Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Healthy Lifestyle Mobile App for Overweight Pregnant Women: Qualitative Study

Background Mobile apps are becoming an increasingly ubiquitous platform for delivery of health behavior interventions among overweight and obese perinatal women. However, only a few methodological guidelines on integrating theory, evidence, and qualitative research for their designs are available. Objective The aim of this study was to develop a theory-based, evidence-driven, and user-centered healthy lifestyle app targeting overweight and obese multiethnic pregnant women. Methods This paper illustrates how intervention development may be enriched with theoretical basis, systematic review, and qualitative study. An individual face-to-face interview was performed to incorporate the user’s involvement in the design. These interviews were audiotaped and transcribed. Thematic analysis technique was used for emerging themes. Results Integrated concepts of social cognitive theory of self-regulation, self-regulation model, and strength model of self-control were selected as bases of the intervention. Evidence from our systematic review and meta-analysis provided the strongest evidence for the development of intervention. We invited 16 obese or overweight pregnant women to participate in a semistructured interview . The following key themes emerged: content, platform, interactivity, format, and functionality. Apps are a favorable technology platform for healthy diet advice, appropriate physical exercise, and weight management because they are user-friendly and convenient. The app used in this study contains culture-specific, pregnancy-related, and credible contents, including educational, professional and peer support, and self-monitoring domains. The design should include aesthetic appeal, visualized features, and interactive multimedia. Conclusions A 3-step process integrating theoretical basis, evidence from systematic review, and research findings from target users can be considered a guide for future app development.
The Relationship Between Inclusive Education Teachers'Vocational Adaptation and Social Support

This study,by using the self-devised questionnaire on inclusive education teachers' vocational adaptation and the revised scale for the social support of primary school teachers to survey 431 inclusive education teachers in primary schools,aims to explore the relationship between inclusive education teachers' vocational adaptation and the social support of them.The study concludes as follows: the inclusive education teachers gaining different social support show a significant difference in their vocational adaptation;on the whole,the principal's support,the colleagues' support,emotional support and information support play a significant role in predicting the inclusive education teachers' vocational adaptation,compared with the family support,parents' support,and instrumental support.Consequently,the study suggests the following: intensifying school leaders' support for inclusive education;creating more platforms for inclusive education teachers' communication;and improving inclusive education teachers' preparation and training to raise their ability in vocational adaptation.
A locally adaptive process-convolution model for estimating the health impact of air pollution

Most epidemiological air pollution studies focus on severe outcomes such as hospitalisations or deaths, but this underestimates the impact of air pollution by ignoring ill health treated in primary care. This paper quantifies the impact of air pollution on the rates of respiratory medication prescribed in primary care in Scotland, which is a proxy measure for the prevalence of less severe respiratory disease. A novel bivariate spatiotemporal process-convolution model is proposed, which: (i) has increased computational efficiency via a tapering function based on nearest neighbourhoods; and (ii) has locally adaptive weights that outperform traditional distance-decay kernels. The results show significant effects of particulate matter on respiratory prescription rates which are consistent with severe endpoint studies.
Drought will not leave your glass empty: Low risk of hydraulic failure revealed by long-term drought observations in world’s top wine regions

Long-term observations in Napa Valley and Bordeaux reveal that grapevines never reach a lethal level of drought. Grapevines are crops of global economic importance that will face increasing drought stress because many varieties are described as highly sensitive to hydraulic failure as frequency and intensity of summer drought increase. We developed and used novel approaches to define water stress thresholds for preventing hydraulic failure, which were compared to the drought stress experienced over a decade in two of the world’s top wine regions, Napa and Bordeaux. We identified the physiological thresholds for drought-induced mortality in stems and leaves and found small intervarietal differences. Long-term observations in Napa and Bordeaux revealed that grapevines never reach their lethal water-potential thresholds under seasonal droughts, owing to a vulnerability segmentation promoting petiole embolism and leaf mortality. Our findings will aid farmers in reducing water use without risking grapevine hydraulic integrity.
Conceptualizing American Attitudes toward Immigrants’ Dual Loyalty

