Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors associated with refusal of childhood vaccines among parents of school-aged children: a case-control study.

BACKGROUND The rate of nonmedical exemptions to school immunization requirements has been increasing, and children with exemptions have contributed to outbreaks of vaccine-preventable diseases. OBJECTIVES To determine why parents claim nonmedical exemptions and to explore differences in perceptions of vaccines and vaccine information sources between parents of exempt and fully vaccinated children. DESIGN Case-control study. SETTING Colorado, Massachusetts, Missouri, and Washington. PARTICIPANTS Surveys were mailed to the parents of 815 exempt children (cases) and 1630 fully vaccinated children (controls randomly selected from the same grade and school) recruited from 112 private and public elementary schools. Surveys were completed by 2435 parents (56.1%). MAIN OUTCOME MEASURES Parental reports. RESULTS Most children (209 [75.5%] of 277) with nonmedical exemptions received at least some vaccines. The most common vaccine not received was varicella (147 [53.1%] of 277 exempt children). The most common reason stated for requesting exemptions (190 [69%] of 277) was concern that the vaccines might cause harm. Parents of exempt children were significantly more likely than parents of vaccinated children to report low perceived vaccine safety and efficacy, a low level of trust in the government, and low perceived susceptibility to and severity of vaccine-preventable diseases. Parents of exempt children were significantly less likely to report confidence in medical, public health, and government sources for vaccine information and were more likely to report confidence in alternative medicine professionals than parents of vaccinated children. CONCLUSION Continued efforts must be made to educate parents about the utility and safety of vaccines, especially parents requesting nonmedical exemptions to school immunization requirements.
Deep Convolutional Neural Network Design Patterns

Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet). Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures. In addition, we describe several architectural innovations, including Fractal of FractalNet network, Stagewise Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt files are available at https://github.com/iPhysicist/CNNDesignPatterns). We hope others are inspired to build on our preliminary work.
The Effect of Special Teaching Methods Class on the Level of Teachers' Self-Efficacy Perception of Pre-Service Teacher.

The purpose of this study is to evaluate the change in the level of teachers’ self-efficacy perception of primary school Social Studies pre-service teachers who take the special teaching methods class. Single group pretest-posttest model from pre-experimental patterns was applied in the research. The study group of the research consisted of 59 students in third year in the spring semester of 2012-2013 Academic Year in Primary School Social Studies at Buca Education Faculty, Dokuz Eylul University. These students are those who attend the special teaching methods class in a 14 week period between February and June. Teacher Self-Efficacy Scale, prepared by Acikgoz and Gokdag, was developed to determine the self-efficacy of teachers to collect research data. Validity and reliability study was repeated by the scale researcher in 2012-2013 teaching year and applied on 652 pre-service teachers. Cronbach Alpha Reliability Coefficient is calculated for the general scale was . 85. Teacher Self-Efficacy Scale, a 5 point Likert scale consisting 29 items is calculated as follows: teaching efficacy subscale coefficient of 0.94, teacher impact subscale of 0.69, student impact subscale of 0.60. The scale was applied as pretest and posttest on pre-service teachers who attended the 14-week special teaching class. The findings of research revealed that the primary school social studies pre-service teachers scored over the average in their general self-efficacy pretest, also, special teaching methods class did not lead to a significant difference on their general teacher self-efficacy perception and there was a significant difference only in student impact sub-scale basis. Key words: Teaching vocational classes, teacher self-efficacy perception, pre-service teacher.
Stigma and disability in schizophrenia: Developing countries’ perspective

Abstract Background: Stigma and disability are two important consequences of schizophrenia that individuals afflicted with it experience. Sociocultural milieu can influence these. We review the literature on stigma and disability experienced by individuals with schizophrenia in the developing countries. Method: We searched English-language literature from developing countries on stigma and disability in schizophrenia using PubMed and Scopus databases. As individual studies adopted widely varying methodologies, the retrieved papers did not yield themselves for a systematic review. We present a narrative review. Results: Much of the literature on stigma and disability in schizophrenia has come from India and only a few other developing countries. Stigma associated with schizophrenia is highly prevalent across regions and across patients themselves, families, communities and professionals. Research is scanty with regard to determinants of stigma and interventions against stigma. A number of tools have been developed for assessment of disability. Preliminary evidence suggests that initiation and continuation of antipsychotic medications is associated with lesser disability. Psychosocial interventions may reduce disability further. Conclusions: Comprehensive, prospective studies evaluating the determinants of stigma and disability need to be conducted in the developing countries. Models of interventions to minimize these adverse consequences, developed based on their results, need to be tested.
Brainstem Cavernomas: Long-term Results of Microsurgical Resection in 52 Patients

