Miracle-dz/UniFAR-FTUs huggingface dataset:
New teacher isolation and its relationship to teacher attrition

New Teacher Isolation and its Relationship to Teacher Attrition By David S. Sleppin M.A., Fairleigh Dickinson University, 2004 M.A.T. , Fairleigh Dickinson University, 1990 B.A., University of Delaware, 1986 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Education Administrator Leadership for Teaching and Learning Walden University February 2009 ABSTRACT Studies suggest that many promising new teachers who experience isolation do not reachStudies suggest that many promising new teachers who experience isolation do not reach their full potential and may leave the teaching profession prematurely. Accordingly, the purpose of this study was to explore the relationship between the experience of isolation among new teachers and the potential for teacher attrition in an urban school district in the northeastern U. S. Grounded in constructivist theory, the phenomenological research design examined in-depth interview data collected from 8 new public elementary school teachers with three or less years experience. A coding procedure began by extracting key phrases and statements from the raw data and reduced information into categories and themes based on frequency and alignment to the research focus. An analysis of the thematic data revealed several shared factors regarding mentoring and isolation including consensus that mentoring was instrumental in reducing feelings of isolation, that isolation is experienced in different ways and to varying degrees, and that new teachers have a strong desire to remain in the profession, but might leave their current assignment due to feelings of isolation. It was concluded that new teachers participating in the study found that mentoring and a strong belief in the importance of education helped them feel less isolated and more connected to their learning community. Recommendations for action included implementing quality induction programs for new teachers, requiring participation in these programs for at least two years, and providing better peer mentoring experiences for new teachers. These recommendations have the potential to create a more positive experience for new teachers. This study has implications for positive social change in new teacher training which involves mentors, school leaders, peer coaches, and communities of teachers working together to meet the needs of today’s new teachers. New Teacher Isolation and its Relationship to Teacher Attrition By
Retrospective on a year of participatory design using the PICTIVE technique

PICTIVE is a participatory design technique for increasing the direct and effective involvement of users and other stakeholders in the design of software. This paper reviews a year of the use of PICTVE on products and research prototypes at Bellcore. What we have learned is illustrated through five brief case studies. The paper concludes with a summary of our current PICTIVE practice, expressed as three developing, interrelated models: an object model, a process model, and a participation model.
Specific immunological test for the rapid identification of members of the genus Histoplasma

A sensitive and specific immunological method was developed for rapid identification of the mycelial forms of Histoplasma capsulatum var. capsulatum, H. capsulatum var. duboisii, and H. farciminosum and for separation of these pathogenic fungi from morphologically similar hyphomycetes and other fungal pathogens. This method is based on the fact that all of the Histoplasma spp. produce H and M histoplasmin antigens, whereas the other fungi do not. Inocula consisting of heavy mycelial growth from a pure, full-grown culture were transferred into flasks containing small volumes of brain heart infusion broth. These cultures were placed on a shaker and grown at 25 C. Using the micro-immunodiffusion technique and antisera containing antibodies to H and M precipitinogens, we detected exoantigens in 3-day-old brain heart infusion culture supernatants concentrated 25 and 50 times. The ability of the procedure to identify Histoplasma spp. was evaluated by testing 96 unknown mycelial cultures that grossly or microscopically resembled Histoplasma spp. Three- and six-day-old concentrated culture supernatants prepared from each unknown were tested against rabbit anti-Arthroderma tuberculatum, Chrysosporium keratinophilum, H. capsulatum var. duboisii, and Corynascus (Thielavia) sepedonium sera and human histoplasmosis case serum. Each unknown was also identified by conventional laboratory procedures involving cultural and, where necessary, in vivo studies. In the comparative evaluation the immunological test was observed to be 100% sensitive. It permitted the accurate generic identification of the Histoplasma spp. within 5 days, in contrast to the average of 33 days required by the routine mycological procedure.
Runx1 Protects Hematopoietic Stem/Progenitor Cells from Oncogenic Insult

The RUNX1/AML1 gene encodes a transcription factor essential for the generation of hematopoietic stem cells and is frequently targeted in human leukemia. In human RUNX1‐related leukemias, the RAS pathway is often concurrently mutated, but the mechanism of the synergism remains elusive. Here, we found that inactivation of Runx1 in mouse bone marrow cells results in an increase in the stem/progenitor cell fraction due to suppression of apoptosis and elevated expression of the polycomb gene Bmi‐1, which is important for stem cell self‐renewal. Introduction of oncogenic N‐RAS into wild‐type cells, in contrast, reduced the stem/progenitor cell fraction because of senescence, apoptosis, and differentiation. Such detrimental events presumably occurred because of the cellular fail‐safe program, although hyperproliferation was initially induced by an oncogenic stimulus. Runx1 insufficiency appears to impair such a fail‐safe mechanism, particularly in the stem/progenitor cells, thereby supporting the clonal maintenance of leukemia‐initiating cells expressing an activated oncogene.
Correlated random features for fast semi-supervised learning