The social issue of immigrants’ dual loyalty figures prominently in the 2016 U.S. presidential primaries, yet little is known about Americans’ views on the subject. Drawing on data from a nationally representative telephone survey, the authors specifically explored nonimmigrant Americans’ attitudes toward immigrants’ dual loyalty. The results show that attitudes toward this dual loyalty are informed by multiple boundary-making processes, including the extent to which respondents strongly believe that immigrants should celebrate American values and traditions and share their vision of America, that immigration should be restricted as much as possible, and that American influence in the world is important.
Multi-party Conversation Summarization Based on Sentence Selection Using Verbal and Nonverbal Information

In this paper, we propose a method for conversation summarization. For the method, we combine two approaches, a scoring method and a machine learning technique (SVMs). First we compare important utterance extraction by the scoring method and SVMs. In the machine learning technique, we introduce verbal features, such as relations between utterances and anaphora features, and nonverbal features. Next we generate a summary from the outputs of the scoring method and SVMs. In our approach, a basic summary consists of utterances with high confidence extracted from the scoring method. Utterances from SVMs are used as supplementary information. In the experiment, we compare a combination method and a method with only SVMs. The output of our method was suitable in terms of readability and correctness as a summary of original conversation.
Of Mice, Men, and Trolleys: Hypothetical Judgment Versus Real-Life Behavior in Trolley-Style Moral Dilemmas

Scholars have been using hypothetical dilemmas to investigate moral decision making for decades. However, whether people’s responses to these dilemmas truly reflect the decisions they would make in real life is unclear. In the current study, participants had to make the real-life decision to administer an electroshock (that they did not know was bogus) to a single mouse or allow five other mice to receive the shock. Our results indicate that responses to hypothetical dilemmas are not predictive of real-life dilemma behavior, but they are predictive of affective and cognitive aspects of the real-life decision. Furthermore, participants were twice as likely to refrain from shocking the single mouse when confronted with a hypothetical versus the real version of the dilemma. We argue that hypothetical-dilemma research, while valuable for understanding moral cognition, has little predictive value for actual behavior and that future studies should investigate actual moral behavior along with the hypothetical scenarios dominating the field.
Renal inflammation: targeted iron oxide nanoparticles for molecular MR imaging in mice.

PURPOSE To determine the feasibility of T2-weighted magnetic resonance (MR) imaging in the noninvasive quantification of renal inflammation by using superparamagnetic iron oxide (SPIO) nanoparticles targeted to tissue-bound C3 activation fragments in a mouse model of lupus nephritis. MATERIALS AND METHODS All animal procedures were approved by the University of Colorado-Denver animal care and use committee. SPIO nanoparticles were encapsulated by using amine-functionalized phospholipids. A recombinant protein containing the C3d-binding region of complement receptor type 2 (CR2) was then conjugated to the surface of the SPIO nanoparticle. Five MRL/lpr mice (a model of lupus nephritis) and six C57BL/6 wild-type mice were assessed with T2-weighted MR imaging at baseline and after SPIO injection. The same five MRL/lpr mice and three C57BL/6 mice also underwent MR imaging after injection of CR2-targeted SPIO. A series of T2-weighted pulses with 16 echo times was used to enable precise T2 mapping and calculation of T2 relaxation times in the cortex and outer and inner medulla of the kidneys, as well as in the spleen, muscle, and fat. The effects of treatment and animal genotype on T2 relaxation times were analyzed with repeated-measures analysis of variance. RESULTS At baseline, the T2-weighted signal intensity in the kidneys of MRL/lpr mice was higher than that in the kidneys of wild-type mice. Injection of untargeted SPIO did not alter the T2-weighted signal in the kidneys in either strain of mice. Injection of CR2-targeted SPIO in MRL/lpr mice, however, caused a significant accumulation of targeted iron oxide with a subsequent decrease in T2 relaxation times in the cortex and outer and inner medulla of the kidneys. No changes in T2 relaxation time were observed in the wild-type mice after injection of targeted SPIO. CONCLUSION Injection of CR2-conjugated SPIO caused a significant reduction in T2-weighted MR imaging signal and T2 relaxation time in nephritic kidneys.
InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs

