Sustainability and Maturation of School Turnaround: A Multiyear Evaluation of Tennessee’s Achievement School District and Local Innovation Zones

Recent evaluations of reforms to improve low-performing schools have almost exclusively focused on shorter term effects. In this study, we extend the literature by examining the sustainability and maturation of two turnaround models in Tennessee: the state-led Achievement School District (ASD) and district-led local Innovation Zones (iZones). Using difference-in-differences models, we find overall positive effects on student achievement in iZone schools and null effects in ASD schools. Additional findings suggest a linkage between staff turnover and the effectiveness of reforms. ASD schools experienced high staff turnover in every cohort, and iZone schools faced high turnover in its latest cohort, the only one with negative effects. We discuss how differences in the ASD and iZone interventions may help explain variation in the schools’ ability to recruit and retain effective teachers and principals

Prepping for Another Recession: Re-Assessing the Validity of Teacher Evaluation Systems for Human Capital Decision-Making

Problem. The school budget cuts concomitant with the COVID-19 pandemic mean educator jobs may again be threatened by layoffs. During prior recessions, school district administration primarily deter- mined teacher layoffs by virtue of seniority. However, as new evidence emerges that seniority policies may not be the most equitable way to determine teacher layoffs, some have turned towards performance-based measures from evaluation systems. Purpose. The purpose of this paper is to examine the validity and reli- ability of the Nevada Educator Performance Framework (NEPF) for making human capital decisions like layoffs. Recommendations. We recommend that Nevada and other states improve the differentiation in scores across the varying evaluation domains by engaging in more rigorous training of evaluators. Addi- tionally, we recommend that Nevada and other states improve the distribution of final teacher evaluation scores so that the performance measure really distinguishes among teacher performance. Strategies could include lessening the administrative burden of filling out the final evaluation, increasing the number of performance levels, or rotating the specific standards focused on each year

Dr. LADA: diagnosing black pepper pest and diseases with decision tree

Malaysia has the distinction of being the world’s fifth largest pepper producer country whereby 98% of the country's annual production comes from the State of Sarawak. However, crop loss due to pest and disease incidence has been identified as one of the major pepper production constraints. Inefficient advisory mechanism and assistance from extension staff due to technical and logistic limitations have hindered the pest and disease diagnosis effort for pepper. Currently, extension staff from MPB will have to travel to the rural farms when contacted, or during their visits to advice or treat the plants. Therefore, “DR. LADA”, was jointly developed by Malaysian Pepper Board and Universiti Kebangsaan Malaysia to diagnose six pests and ten diseases of pepper which commonly found in Malaysia and recommends appropriate management measures to solve the problems. This an interactive android-based mobile app used an inference engine utilises the forward-backward chaining methods to trigger the correct output from decision tree that inter-relates the expert rules which extracted and validated by Malaysian Pepper Board experts. Dr. LADA is a native mobile app develop on a java-based platform which provides fast performance, high degree of reliability and can be used without any internet connection. The app has been tested with 10 case studies carried out by Malaysian Pepper Board and scored 97% of accuracy. Having Dr. LADA, user can identify problems by answering a series of questions from symptoms shown by several plant parts. Therefore, the dependency of farmers on extension staff are reduced, and indirectly minimizing the extension activity costs

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BackgroundPatient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.ObjectivesWe sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.MethodsWe compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites.ResultsSeveral attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001).ConclusionsOverall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare

Evolving Availability and Standardization of Patient Attributes for Matching

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (\u3e90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States

Opioid medication use and blood DNA methylation:epigenome-wide association meta-analysis

Aim: To identify differential methylation related to prescribed opioid use. Methods: This study examined whether blood DNA methylation, measured using Illumina arrays, differs by recent opioid medication use in four population-based cohorts. We meta-analyzed results (282 users; 10,560 nonusers) using inverse-variance weighting. Results: Differential methylation (false discovery rate \u3c0.05) was observed at six CpGs annotated to the following genes: KIAA0226, CPLX2, TDRP, RNF38, TTC23 and GPR179. Integrative epigenomic analyses linked implicated loci to regulatory elements in blood and/or brain. Additionally, 74 CpGs were differentially methylated in males or females. Methylation at significant CpGs correlated with gene expression in blood and/or brain. Conclusion: This study identified DNA methylation related to opioid medication use in general populations. The results could inform the development of blood methylation biomarkers of opioid use

