Assessment of proofreading and editing with technical diploma students at Western Wisconsin Technical College - Mauston

Plan BProofreading and editing are a major component of the Office Assistant program. The practices of proofreading and editing are an integral part of primary skills employers expect from their employees. The ability to proofread and edit a document are critical components in reading and writing skills that employers look for in hiring people or in choosing an employee for promotion. The purpose of this study was to determine the degree of how proofreading and editing help students perceive themselves as better writers as they progress through the process of proofreading, editing, journal writing, error logs and peer editing. Nine students, who entered the Technical Diploma Office Assistant program at Western Wisconsin Technical College - Mauston campus, in August, 1999 and graduated in May 2000, comprised the samples. A proofreading and editing pretest was administered to the entering Technical Diploma class in September of 1999 - prior to the beginning of program instruction. Proofreading and editing assignments were given in September 1999, October 1999 and November 1999. A posttest was given in November of 1999. The researcher at Western Wisconsin Technical College - Mauston campus, administered the pretest, assignments and posttest

Graded persistence diagrams and persistence landscapes

We introduce a refinement of the persistence diagram, the graded persistence diagram. It is the Mobius inversion of the graded rank function, which is obtained from the rank function using the unary numeral system. Both persistence diagrams and graded persistence diagrams are integer-valued functions on the Cartesian plane. Whereas the persistence diagram takes non-negative values, the graded persistence diagram takes values of 0, 1, or -1. The sum of the graded persistence diagrams is the persistence diagram. We show that the positive and negative points in the k-th graded persistence diagram correspond to the local maxima and minima, respectively, of the k-th persistence landscape. We prove a stability theorem for graded persistence diagrams: the 1-Wasserstein distance between k-th graded persistence diagrams is bounded by twice the 1-Wasserstein distance between the corresponding persistence diagrams, and this bound is attained. In the other direction, the 1-Wasserstein distance is a lower bound for the sum of the 1-Wasserstein distances between the k-th graded persistence diagrams. In fact, the 1-Wasserstein distance for graded persistence diagrams is more discriminative than the 1-Wasserstein distance for the corresponding persistence diagrams.Comment: accepted for publication in Discrete and Computational Geometr

Practice-Based Coaching and Early Childhood Professional Standards in a Diverse Field

Abstract The field of early childhood education has long relied on professional development strategies to support teachers with varying degrees of education who enter the field from a variety of disciplines. Research indicated educators needed intensive and individualized professional development efforts that were integrated into daily practice (Rodgers, Kennedy, VanUitert, & Myers, 2019). Practice-based coaching has been used as a professional development strategy in early childhood classrooms to develop educators’ knowledge and skills in best practices for young children. Thirty-two empirical studies conducted since 2011 on the process, effectiveness, and assessment of practice-based coaching were reviewed to identify coaching components, processes, and the strengths and weaknesses of the strategy to consider how coaching could be used to develop professionalism within the diverse early childhood education workforce. The results indicated practice-based coaching was an effective strategy in the classroom to build teacher skills and knowledge and aid in children’s development. Practice-based coaching aligned with NAEYC’s professional standards. Studies in inclusive school classrooms, family childcares, small and large programs were reviewed to determine practice-based coaching’s effect in diverse settings. Results indicated literature was lacking in the full range of diverse settings and provider demographics, exposing a gap in research and an opportunity for future study. Exceptions to the long-term outcomes of coaching in some of the research suggested future studies were needed to consider additional support strategies after the coaching process ended (Unver, 2016). Keywords: Practice-based coaching, mentoring, professional development, family childcare, infant-toddler care, professional standard

Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.Comment: 4 pages, 5 figures, accepted for Neural Engineering (NER) 2019 Conferenc

Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts

Alzheimer\u27s disease biomarkers are becoming increasingly important for characterizing the longitudinal course of disease, predicting the timing of clinical and cognitive symptoms, and for recruitment and treatment monitoring in clinical trials. In this work, we develop and evaluate three methods for modelling the longitudinal course of amyloid accumulation in three cohorts using amyloid PET imaging. We then use these novel approaches to investigate factors that influence the timing of amyloid onset and the timing from amyloid onset to impairment onset in the Alzheimer\u27s disease continuum. Data were acquired from the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI), the Baltimore Longitudinal Study of Aging (BLSA) and the Wisconsin Registry for Alzheimer\u27s Prevention (WRAP). Amyloid PET was used to assess global amyloid burden. Three methods were evaluated for modelling amyloid accumulation using 10-fold cross-validation and holdout validation where applicable. Estimated amyloid onset age was compared across all three modelling methods and cohorts. Cox regression and accelerated failure time models were used to investigate whether sex, apolipoprotein E genotype and e4 carriage were associated with amyloid onset age in all cohorts. Cox regression was used to investigate whether apolipoprotein E (e4 carriage and e3e3, e3e4, e4e4 genotypes), sex or age of amyloid onset were associated with the time from amyloid onset to impairment onset (global clinical dementia rating ≥1) in a subset of 595 ADNI participants that were not impaired before amyloid onset. Model prediction and estimated amyloid onset age were similar across all three amyloid modelling methods. Sex and apolipoprotein E e4 carriage were not associated with PET-measured amyloid accumulation rates. Apolipoprotein E genotype and e4 carriage, but not sex, were associated with amyloid onset age such that e4 carriers became amyloid positive at an earlier age compared to non-carriers, and greater e4 dosage was associated with an earlier amyloid onset age. In the ADNI, e4 carriage, being female and a later amyloid onset age were all associated with a shorter time from amyloid onset to impairment onset. The risk of impairment onset due to age of amyloid onset was non-linear and accelerated for amyloid onset age \u3e65. These findings demonstrate the feasibility of modelling longitudinal amyloid accumulation to enable individualized estimates of amyloid onset age from amyloid PET imaging. These estimates provide a more direct way to investigate the role of amyloid and other factors that influence the timing of clinical impairment in Alzheimer\u27s disease

In-Context Learning in Large Language Models: A Neuroscience-inspired Analysis of Representations

Large language models (LLMs) exhibit remarkable performance improvement through in-context learning (ICL) by leveraging task-specific examples in the input. However, the mechanisms behind this improvement remain elusive. In this work, we investigate embeddings and attention representations in Llama-2 70B and Vicuna 13B. Specifically, we study how embeddings and attention change after in-context-learning, and how these changes mediate improvement in behavior. We employ neuroscience-inspired techniques, such as representational similarity analysis (RSA), and propose novel methods for parameterized probing and attention ratio analysis (ARA, measuring the ratio of attention to relevant vs. irrelevant information). We designed three tasks with a priori relationships among their conditions: reading comprehension, linear regression, and adversarial prompt injection. We formed hypotheses about expected similarities in task representations to investigate latent changes in embeddings and attention. Our analyses revealed a meaningful correlation between changes in both embeddings and attention representations with improvements in behavioral performance after ICL. This empirical framework empowers a nuanced understanding of how latent representations affect LLM behavior with and without ICL, offering valuable tools and insights for future research and practical applications.Comment: Added overview figures 1-3 in this versio

Prospective nasal screening for methicillin-resistant Staphylococcus aureus in critically ill patients with suspected pneumonia

We carried out a prospective de-escalation study based on methicillin-resistan

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting

The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0.803 [95% confidence interval [CI]: 0.795-0.811], area under the precision recall curves: 0.130 [95% CI: 0.121-0.140]) followed NEWS, Modified Early Warning Score, and quick Sequential Organ Failure Assessment (qSOFA). Using validated thresholds, NEWS 2 also had the highest recall (0.758 [95% CI: 0.736-0.778]) but qSOFA had the highest specificity (0.950 [95% CI: 0.948-0.952]), positive predictive value (0.184 [95% CI: 0.169-0.198]), and F1 score (0.236 [95% CI: 0.220-0.253]). While NEWS 2 outperformed all other compared EWS and patient acuity scores, due to the low prevalence of sepsis, all scoring systems were prone to false positives (low positive predictive value without drastic sacrifices in sensitivity), thus leaving room for more computationally advanced approaches

Sepsis prediction for the general ward setting

OBJECTIVE: To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design. DESIGN: Retrospective analysis of data extracted from electronic health records (EHR). SETTING: Single, tertiary-care academic medical center in St. Louis, MO, USA. PATIENTS: Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (AUPRC) of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 h of crossing the alert threshold, 20.9% had a sepsis-related event occur. CONCLUSIONS: A machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h