OBJECTIVE: To review the natural history and the long-term results of microsurgical resection of brainstem cavernous angiomas operated on in one institution. METHODS: A retrospective analysis was conducted of the preoperative and postoperative course in 52 consecutive patients who underwent microsurgical resection of a brainstem cavernoma between 1990 and 2002. The role of sex, age, cavernoma location, size, multiple bleedings, relationships to the pial-ependymal surface, surgical approach, and preoperative magnetic resonance imaging appearance were evaluated as prognostic factors possibly influencing outcome. Discrete data were compared by use of the &khgr;2 test and Fisher’s exact test as appropriate. RESULTS: The risk of hemorrhage was 3.8% per patient per year. The rebleeding rate was 34.7%. Nineteen of 29 patients who experienced new neurological deficits after surgery improved over time to their preoperative condition or better. Permanent morbidity was observed in 10 (19%) of 52 patients (follow-up: 1.5–10.5 yr; mean, 4.7 yr; median, 4.3 yr; standard deviation, 0.2 yr). The final Karnofsky Performance Scale score for these 10 patients was 90 in 2 patients, 80 in 2, 70 in 2, 60 in 2, 50 in 1, and 30 in 1. The mortality rate was 1.9%. The incidence of permanent new neurological deficits was lower in the 20 patients whose lesion could be removed through an anterolateral pontine approach (5 versus 29%; P = 0.035). CONCLUSION: Surgical resection is recommended for superficial lesions and for lesions that can be reached through the anterolateral pontine surface. Surgery is also recommended for symptomatic cavernomas with a satellite subacute hematoma.
Running head: DISORIENTATION AND INCIDENTAL LEARNING IN WEB NAVIGATION Perceived Disorientation and Incidental Learning in a Web-based Environment: Internal and External Factors Baylor, A. L. (2001). Incidental Learning and Perceived Disorientation in a Web- based Environment: Internal and External 

In a four-factor MANOVA design, this exploratory study experimentally investigated the influence of navigation mode (linear, nonlinear), distracting links (presence, absence), sensationseeking tendency (high, low), and spatial-synthetic ability (high, low) on perceived disorientation and incidental learning (accuracy of main point, example generation) in web navigation. Seventy-five participants completed a search task to find the location of five sentences within a nine-page web-based passage. Regarding disorientation, a moderately large effect size indicated that those in the linear navigation mode were more disorientated than those in nonlinear navigation mode. Contrary to predictions, there were no effects on disorientation for spatialsynthetic ability and sensation seeking tendency. Regarding incidental learning, example generation was facilitated by sensation seeking tendency and the absence of distracting links; main point score was facilitated by spatial-synthetic ability. Further, a moderately large effect size indicated the role of the nonlinear navigation mode in facilitating higher main point and example generation scores. Disorientation was negatively correlated with both example generation and main point score.
Coordinating management activities in distributed software development projects

Coordinating distributed processes, especially engineering and software design processes, has been a research topic for some time now. Several approaches have been published that aim at coordinating large projects in general, and large software development processes specifically. However most of these approaches focus on the technical part of the design process and omit management activities like planning and scheduling the project, or monitoring it during execution. We focus on coordinating the management activities that accompany the technical software design process. We state the requirements for a Software Engineering Environment (SEE), accommodating management, and we describe a possible architecture for such an SEE.
Diffuse Cerebral Air Embolism Treated With Hyperbaric Oxygen: A Case Report

A 54‐year‐old woman presented for cardiac evaluation of atypical chest pain. Workup included coronary angiography and a left ventriculogram, during which air was inadvertently injected, resulting in the development of an acute right hemisphere syndrome. Right carotid angiography was immediately performed, yielding only a delayed diffuse venous phase without focal vessel cutoffs. Within 60 minutes, the patient underwent hyperbaric oxygen therapy for the suspected cerebral air emboli. After removal from the chamber for technical reasons, she had a generalized tonic‐clonic seizure, and further hyperbaric oxygen therapy was withheld. Initial computed tomography imaging obtained approximately 8 hours after symptom onset showed signs of early right hemispheric edema. Subsequent magnetic resonance imaging studies were markedly abnormal and suggestive of diffuse bilateral but predominantly right‐sided parietal lobe edema with mildly positive diffusion‐weighted imaging. Follow‐up magnetic resonance imaging at 6 months was normal, and the patient's neurological examination returned to normal.
Robust self-triggered model predictive control for constrained discrete-time LTI systems based on homothetic tubes

