Pedometer-Determined Step-Count Guidelines for Afterschool Programs

Background:Policies to require afterschool programs (ASPs, 3 PM to 6 PM) to provide children a minimum of 30 minutes of moderate-to-vigorous physical activity (MVPA) exist. With few low-cost, easy-to-use measures of MVPA available to the general public, ASP providers are limited in their ability to track progress toward achieving this policy-goal. Pedometers may fill this gap, yet there are no step-count guidelines for ASPs linked to 30 minutes of MVPA.Methods:Steps and accelerometer estimates of MVPA were collected concurrently over multiple days on 245 children (8.2 years, 48% boys, BMI-percentile 68.2) attending 3 community-based ASPs. Random intercept logit models and receiver operating characteristic (ROC) analyses were used to identify a threshold of steps that corresponded with attaining 30 minutes of MVPA.Results:Children accumulated an average of 2876 steps (standard error [SE] 79) and 16.1 minutes (SE0.5) of MVPA over 111 minutes (SE1.3) during the ASP. A threshold of 4600 steps provided high specificity (0.967) and adequate sensitivity (0.646) for discriminating children who achieved the 30 minutes of MVPA; 93% of the children were correctly classified. The total area under the curve was 0.919. Children accumulating 4600 steps were 25times more likely to accumulate 30 minutes of MVPA.Conclusions:This step threshold will provide ASP leaders with an objective, low-cost, easy-to-use tool to monitor progress toward policy-related goals.

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Development of Step-Count Cut Points for School-Day Vigorous Physical Activity

BioMed Research International ◽

10.1155/2018/9717848 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Ryan D. Burns ◽

Timothy A. Brusseau ◽

You Fu ◽

Peng Zhang

Keyword(s):

Physical Activity ◽

Vigorous Physical Activity ◽

Area Under The Curve ◽

Roc Curves ◽

Step Count ◽

Convenience Sample ◽

Cut Points ◽

Step Counts ◽

School Day ◽

The Usa

Background. No study has established step-count cut points for varying amounts of accelerometer-assessed vigorous physical activity (VPA) accrued during the school day in children. The purpose of this study was to establish step-count cut points for discriminating children meeting VPA in 5 minutes, 10 minutes, 15 minutes, and 20 minutes per 7-hour school day. Methods. Participants were a convenience sample of 1,053 children (mean age = 8.4 (1.8) years) recruited from 5 schools from the Mountain West region of the USA. Data within students were observed across multiple semesters totaling 2,119 separate observations. Step counts and time in VPA were assessed using ActiGraph wGT3X-BT triaxial accelerometers that were worn during the entirety of a 7-hour school day for one school week. Average censored step counts and minutes in VPA were calculated across 3 to 5 days. Receiver operating characteristic (ROC) curves were employed to derive step counts via calculation of the maximum Youden J statistic. Results. Area-under-the-curve (AUC) scores ranged from AUC = 0.81 (95% CI: 0.78–0.83; p<0.001) for meeting at least 5 minutes of VPA to AUC = 0.94 (95% CI: 0.88–1.00, p<0.001) for meeting at least 20 minutes of VPA. Approximately 3,460 steps best discriminated children meeting at least 5 minutes of VPA (sensitivity = 74.0%, specificity = 74.0%, and accuracy = 74.1%) and approximately 5,628 steps best discriminated children meeting at least 20 minutes per day of VPA (sensitivity = 85.7%, specificity = 95.1%, and accuracy = 95.1%). Conclusion. Step counts can discriminate with reasonable accuracy children that meet at least 5 minutes of school-day VPA and with strong accuracy children that meet 20 minutes of school-day VPA.

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Urinary MicroRNAs as Potential Markers for Non-Invasive Diagnosis of Bladder Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms21113814 ◽

2020 ◽

Vol 21 (11) ◽

pp. 3814 ◽

Cited By ~ 2

Author(s):

Kati Erdmann ◽

Karsten Salomo ◽

Anna Klimova ◽

Ulrike Heberling ◽

Andrea Lohse-Fischer ◽

...