This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates two views consisting of computationally inexpensive random features. Second, multiview regression, using Canonical Correlation Analysis (CCA) on unlabeled data, biases the regression towards useful features. It has been shown that CCA regression can substantially reduce variance with a minimal increase in bias if the views contains accurate estimators. Recent theoretical and empirical work shows that regression with random features closely approximates kernel regression, implying that the accuracy requirement holds for random views. We show that XNV consistently outperforms a state-of-the-art algorithm for semi-supervised learning: substantially improving predictive performance and reducing the variability of performance on a wide variety of real-world datasets, whilst also reducing runtime by orders of magnitude.
Rural Caregivers for a Family Member With Dementia

Forecasts of increasing prevalence of dementia in rural settings, coupled with reliance on family caregiver support, indicate that a greater understanding of caregiver distress in these contexts is necessary. The purpose of this study was to examine family caregiver burden and severity of distress on the day that a family member was diagnosed with dementia at a memory clinic that serves a rural population. Participants in this retrospective study were 231 primary family caregivers of a rural community-dwelling person with dementia. On the diagnostic day, women reported more burden and severity of distress than men and spouses reported more severity of distress than adult children. A structural equation model was not supported for the entire sample, but was supported for women caregivers only (n = 161). Caregiver distress related to dementia-specific behaviors explained both global distress and burden. Patients’ functional decline was related to caregiver burden.
Control of heart rate by cAMP sensitivity of HCN channels

“Pacemaker” f-channels mediating the hyperpolarization-activated nonselective cation current If are directly regulated by cAMP. Accordingly, the activity of f-channels increases when cellular cAMP levels are elevated (e.g., during sympathetic stimulation) and decreases when they are reduced (e.g., during vagal stimulation). Although these biophysical properties seem to make f-channels ideal molecular targets for heart rate regulation by the autonomic nervous system, the exact contribution of the major If-mediating cardiac isoforms HCN2 and HCN4 to sinoatrial node (SAN) function remains highly controversial. To directly investigate the role of cAMP-dependent regulation of hyperpolarization activated cyclic nucleotide activated (HCN) channels in SAN activity, we generated mice with heart-specific and inducible expression of a human HCN4 mutation (573X) that abolishes the cAMP-dependent regulation of HCN channels. We found that hHCN4–573X expression causes elimination of the cAMP sensitivity of If and decreases the maximum firing rates of SAN pacemaker cells. In conscious mice, hHCN4–573X expression leads to a marked reduction in heart rate at rest and during exercise. Despite the complete loss of cAMP sensitivity of If, the relative extent of SAN cell frequency and heart rate regulation are preserved. Our data demonstrate that cAMP-mediated regulation of If determines basal and maximal heart rates but does not play an indispensable role in heart rate adaptation during physical activity. Our data also reveal the pathophysiologic mechanism of hHCN4–573X–linked SAN dysfunction in humans.
Effect of an interactive therapeutic robotic animal on engagement, mood states, agitation and psychotropic drug use in people with dementia: a cluster-randomised controlled trial protocol

Introduction Apathy, agitated behaviours, loneliness and depression are common consequences of dementia. This trial aims to evaluate the effect of a robotic animal on behavioural and psychological symptoms of dementia in people with dementia living in long-term aged care. Methods and analysis A cluster-randomised controlled trial with three treatment groups: PARO (robotic animal), Plush-Toy (non-robotic PARO) or Usual Care (Control). The nursing home sites are Australian Government approved and accredited facilities of 60 or more beds. The sites are located in South-East Queensland, Australia. A sample of 380 adults with a diagnosis of dementia, aged 60 years or older living in one of the participating facilities will be recruited. The intervention consists of three individual 15 min non-facilitated sessions with PARO or Plush-Toy per week, for a period of 10 weeks. The primary outcomes of interest are improvement in agitation, mood states and engagement. Secondary outcomes include sleep duration, step count, change in psychotropic medication use, change in treatment costs, and staff and family perceptions of PARO or Plush-Toy. Video data will be analysed using Noldus XT Pocket Observer; descriptive statistics will be used for participants’ demographics and outcome measures; cluster and individual level analyses to test all hypotheses and Generalised Linear Models for cluster level and Generalised Estimation Equations and/or Multi-level Modeling for individual level data. Ethics and dissemination The study participants or their proxy will provide written informed consent. The Griffith University Human Research Ethics Committee has approved the study (NRS/03/14/HREC). The results of the study will provide evidence of the efficacy of a robotic animal as a psychosocial treatment for the behavioural and psychological symptoms of dementia. Findings will be presented at local and international conference meetings and published in peer-reviewed journals. Trial registration number Australian and New Zealand Clinical Trials Registry number ACTRN12614000508673 date registered 13/05/2014.
I Want To Learn What I Want To Learn in the Way I Choose To Learn It: Using Learning Contracts.