In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every sampling instant an optimization problem based on the current state of the system is solved in order to determine the input applied to the system until the next sampling instant, as well as the next sampling instant itself. This leads to inter-sampling times that depend on the trajectory of the system. By maximizing the inter-sampling time, the amount of communication in the control system is reduced. In order to guarantee robust constraint satisfaction, Tube MPC methods are employed. Specifically, in order to account for the uncertainty in the system, homothetic sets are used in the prediction of the future evolution of the system. The proposed controller is shown to stabilize a closed and bounded set including the origin in its interior.
Comparison of the Stress Distribution and Fatigue Behavior of 10- and 25- $\mu\hbox{m}$-Thick Deep-Reactive-Ion-Etched Si Kilohertz Resonators

The stress distribution and fatigue behavior of nominally identical kilohertz fatigue resonators with two different thicknesses, 10 and 25 μm, was compared in this study. The results highlight the non-uniform 3-D stress distribution of the micron-scale notched cantilever beams that depends on the thickness. The areas corresponding to the first principal stress being within 2% of the maximum value are much smaller than the overall notch area and are a function of device thickness. It is also shown that the non-negligible influence of small, nanometer-scale geometrical variations in the dimensions of nominally identical devices on the maximum stress values can be accounted for by measuring the device's resonant frequency (f0). The observed scatter in the fatigue results of these microresonators is in part associated with the challenge in accurately calculating the local stress amplitudes. Despite that large scatter, the fatigue behavior of the 10 and 25 μm thick devices is similar. Particularly, the overall relative decrease rates in f0 are well related to fatigue life (Nf) and can be used to predict Nf within a factor of 5, for Nf ranging from 104 to 1010 cycles.
‘Wanna race?’: Primary student preference for competitive or non-competitive singing games

This study compared primary student preference for competitive and non-competitive singing games. Students in three intact classes of second graders (n = 65) and three classes of fourth graders (n = 67) at one school in the USA served as subjects. After playing a pair of games, one competitive and one non-competitive, over the course of five classes, students anonymously named their preferred game and reported reasons for their preference. Results indicated that the students significantly preferred the competitive game, regardless of grade level or gender. The most common reason students provided for their preference was the higher level of kinaesthetic activity.
Comparison of Nifedipine and Ritodrine for the Treatment of Preterm Labor

Treatment of preterm labor with beta-sympathomimetics has been questioned because of the many maternal and fetal complications associated with its use. Nifedipine, a calcium antagonist, has been shown to suppress uterine activity in vitro and in vivo. A randomized prospective study was performed to compare the efficacy of nifedipine to ritodrine in the suppression of preterm labor. Data obtained from 42 women, of which 19 were randomized to the ritodrine group and 23 to the nifedipine group, were analyzed. Ritodrine and nifedipine proved to be equally effective in the suppression of preterm labor. However, the nifedipine group had fewer maternal and fetal complications.
Metacognitive Learning in a Fully Complex-Valued Radial Basis Function Neural Network

Recent studies on human learning reveal that self-regulated learning in a metacognitive framework is the best strategy for efficient learning. As the machine learning algorithms are inspired by the principles of human learning, one needs to incorporate the concept of metacognition to develop efficient machine learning algorithms. In this letter we present a metacognitive learning framework that controls the learning process of a fully complex-valued radial basis function network and is referred to as a metacognitive fully complex-valued radial basis function (Mc-FCRBF) network. Mc-FCRBF has two components: a cognitive component containing the FC-RBF network and a metacognitive component, which regulates the learning process of FC-RBF. In every epoch, when a sample is presented to Mc-FCRBF, the metacognitive component decides what to learn, when to learn, and how to learn based on the knowledge acquired by the FC-RBF network and the new information contained in the sample. The Mc-FCRBF learning algorithm is described in detail, and both its approximation and classification abilities are evaluated using a set of benchmark and practical problems. Performance results indicate the superior approximation and classification performance of Mc-FCRBF compared to existing methods in the literature.
Approximation Algorithms for Semi-random Graph Partitioning Problems

In this paper, we propose and study a new semi-random model for graph partitioning problems. We believe that it captures many properties of real--world instances. The model is more flexible than the semi-random model of Feige and Kilian and planted random model of Bui, Chaudhuri, Leighton and Sipser. We develop a general framework for solving semi-random instances and apply it to several problems of interest. We present constant factor bi-criteria approximation algorithms for semi-random instances of the Balanced Cut, Multicut, Min Uncut, Sparsest Cut and Small Set Expansion problems. We also show how to almost recover the optimal solution if the instance satisfies an additional expanding condition. Our algorithms work in a wider range of parameters than most algorithms for previously studied random and semi-random models. Additionally, we study a new planted algebraic expander model and develop constant factor bi-criteria approximation algorithms for graph partitioning problems in this model.
The accumulation of methylamine counteracting solutes in elasmobranchs with differing levels of urea: a comparison of marine and freshwater species

