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IntroductionCancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects.MethodsThis prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes.AnalysisMachine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics.ConclusionThe individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Abstract Genetic evidence suggests glioma risk is altered by leukocyte telomere length, allergic disease (asthma, hay fever or eczema), alcohol consumption, childhood obesity, low-density lipoprotein cholesterol (LDLc) and triglyceride levels. DNA methylation (DNAm) variation influences many of these glioma-related traits and is an established feature of glioma. Yet the causal relationship between DNAm variation with both glioma incidence and glioma risk factors is unknown. We applied a two-step Mendelian randomization (MR) approach and several sensitivity analyses (including colocalization and Steiger filtering) to assess the association of DNAm with glioma risk factors and glioma incidence. We used data from a recently published catalogue of germline genetic variants robustly associated with DNAm variation in blood (32,851 participants) and data from a genome-wide association study of glioma risk (12,488 cases and 18,169 controls, sub-divided into 6191 glioblastoma cases and 6305 non-glioblastoma cases). MR evidence indicated that DNAm at 3 CpG sites (cg01561092, cg05926943, cg01584448) in one genomic region (HEATR3) had a putative association with glioma and glioblastoma risk (False discovery rate [FDR]

Abstract Passive acoustic monitoring using Autonomous Recording Units (ARUs) is becoming a significant research tool for collecting large amounts of ecological data. Northern bobwhite Colinus virginianus is an economically important game bird whose declining populations are of conservation concern, so efforts to monitor bobwhite abundance using ARUs are being intensified. Yet, manual processing of ARU data is time consuming and often expensive, so developing automatic call detection methods is a key step in acoustic monitoring. We present here the first single species convolutional neural network (CNN) developed purely for automatic bobwhite covey call identification and classification. We demonstrate the value of meaningful data augmentation by including non‐target calls and background noise into our training dataset, as well as evaluating alternative CNN score thresholds and model extrapolation performance. We trained our CNN on 6,682 manually labeled covey calls across three groups of sites within the southeastern USA. Precision and AUC from both CNN classification and individual call detection was high (0.80–0.99), and our model showed strong extrapolation ability across site groups. However, extrapolation performance significantly decreased for sites that were more dissimilar to the training data set if our meaningful data augmentation process was omitted. Our CNN detected significantly more covey calls than manual labeling using Raven Pro software, and processing time was greatly reduced: a single one hour wav file can be now analyzed by the CNN in roughly eight seconds. We also demonstrate using a simple case study that extremely high variability in estimates of bobwhite site occupancy and detection are obtained depending on the method of acoustic data processing (manual versus CNN). Our results suggest that our CNN provides robust and time‐saving analysis of bobwhite covey call acoustic data and can be applied to future research and monitoring projects with high confidence in the performance of the model.

The DIII-D tokamak has elucidated crucial physics and developed projectable solutions for ITER and fusion power plants in the key areas of core performance, boundary heat and particle transport, and integrated scenario operation, with closing the core-edge integration knowledge gap being the overarching mission. New experimental validation of high-fidelity, multi-channel, non-linear gyrokinetic turbulent transport models for ITER provides strong confidence it will achieve Q ⩾ 10 operation. Experiments identify options for easing H-mode access in hydrogen, and give new insight into the isotopic dependence of transport and confinement. Analysis of 2,1 islands in unoptimized low-torque IBS demonstration discharges suggests their onset time occurs randomly in the constant β phase, most often triggered by non-linear 3-wave coupling, thus identifying an NTM seeding mechanism to avoid. Pure deuterium SPI for disruption mitigation is shown to provide favorable slow cooling, but poor core assimilation, suggesting paths for improved SPI on ITER. At the boundary, measured neutral density and ionization source fluxes are strongly poloidally asymmetric, implying a 2D treatment is needed to model pedestal fuelling. Detailed measurements of pedestal and SOL quantities and impurity charge state radiation in detached divertors has validated edge fluid modelling and new self-consistent ‘pedestal-to-divertor’ integrated modeling that can be used to optimize reactors. New feedback adaptive ELM control minimizes confinement reduction, and RMP ELM suppression with sustained high core performance was obtained for the first time with the outer strike point in a W-coated, compact and unpumped small-angle slot divertor. Advances have been made in integrated operational scenarios for ITER and power plants. Wide pedestal intrinsically ELM-free QH-modes are produced with more reactor-relevant conditions, Low torque IBS with W-equivalent radiators can exhibit predator-prey oscillations in T _e and radiation which need control. High- β _P scenarios with q _min > 2, q _95 –7.9, β _N > 4, β _T –3.3% and H _98y2 > 1.5 are sustained with high density ( $\bar n$ = 7E19 m ^−3 , f _G –1) for 6 τ _E , improving confidence in steady-state tokamak reactors. Diverted NT plasmas achieve high core performance with a non-ELMing edge, offering a possible highly attractive core-edge integration solution for reactors.