Abstract Computational protein docking is a powerful strategy to predict structures of protein-protein interactions and provides crucial insights for the functional characterization of macromolecular cross-talks. We previously developed InterEvDock, a server for ab initio protein docking based on rigid-body sampling followed by consensus scoring using physics-based and statistical potentials, including the InterEvScore function specifically developed to incorporate co-evolutionary information in docking. InterEvDock2 is a major evolution of InterEvDock which allows users to submit input sequences – not only structures – and multimeric inputs and to specify constraints for the pairwise docking process based on previous knowledge about the interaction. For this purpose, we added modules in InterEvDock2 for automatic template search and comparative modeling of the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database. InterEvDock2 identified a correct model among the top 10 consensus in 29% of these cases (compared to 15–24% for individual scoring functions) and at least one correct interface residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique protein docking server, designed to be useful for the experimental biology community. The InterEvDock2 web interface is available at http://bioserv.rpbs. univ-paris-diderot.fr/services/InterEvDock2/.
The Idea of Using Practice in Mind Training Program for Rugby Players to Improve Anxiety and Kicking Performance

PIM training program is one of the imagery interventions employed by athletes to improve golf skills performance. This study attempted to measure the effects of Practice in Mind (PIM) training program on anxiety level and the right side of 22 meter place kick performance of rugby players. This study was a double blind experimental design research. Fifty-eight university male rugby players between the age range of 18 to 25 years old (M = 21.43, SD = 2.04) participated in this study. Participants were divided into two groups which is the intervention group (PIM group) and the control group (only physical practice). In PIM group, participants were instructed to do imagery - physical practice, meanwhile, the control group only performed physical practice for three times per week. The Mixed Between - Within Subjects found that PIM group significantly performed better as compared to control group in place kick performance, self-confidence and had higher anxiety level tolerance. Future research can be conducted to determine the effectiveness of PIM training program on skilled players and different genders or sports that have varying cognitive complexity. Research still needs to be conducted particularly in the mediating role of self-efficacy, moods, and arousal of PIM training program.
Modeling of the dynamics of a resonant wireless power transfer circuit

This paper deals with the modeling of the voltage and current dynamics in a resonant Wireless Power Transfer (WPT) circuit in the view of their control. The high-frequency operation of the circuit calls for the selection of appropriate quantities to be considered and for the development of specific methods to be used for describing their dynamics. In the paper, three methods developed for this purpose are presented and discussed. They are the Generalized State-Space Averaging (GSSA), Laplace Phasor Transform (LPT) and Modulated Variable Laplace Transform (MVLT) methods, where the latter one is here proposed. Afterwards, the receiving circuit of a series-resonant WPT charger of an electric vehicle is picked up as a study case, and the model of its dynamics is worked out by the three methods. At last, the accuracy of the resulting models is evaluated by checking their step response against that of the study case as obtained by simulation.
CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases

Abstract CircR2Disease is a manually curated database, which provides a comprehensive resource for circRNA deregulation in various diseases. Increasing evidences have shown that circRNAs play critical roles in transcriptional, post-transcriptional and translational regulation. Therefore, the aberrant expression of circRNAs has been associated with a group of diseases. It is significant to develop a high-quality database to deposit the deregulated circRNAs in diseases. The current version of CircR2Disease contains 725 associations between 661 circRNAs and 100 diseases by reviewing existing literatures. Each entry in the CircR2Disease contains detailed information for the circRNA–disease relationship, including circRNA name, coordinates and gene symbol, disease name, expression patterns of circRNA, experimental techniques, a brief description of the circRNA–disease relationship, year of publication and the PubMed ID. CircR2Disease provides a user-friendly interface to browse, search and download as well as to submit novel disease-related circRNAs. CircR2Disease could be very beneficial for researches to investigate the mechanism of disease-related circRNAs and explore the appropriate algorithms for predicting novel associations. Database URL: http://bioinfo.snnu. edu.cn/CircR2Disease/
Effect of Selective Encapsulation of Hydroxypropyl-β-cyclodextrin on Components and Antibacterial Properties of Star Anise Essential Oil