Keyword(s):

Bladder Cancer ◽

Operating Characteristic ◽

Area Under The Curve ◽

High Specificity ◽

Specific Expression ◽

Predictive Values ◽

Non Invasive ◽

Diagnostic Potential ◽

Urinary Levels ◽

Sensitivity Specificity

Currently, voided urine cytology (VUC) serves as the gold standard for the detection of bladder cancer (BCa) in urine. Despite its high specificity, VUC has shortcomings in terms of sensitivity. Therefore, alternative biomarkers are being searched, which might overcome these disadvantages as a useful adjunct to VUC. The aim of this study was to evaluate the diagnostic potential of the urinary levels of selected microRNAs (miRs), which might represent such alternative biomarkers due to their BCa-specific expression. Expression levels of nine BCa-associated microRNAs (miR-21, -96, -125b, -126, -145, -183, -205, -210, -221) were assessed by quantitative PCR in urine sediments from 104 patients with primary BCa and 46 control subjects. Receiver operating characteristic (ROC) curve analyses revealed a diagnostic potential for miR-96, -125b, -126, -145, -183, and -221 with area under the curve (AUC) values between 0.605 and 0.772. The combination of the four best candidates resulted in sensitivity, specificity, positive and negative predictive values (NPV), and accuracy of 73.1%, 95.7%, 97.4%, 61.1%, and 80.0%, respectively. Combined with VUC, sensitivity and NPV could be increased by nearly 8%, each surpassing the performance of VUC alone. The present findings suggested a diagnostic potential of miR-125b, -145, -183, and -221 in combination with VUC for non-invasive detection of BCa in urine.

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Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

Energies ◽

10.3390/en14102960 ◽

2021 ◽

Vol 14 (10) ◽

pp. 2960

Author(s):

Marcus Vinícius Coelho Vieira da Costa ◽

Osmar Luiz Ferreira de Carvalho ◽

Alex Gois Orlandi ◽

Issao Hirata ◽

Anesmar Olino de Albuquerque ◽

...

Keyword(s):

Solar Energy ◽

Operating Characteristic ◽

Low Cost ◽

Computational Cost ◽

Area Under The Curve ◽

Semantic Segmentation ◽

Tropical Country ◽

Feature Pyramid ◽

Characteristic Area ◽

The Cost

Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach.

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End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

BMJ Innovations ◽

10.1136/bmjinnov-2021-000668 ◽

2021 ◽

Vol 7 (2) ◽

pp. 356-362

Author(s):

Harry Coppock ◽

Alex Gaskell ◽

Panagiotis Tzirakis ◽

Alice Baird ◽

Lyn Jones ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Low Cost ◽

Area Under The Curve ◽

Alternative Form ◽

Economic Damage ◽

Audio Signals ◽

Operating Characteristics ◽

Data Set ◽

Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

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Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

LaboratoriumsMedizin ◽

10.1515/labmed-2019-0152 ◽

2020 ◽

Vol 0 (0) ◽

Cited By ~ 1

Author(s):

Lei Xi ◽

Chunqing Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Operating Characteristic ◽

Meta Analysis ◽

Area Under The Curve ◽

Diagnostic Odds Ratio ◽

Diagnostic Value ◽

Receiver Operating Characteristic Curves ◽

Sensitivity Specificity ◽

Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

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Statistical Assessment of Discrimination Capabilities of a Fractional Calculus Based Image Watermarking System for Gaussian Watermarks

Entropy ◽

10.3390/e23020255 ◽

2021 ◽

Vol 23 (2) ◽

pp. 255

Author(s):

Mario Gonzalez-Lee ◽

Hector Vazquez-Leal ◽

Luis J. Morales-Mendoza ◽

Mariko Nakano-Miyatake ◽

Hector Perez-Meana ◽

...