ABSTRACT The Genomics Education Partnership (GEP) engages students in a course-based undergraduate research experience (CURE). To better understand the student attributes that support success in this CURE, we asked students about their attitudes using previously published scales that measure epistemic beliefs about work and science, interest in science, and grit. We found, in general, that the attitudes students bring with them into the classroom contribute to two outcome measures, namely, learning as assessed by a pre- and postquiz and perceived self-reported benefits. While the GEP CURE produces positive outcomes overall, the students with more positive attitudes toward science, particularly with respect to epistemic beliefs, showed greater gains. The findings indicate the importance of a student’s epistemic beliefs to achieving positive learning outcomes.

Liver hepatocellular carcinoma (LIHC) is the major form of liver cancer that is the fourth most common cause of cancer death worldwide. It has been reported that the multifunctional protein p62 (also known as SQSTM1) plays a cancer-promoting role in LIHC, but the detailed mechanisms underlying p62 interaction with LIHC remains unclear. To gain a comprehensive understanding of p62 interaction with LIHC in clinical settings, we performed bioinformatic analyses using various online algorithms derived from high throughput profiling. Our results indicate that p62 expression is significantly upregulated, partially due to its promoter demethylation, rather than p62 gene mutation, in LIHC. Mutation of TP53, CTNNB1, or ALB significantly correlates with, and mutation of AXIN1 reversely correlates with, the p62 expression level. Its upregulation occurs as early as liver cirrhosis, and go through all stages of the carcinogenesis. HCV infection makes a significant contribution to p62 upregulation in LIHC. We further identified p62-associated molecular signatures in LIHC, including many genes that are involved in antioxidant stress and metabolism, such as SRX1 and TXNRD1. Regarding to the clinical outcome, p62 expression level reversely correlates with the survival of LIHC patients (p

Conservation translocations are increasingly used to help recover imperiled species. However, success of establishing populations remains low, especially for amphibians. Identifying factors associated with translocation success can help increase efficiency and efficacy of recovery efforts. Since the 1990s, several captive and semi-captive facilities have produced Chiricahua Leopard Frogs (Rana chiricahuensis) to establish or augment wild populations in Arizona and New Mexico, USA. During this same time, personnel associated with several programs surveyed translocation and non-translocation sites for presence of amphibians. We used 25 years (1995–2019) of survey and translocation data for the federally threatened Chiricahua Leopard Frog to identify factors linked with population persistence. Our dataset included approximately 40,642 egg masses or animals translocated in 314 events to 115 distinct sites and > 5800 visual encounter surveys from 641 sites; 120 of these sites were also surveyed with environmental DNA methods in 2018. We used a hierarchical dynamic occupancy model that accounted for imperfect detection to identify patch- and landscape-level attributes associated with site occupancy, and then used predictions from that model to evaluate factors associated with population persistence at translocation sites. Across all sites, extinction probability for Chiricahua Leopard Frogs was higher in lotic (stream) than lentic (pond) habitats and when Western Tiger Salamanders (Ambystoma mavortium) were present. Restoration of sites specifically for frog conservation reduced extinction probability. Colonization of unoccupied sites increased moderately with increasing numbers of translocation sites within 2 km, indicating a benefit of translocation efforts beyond sites where frogs were stocked. At translocation sites, persistence was greater in lentic than lotic habitats and was negatively correlated with the proportion of years tiger salamanders were present. Increasing numbers of translocation events, especially of late-stage larvae, increased persistence. There was little difference in population persistence based on whether stock was from captive, semi-captive, or wild sources, but translocations during the dry season (JanuaryJuly) succeeded more than those after the typical arrival of summer rains (AugustDecember). Based on the number of years translocation sites were predicted to be occupied, 2 or more translocations produced, on average, a > 4-yr increase in predicted occupancy compared to sites without translocations. While translocations have increased the number of populations across the landscape, continued management of water availability and threats such as invasive predators and disease remain critical to recovery of the Chiricahua Leopard Frog.

Abstract Genome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

Abstract Due to fear of short‐term toxicities, there is nonconsensus of hydroxycarbamide dosing strategy (escalated vs fixed‐dosing methods), which contributes to its suboptimal use. We performed a meta‐analysis to summarize the incidence rates of toxicities associated with both dosing methods. Summarized incidence rates could not be statistically compared between dosing methods due to sparse data. Summarized neutropenia and thrombocytopenia incidence rates were slightly higher when using escalated dosing than with fixed. Summarized reticulocytopenia was comparable. Summarized hepatic and renal toxicities’ incidence rates were slightly higher when using fixed doses than with escalated. We recommend diligent and transparent reporting of toxicities.

