the decoding efficiency. Experiments on Chinese-English machine translation task show an average BLEU improvement by 4.53, respectively.
Between a rock and a (very) hard place

Conversations with teachers and professional development leaders enable us, as researchers, to highlight the ambiguous promise of action research within the context of mandated teacher professional development in the province of Alberta, Canada. From this departure, we investigate how educators believe action research projects influence their professional practices and we explore the question of the validity of using state-mandated action research projects as a means of bringing about authentic teacher professional development. Using conversations, we recount the experiences of three teachers who took part in two separate action research projects. As the conversations we present underscore, many teachers involved in state-mandated school-based projects found themselves caught between competing discourses of personal empowerment and individual autonomy on the one hand and externally driven measures of accountability and excellence on the other. In this complex and ambiguous location and within the context of their involvement in action research projects, the three teachers in this study negotiated their own understandings of professional development.
Dimensionality reduction for long duration and complex spatio-temporal queries

In this paper we present an approach to mine and query spatio-temporal data with the aim of finding interesting patterns and understanding the underlying data generating process. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a certain pre-defined time. One approach to process a "flock query" is to map spatio-temporal data into a high dimensional space and reduce the query into a sequence of standard range queries which can be presented using a spatial indexing structure. However, as is well known, the performance of spatial indexing structures drastically deteriorates in high dimensional space. In this paper we propose a preprocessing strategy which consists of using a random projection to reduce the dimensionality of the transformed space. Our experimental results show, for the first time, the possibility of breaking the curse of dimensionality in a spatio-temporal setting.
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System

Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfal.
Richness Dependence of the Recent Evolution of Clusters of Galaxies

We revisit the issue of the recent dynamical evolution of clusters of galaxies using a sample of Abell, Corwin & Olowin (ACO) clusters with z < 0.14, which has been selected such that it does not contain clusters with multiple velocity components nor strongly merging or interacting clusters, as revealed in X-rays. We use as proxies of the cluster dynamical state the projected cluster ellipticity, velocity dispersion and X-ray luminosity. We find indications for a recent dynamical evolution of this cluster population, which however strongly depends on the cluster richness. Poor clusters appear to be undergoing their primary phase of virialization, with their ellipticity increasing with redshift with a rate de/dz ≃ 2.5 ± 0.4, while the richest clusters show an ellipticity evolution in the opposite direction (with de/d z ≃ -1.2 ± 0.1), which could be due to secondary infall. When taking into account sampling effects due to the magnitude-limited nature of the ACO cluster catalogue we find no significant evolution of the cluster X-ray luminosity, while the velocity dispersion increases with decreasing redshift, independent of the cluster richness, at a rate dσ v /d z ≃ -1700 ± 400 km s -1 .