SUMMARY We compared levels of the major organic osmolytes in the muscle of elasmobranchs, including the methylamines trimethylamine oxide (TMAO), betaine and sarcosine as well as the β-amino acids taurine and β-alanine, and the activities of enzymes of methylamine synthesis (betaine and TMAO) in species with a wide range of urea contents. Four marine, a euryhaline in freshwater (Dasyatis sabina), and two freshwater species, one that accumulates urea (Himantura signifer) and one that does not (Potamotrygon motoro), were analyzed. Urea contents in muscle ranged from 229–352 μmol g–1 in marine species to 2. 0μ mol g–1 in P. motoro. Marine elasmobranchs preferentially accumulate methylamines, possibly to counteract urea effects on macromolecules, whereas the freshwater species with lower urea levels accumulate the β-amino acid taurine as the major non-urea osmolyte. A strong correlation (r2=0.84, P<0.001) with a slope of 0.40 was found between muscle urea content and the combined total methylamines plus total β-amino acids, supporting the hypothesis that `non-urea' osmolytes are specifically maintained at an approximately 2:1 ratio with urea in the muscle of elasmobranchs. All species examined had measurable synthetic capacity for betaine in the liver but only one species had detectable TMAO synthetic capacity. We propose a phylogenetic explanation for the distribution of TMAO synthesis in elasmobranchs and suggest that activation of liver betaine aldehyde dehydrogenase, relative to choline dehydrogenase, coincides with betaine accumulation in elasmobranchs. The latter relationship may be important in maintaining methylamine levels during periods of low dietary TMAO intake for species lacking TMAO synthesis.
What is the Best Material for Molding Casts in Children?

Background: Casts are used to treat clubfeet, developmental dysplasia of the hip (DDH), forearm fractures, and femur fractures. The ability of a cast to maintain a desired shape is termed moldability. Clinicians use plaster, fiberglass, and soft casts. To our knowledge the biomechanical molding characteristics of these 3 materials have never been reported. We hypothesized that moldability of plaster would be better than fiberglass and fiberglass would be better than soft cast. Methods: We compared 12.7 cm wide casts of plaster, fiberglass, and soft cast. Casts were 5 layers thick, prepared in 40°C water, and placed over 2 layers of cotton padding on 5.1 cm and 15.2 cm diameter foam cylinders. A loading device simulated loads applied by clinicians when molding casts for 4 conditions: clubfoot (thumb-shaped 50 N load on 5.1 cm model), DDH (thumb-shaped 100 N load on 15.2 cm model), forearm fracture (palm-shaped 50 N load on 5.1 cm model), and femur fracture (palm-shaped 100 N load on 15.2 cm model). The loading device applied molding for 7 minutes. Five casts of each material were made for each model. Casts were removed, photographed, and the area of maximal deformation was compared with an unmolded cast. A large area of maximal deformation meant that the deformation was spread out over a large area, less precise molding. Results: In the clubfoot model, plaster was more precise than fiberglass (P=0.002) and soft cast (P<0.0001). In the DDH model, plaster was more precise than fiberglass (P<0.0001) and soft cast (P<0.0001) and fiberglass was more precise than soft cast (P<0.0001). In the femur fracture model, plaster was more precise than fiberglass (P=0.001) and soft cast (P=0.001). Conclusions: The moldability of plaster is better than fiberglass and soft cast and fiberglass is better than soft cast. Clinical Relevance: If precise molding is required, plaster has the best moldability. In cases not requiring precise molding, fiberglass and soft cast are lightweight, waterproof, and available in child-friendly colors.
Deep Learning for Robust Normal Estimation in Unstructured Point Clouds

Normal estimation in point clouds is a crucial first step for numerous algorithms, from surface reconstruction and scene understanding to rendering. A recurrent issue when estimating normals is to make appropriate decisions close to sharp features, not to smooth edges, or when the sampling density is not uniform, to prevent bias. Rather than resorting to manually‐designed geometric priors, we propose to learn how to make these decisions, using ground‐truth data made from synthetic scenes. For this, we project a discretized Hough space representing normal directions onto a structure amenable to deep learning. The resulting normal estimation method outperforms most of the time the state of the art regarding robustness to outliers, to noise and to point density variation, in the presence of sharp edges, while remaining fast, scaling up to millions of points.