Abstract Background Whilst epidemiological studies have provided evidence of associations between certain risk factors and glioma onset, inferring causality has proven challenging. Using Mendelian randomization (MR), we assessed whether associations of 36 reported glioma risk factors showed evidence of a causal relationship. Methods We performed a systematic search of MEDLINE from inception to October 2018 to identify candidate risk factors and conducted a meta-analysis of two glioma genome-wide association studies (5739 cases and 5501 controls) to form our exposure and outcome datasets. MR analyses were performed using genetic variants to proxy for candidate risk factors. We investigated whether risk factors differed by subtype diagnosis (either glioblastoma (n = 3112) or non-glioblastoma (n = 2411)). MR estimates for each risk factor were determined using multiplicative random effects inverse-variance weighting (IVW). Sensitivity analyses investigated potential pleiotropy using MR-Egger regression, the weighted median estimator, and the mode-based estimator. To increase power, trait-specific polygenic risk scores were used to test the association of a genetically predicated increase in each risk factor with glioma onset. Results Our systematic search identified 36 risk factors that could be proxied using genetic variants. Using MR, we found evidence that four genetically predicted traits increased risk of glioma, glioblastoma or non-glioblastoma: longer leukocyte telomere length, liability to allergic disease, increased alcohol consumption and liability to childhood extreme obesity (> 3 standard deviations from the mean). Two traits decreased risk of non-glioblastoma cancers: increased low-density lipoprotein cholesterol (LDLc) and triglyceride levels. Our findings were similar across sensitivity analyses that made allowance for pleiotropy (genetic confounding). Conclusions Our comprehensive investigation provides evidence of a causal link between both genetically predicted leukocyte telomere length, allergic disease, alcohol consumption, childhood extreme obesity, and LDLc and triglyceride levels, and glioma. The findings from our study warrant further research to uncover mechanisms that implicate these traits in glioma onset.

Objectives Sickle cell disease (SCD) leads to chronic and acute complications that require specialised care to manage symptoms and optimise clinical results. The National Heart Lung and Blood Institute (NHLBI) evidence-based guidelines assist providers in caring for individuals with SCD, but adoption of these guidelines by providers has not been optimal. The objective of this study was to identify barriers to treating individuals with SCD.Methods The SCD Implementation Consortium aimed to investigate the perception and level of comfort of providers regarding evidence-based care by surveying providers in the regions of six clinical centres across the USA, focusing on non-emergency care from the providers’ perspective.Results Respondents included 105 providers delivering clinical care for individuals with SCD. Areas of practice were most frequently paediatrics (24%) or haematology/SCD specialist (24%). The majority (77%) reported that they were comfortable managing acute pain episodes while 63% expressed comfort with managing chronic pain. Haematologists and SCD specialists showed higher comfort levels prescribing opioids (100% vs 67%, p=0.004) and managing care with hydroxyurea (90% vs 51%, p=0.005) compared with non-haematology providers. Approximately 33% of providers were unaware of the 2014 NHLBI guidelines. Nearly 63% of providers felt patients’ medical needs were addressed while only 22% felt their mental health needs were met.Conclusions A substantial number of providers did not know about NHLBI’s SCD care guidelines. Barriers to providing care for patients with SCD were influenced by providers’ specialty, training and practice setting. Increasing provider knowledge could improve hydroxyurea utilisation, pain management and mental health support.

Abstract Humans modify landscapes for a variety of reasons including agriculture, urbanization, and to facilitate recreation. Landscape modifications can lead to complete loss of existing cover types and/or fragmentation of remaining cover types. Grasslands are one of the ecosystems most heavily impacted by human modifications; however, for many species, an understanding of how fragmentation affects population parameters is lacking. We investigated the influence of landscape fragmentation on survival, reproduction, and density using the northern bobwhite (Colinus virginianus), a facultative grassland species, as our case study. We experimentally manipulated the level of edge density (none, low, and high) of bobwhite management units and estimated seasonal survival, productivity, and autumn densities. Winter survival was lower in edge density treatments, relative to the control. An increase in migratory raptor density during the winter, along with a decrease in thermal refugia, may be contributing to lower winter survival in edge density treatments. Fecundity was higher in the edge density treatments, relative to the control, and was negatively related to winter survival. Similarly, breeding season survival was higher in more fragmented sites. This indicated a density‐dependent relationship, where lower bird densities at the beginning of the breeding season possibly led to greater resource availability, and thus survival and productivity, of the birds that remained. However, a density‐dependent response in breeding season survival and productivity was not enough to offset the negative impact of edges in the low edge density sites. Consequently, autumn density was lower relative to both control and high edge density treatments. Our study demonstrates the importance of capturing the entire life cycle when exploring the relationship between landscape pattern and population parameters.