Contract learning is an alternative way of structuring a learning experience. Instead of specifying how a body of content will be transmitted (content plan), it specifies how a body of content will be acquired by the learner (process plan). Students identify problems or issues that are real for them and relevant to the course at hand, state learning objectiyes that address attainment of knowledge and/or development of a skill, and name learning resources. Results of the learning contract demonstrate evidence of the achievement of learning objectives. This guide provides information on: (1) the theoretical foundations of contract learning; (2) developing effective learning contract assignments; (3) the instructor's role; (4) learning contracts to improve students' understanding of the dynamics of interpersonal communication; (5) an interpersonal communication evaluation form; (6) student feedback regarding the learning contract assignment; and (7) advantages of using learning contracts. A bibliography and an example of a completed learning contract written by a student including summaries of information sources, personal insights, and a follow-up action plan are appended. (LL) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * *********************************************************************** I WANT TO LEARN WHAT I WANT TO LEARN IN THE WAY I CHOOSE TO LEARN IT: USING LEARNING CONTRACTS
Constrained speaker diarization of TV series based on visual patterns

Speaker diarization, usually denoted as the “who spoke when” task, turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects.). Despite this acoustic variability, such movies exhibit specific visual patterns in the dialogue scenes. In this paper, we introduce a two-step method to achieve speaker diarization in TV series: a speaker diarization is first performed locally in the scenes detected as dialogues; then, the hypothesized local speakers are merged in a second agglomerative clustering process, with the constraint that speakers locally hypothesized to be distinct must not be assigned to the same cluster. The performances of our approach are compared to those obtained by standard speaker diarization tools applied to the same data.
The effectiveness of peer-designed agents in agent-based simulations

The ability to reliably represent and replicate choices people make is crucial for building accurate models of day-to-day situations. The fact that people are inherently rationally-and computationally-bounded increases the difficulties in designing such simulations. This paper builds on the use of peer-designed agents PDAs --computer agents developed by people --to show their effectiveness in generating a variety of strategies and behaviors and in alleviating the simulation and behavior analysis of systems populated by human individuals with diverse strategies. The paper synthesizes the PDA-based simulation components and ideas that appear in recent PDAs literature into a cohesive simulation design, and reports a set of experiments aiming at validating the ability of PDA-based simulations to exhibit realistic behavior both in the individual agent and the system levels. The validation of individuals' ability to reliably capture their strategies into PDAs is quantitative, relying on four games from different domains. The domains vary in aspects such as game complexity and the environment dynamics. The applicability of the PDA-based approach in the system level is evaluated using a large scale experiment involving 34 PDAs, each designed by a different person. All in all, the set of strategies obtained by means of PDAs is substantially richer and more varied in comparison to the limited sets of strategies used in prior multi-agent simulation studies.
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation

We present an algorithmic framework for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large data sets with relatively small samples. The selected feature sets can be used for causal discovery and classification. The framework (Generalized Local Learning, or GLL) can be instantiated in numerous ways, giving rise to both existing state-of-the-art as well as novel algorithms. The resulting algorithms are sound under well-defined sufficient conditions. In a first set of experiments we evaluate several algorithms derived from this framework in terms of predictivity and feature set parsimony and compare to other local causal discovery methods and to state-of-the-art non-causal feature selection methods using real data. A second set of experimental evaluations compares the algorithms in terms of ability to induce local causal neighborhoods using simulated and resimulated data and examines the relation of predictivity with causal induction performance. Our experiments demonstrate, consistently with causal feature selection theory, that local causal feature selection methods (under broad assumptions encompassing appropriate family of distributions, types of classifiers, and loss functions) exhibit strong feature set parsimony, high predictivity and local causal interpretability. Although non-causal feature selection methods are often used in practice to shed light on causal relationships, we find that they cannot be interpreted causally even when they achieve excellent predictivity. Therefore we conclude that only local causal techniques should be used when insight into causal structure is sought. In a companion paper we examine in depth the behavior of GLL algorithms, provide extensions, and show how local techniques can be used for scalable and accurate global causal graph learning.
Safety/tolerability of the anti-semaphorin 4D Antibody VX15/2503 in a randomized phase 1 trial