Surgical Outcomes in Benign Gynecologic Surgery Patients during the COVID-19 Pandemic (SOCOVID study)

Study Objective To determine the incidence of perioperative coronavirus disease (COVID-19) in women undergoing benign gynecologic surgery and to evaluate perioperative complication rates in patients with active, previous, or no previous severe acute respiratory syndrome coronavirus 2 infection. Design A multicenter prospective cohort study. Setting Ten institutions in the United States. Patients Patients aged >18 years who underwent benign gynecologic surgery from July 1, 2020, to December 31, 2020, were included. All patients were followed up from the time of surgery to 10 weeks postoperatively. Those with intrauterine pregnancy or known gynecologic malignancy were excluded. Interventions Benign gynecologic surgery. Measurements and Main Results The primary outcome was the incidence of perioperative COVID-19 infections, which was stratified as (1) previous COVID-19 infection, (2) preoperative COVID-19 infection, and (3) postoperative COVID-19 infection. Secondary outcomes included adverse events and mortality after surgery and predictors for postoperative COVID-19 infection. If surgery was delayed because of the COVID-19 pandemic, the reason for postponement and any subsequent adverse event was recorded. Of 3423 patients included for final analysis, 189 (5.5%) postponed their gynecologic surgery during the pandemic. Forty-three patients (1.3% of total cases) had a history of COVID-19. The majority (182, 96.3%) had no sequelae attributed to surgical postponement. After hospital discharge to 10 weeks postoperatively, 39 patients (1.1%) became infected with severe acute respiratory syndrome coronavirus 2. The mean duration of time between hospital discharge and the follow-up positive COVID-19 test was 22.1 ± 12.3 days (range, 4–50 days). Eleven (31.4% of postoperative COVID-19 infections, 0.3% of total cases) of the newly diagnosed COVID-19 infections occurred within 14 days of hospital discharge. On multivariable logistic regression, living in the Southwest (adjusted odds ratio, 6.8) and single-unit increase in age-adjusted Charlson comorbidity index (adjusted odds ratio, 1.2) increased the odds of postoperative COVID-19 infection. Perioperative complications were not significantly higher in patients with a history of positive COVID-19 than those without a history of COVID-19, although the mean duration of time between previous COVID-19 diagnosis and surgery was 97 days (14 weeks). Conclusion In this large multicenter prospective cohort study of benign gynecologic surgeries, only 1.1% of patients developed a postoperative COVID-19 infection, with 0.3% of infection in the immediate 14 days after surgery. The incidence of postoperative complications was not different in those with and without previous COVID-19 infections

Genome-Wide Analyses of Nkx2-1 Binding to Transcriptional Target Genes Uncover Novel Regulatory Patterns Conserved in Lung Development and Tumors

The homeodomain transcription factor Nkx2-1 is essential for normal lung development and homeostasis. In lung tumors, it is considered a lineage survival oncogene and prognostic factor depending on its expression levels. The target genes directly bound by Nkx2-1, that could be the primary effectors of its functions in the different cellular contexts where it is expressed, are mostly unknown. In embryonic day 11.5 (E11.5) mouse lung, epithelial cells expressing Nkx2-1 are predominantly expanding, and in E19.5 prenatal lungs, Nkx2-1-expressing cells are predominantly differentiating in preparation for birth. To evaluate Nkx2-1 regulated networks in these two cell contexts, we analyzed genome-wide binding of Nkx2-1 to DNA regulatory regions by chromatin immunoprecipitation followed by tiling array analysis, and intersected these data to expression data sets. We further determined expression patterns of Nkx2-1 developmental target genes in human lung tumors and correlated their expression levels to that of endogenous NKX2-1. In these studies we uncovered differential Nkx2-1 regulated networks in early and late lung development, and a direct function of Nkx2-1 in regulation of the cell cycle by controlling the expression of proliferation-related genes. New targets, validated in Nkx2-1 shRNA transduced cell lines, include E2f3, Cyclin B1, Cyclin B2, and c-Met. Expression levels of Nkx2-1 direct target genes identified in mouse development significantly correlate or anti-correlate to the levels of endogenous NKX2-1 in a dosage-dependent manner in multiple human lung tumor expression data sets, supporting alternative roles for Nkx2-1 as a transcriptional activator or repressor, and direct regulator of cell cycle progression in development and tumors

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care