Background: Hospitalized patients with diabetes are at risk of complications and longer length of stay (LOS). Inpatient Diabetes Management Services (IDMS) are known to be beneficial; however, their impact on patient care measures in community, non-teaching hospitals, is unknown. Objectives: To evaluate whether co-managing patients with diabetes by the IDMS team reduces LOS and 30-day readmission rate (30DR). Methods: This retrospective quality improvement cohort study analyzed LOS and 30DR among patients with diabetes admitted to a community hospital. The IDMS medical team consisted of an endocrinologist, nurse practitioner, and diabetes educator. The comparison group consisted of hospitalized patients with diabetes under standard care of attending physicians (mostly internal medicine-trained hospitalists). The relationship between study groups and outcome variables was assessed using Generalized Estimating Equation models. Results: 4,654 patients with diabetes (70.8 ± 0.2 years old) were admitted between January 2016 and May 2017. The IDMS team co-managed 18.3% of patients, mostly with higher severity of illness scores (p

Neuroimaging offers a valuable insight into human brain development by allowing in vivo assessment of structure, connectivity and function. Multimodal neuroimaging data have been obtained as part of three sub-studies within the Avon Longitudinal Study of Parents and Children, a prospective multigenerational pregnancy and birth cohort based in the United Kingdom. Brain imaging data were acquired when offspring were between 18 and 24 years of age, and included acquisition of structural, functional and magnetization transfer magnetic resonance, diffusion tensor, and magnetoencephalography imaging. This resource provides a unique opportunity to combine neuroimaging data with extensive phenotypic and genotypic measures from participants, their mothers, and fathers.

A hallmark of the research experience is encountering difficulty and working through those challenges to achieve success. This ability is essential to being a successful scientist, but replicating such challenges in a teaching setting can be difficult. The Genomics Education Partnership (GEP) is a consortium of faculty who engage their students in a genomics Course-Based Undergraduate Research Experience (CURE). Students participate in genome annotation, generating gene models using multiple lines of experimental evidence. Our observations suggested that the students’ learning experience is continuous and recursive, frequently beginning with frustration but eventually leading to success as they come up with defendable gene models. In order to explore our “formative frustration” hypothesis, we gathered data from faculty via a survey, and from students via both a general survey and a set of student focus groups. Upon analyzing these data, we found that all three datasets mentioned frustration and struggle, as well as learning and better understanding of the scientific process. Bioinformatics projects are particularly well suited to the process of iteration and refinement because iterations can be performed quickly and are inexpensive in both time and money. Based on these findings, we suggest that a dynamic of “formative frustration” is an important aspect for a successful CURE.

Introduction: Both converted and difficult laparoscopic cholecystectomies (LC) have impact on operating time and training of juniors. The aim of this study is to evaluate parameters that predict difficult LC or conversion (C), and find predictive values for different cut-off points of C-reactive protein (CRP) for conversion. Materials and Methods: A retrospective cohort study of cholecystectomies performed from January 2011 to December 2012 at NHS trust was undertaken. Association of intra-operative difficulties or conversion with the following factors was studied: Age, gender, CRP, white blood cell count (WBC), history of pancreatitis, and endoscopic retrograde cholangiopancreatography (ERCP). Results: Two hundred and ninety one patients were analysed (222 laparoscopic, 45 difficult LC and 24 C). Only 141 patients had a recorded CRP. Median CRP was highest for patients who were converted (286.20) compared to those who had difficult LC (67.40) or LC (7.05). Those patients who did not have preoperative CRP (8/150, 5.3%) had less chance of conversion than those who had CRP (16/141, 11.34%) (P = 0.063). Patients with CRP of ≤220 (3/91, 3.2%) had significantly less chance of conversion than those with CRP >220 (13/21, 61.9%) (P < 0.001). High preoperative CRP, WBC count and ERCP, were predictors of conversion. These factors were only marginally better than CRP alone in predicting conversion. Conclusion: CRP can be a strong predictor of conversion of LC. Further validation of the results is needed.

3D printing represents an emerging technology with significant potential to advance life-science education by allowing students to directly explore the relationship between macromolecular structure and function. In this article and supplemental video guide, we describe our development of a model-based instructional module on DNA supercoiling and outline practical tips for implementing models in undergraduate classrooms. We also present a procedure to design and print 3D dynamic models for classroom use. Furthermore, we describe repositories of 3D biomolecule files to make using models accessible and cost-effective.