Star anise essential oil (SAEO) is a plant essential oil with good antibacterial activity, but its applications are limited due to its high volatility, strong smell, and unstable physical and chemical properties. The effect of selective encapsulation of SAEO by hydroxypropyl-β-cyclodextrin (HPCD) on its compositions, volatility stability and antibacterial activity was investigated. The GC-MS results indicated that the compositions reduced and content of the compositions of SAEO changed after encapsulation. Most of the components in SAEO were successfully encapsulated by HPCD, which can be supported by data from FTIR and 1H NMR. According to the molecular modeling results, the three guest molecules (trans-anethole, estragole and trans-foeniculin) were all docked in the cavity of HPCD on the isoallyl (or allyl) side. The volatile stability of SAEO before and after encapsulation was evaluated by electronic nose, and the results confirmed that encapsulation significantly reduced the irritating smell of SAEO and makes the clathrate have a sustained release effect. Furthermore, in the antibacterial test, the selective encapsulation of HPCD improved the inhibition effect of SAEO on Rhizopus stolonoifer, Saccharomyces cerevisiae, and E. coli and its antibacterial stability in 24 h.
A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase

In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), i.e. a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity.
Dynamic Deployment and Cost-Sensitive Provisioning for Elastic Mobile Cloud Services

As mobile customers gradually occupying the largest share of cloud service users, the effective and cost-sensitive provisioning of mobile cloud services quickly becomes a main theme in cloud computing. The key issues involved are much more than just enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection problems of mobile environments have intrinsic conflict with the continuous connection assumption of the cloud service usage patterns. We advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all available resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on data center, base stations, client units, even peer devices. The best deployment location is dynamically determined based on a context-aware and cost-sensitive evaluation model. To facilitate easy adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can easily convert a service for execution on different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with very low overhead.
Visual Global Localization with a Hybrid WNN-CNN Approach

Currently, self-driving cars rely greatly on the Global Positioning System (GPS) infrastructure, albeit there is an increasing demand for alternative methods for GPS-denied environments. One of them is known as place recognition, which associates images of places with their corresponding positions. We previously proposed systems based on Weightless Neural Networks (WNN) to address this problem as a classification task. This encompasses solely one part of the global localization, which is not precise enough for driverless cars. Instead of just recognizing past places and outputting their poses, it is desired that a global localization system estimates the pose of current place images. In this paper, we propose to tackle this problem as follows. Firstly, given a live image, the place recognition system returns the most similar image and its pose. Then, given live and recollected images, a visual localization system outputs the relative camera pose represented by those images. To estimate the relative camera pose between the recollected and the current images, a Convolutional Neural Network (CNN) is trained with the two images as input and a relative pose vector as output. Together, these systems solve the global localization problem using the topological and metric information to approximate the current vehicle pose. The full approach is compared to a Real- Time Kinematic GPS system and a Simultaneous Localization and Mapping (SLAM) system. Experimental results show that the proposed approach correctly localizes a vehicle 90% of the time with a mean error of 1.20m compared to 1.12m of the SLAM system and 0.37m of the GPS, 89% of the time.
Investigating blended learning in the high school science classroom

Blended learning combines the best practices of online learning with face-to-face learning and some research has shown it to have positive benefits for students at the postsecondary level. However, few studies have reported the use of blended learning in the high school setting. This study used quantitative methods to measure student attitudes and learning of science content in both a treatment and control group consisting of 9th grade Physical Science classes. Students in the treatment group experienced one semester of blended learning by using online science modules to supplement their in-class learning while the control group continued to have only face-to-face instruction. The findings show no significant change in student attitudes about science and also no significant difference between the groups on a posttest measuring science knowledge. However, the treatment group exposed to the blended learning approach did show significant growth in science content knowledge from pretest to posttest while the growth by the control group was not significant. Students in the treatment group were also interviewed to gather their opinions of the blended learning experience. Responses show students were engaged by the online simulations and self-paced content but participants also suggested ways to make the blended learning experience more beneficial for student learning. These implications for instruction and the design of blended learning are discussed. INVESTIGATING BLENDED LEARNING IN THE HIGH SCHOOL SCIENCE CLASSROOM A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Master of Arts in Science Education Holly Cassandra Hinkhouse University of Northern Iowa August, 2013