Objective: To evaluate the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of VX15/2503 in a randomized, single-dose, dose-escalation, double-blind, placebo-controlled study enrolling adult patients with MS. Methods: Single IV doses of VX15/2503 or placebo were administered. Ten patients each were randomized (4:1 randomization ratio) into 5 ascending dose cohorts of 1, 3, 6, 10, or 20 mg/kg. Safety, immunogenicity, PK/PD, MRI, ECG, and lymphocyte subset levels were evaluated. A Dose Escalation Safety Committee (DESC) approved each dose escalation. Results: VX15/2503 was well tolerated, and all participants completed the study. Antibody treatment–related adverse events were primarily grade 1 or 2 and included urinary tract infection (12.5%) and muscle weakness, contusion, and insomnia (each 7.5%). No dose-limiting toxicities were observed, and no maximum tolerated dose was determined. One subject (20 mg/kg) experienced disease relapse 3 months before study entry and exhibited a grade 3 (nonserious) increase in brain lesions by day 29, possibly related to VX15/2503. Twenty-nine patients exhibited human anti-humanized antibody responses; 5 with titer ≥100. No anti-VX15/2503 antibody responses were fully neutralizing. VX15/2503 Cmax, area under the time-concentration curve, and mean half-life increased with dose level; at 20 mg/kg, the T1/2 was 20 days. Cellular SEMA4D saturation occurred at serum antibody concentrations ≤0.3 μg/mL, resulting in decreased cSEMA4D expression. At 20 mg/kg, cSEMA4D saturation persisted for ≥155 days. Total sSEMA4D levels increased with dose level and declined with antibody clearance. Conclusions: These results support the continued investigation of VX15/2503 in neurodegenerative diseases. ClinicalTrials.gov identifier: NCT01764737. Classification of evidence: This study provides Class III evidence that anti-semaphorin 4D antibody VX15/2503 at various doses was safe and well tolerated vs placebo, although an increase in treatment-emergent adverse events in the treatment group could not be excluded (risk difference −0.7%, 95% CI −28.0% to 32.7%).
PSF accuracy measure for evaluation of blur estimation algorithms

Given the large amount of blur estimation and blind deconvolution methods just in the last decade, there is an increasing need to compare the performance of a particular method with others. Unlike in other fields in image processing, there are very few well-established benchmark databases of test data and, more importantly, no standard way of performance evaluation. In this paper, we focus on the latter. We propose a new error measure for the blur kernel - a method for comparison of the blur estimate with the ground truth - which correctly reflects how inaccuracies in the blur estimation affect the subsequent image restoration, without the necessity to perform the actual deconvolution.
The lead content of blood serum.

Serum lead concentrations measured by stable isotope dilution with a thermal ionization mass spectrometer and blood lead concentrations measured by graphite furnace atomic absorption spectrophotometry are reported for 73 women of child bearing age resident in Los Angeles, California. The two quantities are related by the line y=0.00030+0.00241x (r=0.83), where y is serum lead concentration and x is blood lead concentration, both being expressed in units of microg/L. The linearity of the relationship appears to hold to a blood lead concentration of at least 60 microg Pb/L. The slope of the line indicates that lead in serum is 0.24% of that in whole blood in contrast to recent reports of between 0.32 and 0.35% determined by inductively coupled plasma (ICP) mass spectrometry with bismuth used as an internal standard. The discrepancy stems from the ICP mass spectrometer-generated curves not passing through the origin.
Unsupervised learning on k-partite graphs

Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the research on mining the hidden structures from a k-partite graph is still limited and preliminary. In this paper, we propose a general model, the relation summary network, to find the hidden structures (the local cluster structures and the global community structures) from a k-partite graph. The model provides a principal framework for unsupervised learning on k-partite graphs of various structures. Under this model, we derive a novel algorithm to identify the hidden structures of a k-partite graph by constructing a relation summary network to approximate the original k-partite graph under a broad range of distortion measures. Experiments on both synthetic and real datasets demonstrate the promise and effectiveness of the proposed model and algorithm. We also establish the connections between existing clustering approaches and the proposed model to provide a unified view to the clustering approaches.
Dependency-based Syntactic–Semantic Analysis with PropBank and NomBank

This paper presents our contribution in the closed track of the 2008 CoNLL Shared Task (Surdeanu et al., 2008). To tackle the problem of joint syntactic--semantic analysis, the system relies on a syntactic and a semantic subcomponent. The syntactic model is a bottom-up projective parser using pseudo-projective transformations, and the semantic model uses global inference mechanisms on top of a pipeline of classifiers. The complete syntactic--semantic output is selected from a candidate pool generated by the subsystems. The system achieved the top score in the closed challenge: a labeled syntactic accuracy of 89.32%, a labeled semantic F1 of 81.65, and a labeled macro F1 of 85.49.