Keyword(s):

Fractional Calculus ◽

Roc Curve ◽

Gaussian Noise ◽

False Positive ◽

Operating Characteristic ◽

Image Watermarking ◽

Area Under The Curve ◽

Statistical Assessment ◽

Watermarking Scheme ◽

Fractional Equation

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

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Snoring: a source of noise pollution and sleep apnea predictor

SLEEP ◽

10.1093/sleep/zsz305 ◽

2019 ◽

Vol 43 (6) ◽

Cited By ~ 4

Author(s):

Mudiaga Sowho ◽

Francis Sgambati ◽

Michelle Guzman ◽

Hartmut Schneider ◽

Alan Schwartz

Keyword(s):

Sleep Apnea ◽

Operating Characteristic ◽

Area Under The Curve ◽

Noise Pollution ◽

Apnea Hypopnea Index ◽

Sleep Study ◽

Sound Levels ◽

Obstructive Sleep ◽

Single Night ◽

The Relationship

Abstract Snoring is a highly prevalent condition associated with obstructive sleep apnea (OSA) and sleep disturbance in bed partners. Objective measurements of snoring in the community, however, are limited. The present study was designed to measure sound levels produced by self-reported habitual snorers in a single night. Snorers were excluded if they reported nocturnal gasping or had severe obesity (BMI > 35 kg/m2). Sound was measured by a monitor mounted 65 cm over the head of the bed on an overnight sleep study. Snoring was defined as sound ≥40 dB(A) during flow limited inspirations. The apnea hypopnea index (AHI) and breath-by-breath peak decibel levels were measured. Snore breaths were tallied to determine the frequency and intensity of snoring. Regression models were used to determine the relationship between objective measures of snoring and OSA (AHI ≥ 5 events/h). The area under the curve (AUC) for the receiver operating characteristic (ROC) was used to predict OSA. Snoring intensity exceeded 45 dB(A) in 66% of the 162 participants studied, with 14% surpassing the 53 dB(A) threshold for noise pollution. Snoring intensity and frequency were independent predictors of OSA. AUCs for snoring intensity and frequency were 77% and 81%, respectively, and increased to 87% and 89%, respectively, with the addition of age and sex as predictors. Snoring represents a source of noise pollution in the bedroom and constitutes an important target for mitigating sound and its adverse effects on bed partners. Precise breath-by-breath identification and quantification of snoring also offers a way to risk stratify otherwise healthy snorers for OSA.

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Soluble SIGLEC5: A New Prognosis Marker in Colorectal Cancer Patients

Cancers ◽

10.3390/cancers13153896 ◽

2021 ◽

Vol 13 (15) ◽

pp. 3896

Author(s):

Karla Montalbán-Hernández ◽

Ramón Cantero-Cid ◽

Roberto Lozano-Rodríguez ◽

Alejandro Pascual-Iglesias ◽

José Avendaño-Ortiz ◽

...

Keyword(s):

Colorectal Cancer ◽

Operating Characteristic ◽

Prognostic Markers ◽

Characteristic Curve ◽

Area Under The Curve ◽

Significant Heterogeneity ◽

Kaplan Meier ◽

Prognosis Marker ◽

Colorectal Cancer Patients ◽

Healthy Participants

Colorectal cancer (CRC) is the second most deadly and third most commonly diagnosed cancer worldwide. There is significant heterogeneity among patients with CRC, which hinders the search for a standard approach for the detection of this disease. Therefore, the identification of robust prognostic markers for patients with CRC represents an urgent clinical need. In search of such biomarkers, a total of 114 patients with colorectal cancer and 67 healthy participants were studied. Soluble SIGLEC5 (sSIGLEC5) levels were higher in plasma from patients with CRC compared with healthy volunteers. Additionally, sSIGLEC5 levels were higher in exitus than in survivors, and the receiver operating characteristic curve analysis revealed sSIGLEC5 to be an exitus predictor (area under the curve 0.853; cut-off > 412.6 ng/mL) in these patients. A Kaplan–Meier analysis showed that patients with high levels of sSIGLEC5 had significantly shorter overall survival (hazard ratio 15.68; 95% CI 4.571–53.81; p ≤ 0.0001) than those with lower sSIGLEC5 levels. Our study suggests that sSIGLEC5 is a soluble prognosis marker and exitus predictor in CRC.

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Identification of an optimal prolactin threshold to determine prolactinoma size using receiver operating characteristic analysis

Scientific Reports ◽

10.1038/s41598-021-89256-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bianca M. Leca ◽

Maria Mytilinaiou ◽

Marina Tsoli ◽

Andreea Epure ◽

Simon J. B. Aylwin ◽

...

Keyword(s):

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Tumour Size ◽

Area Under The Curve ◽

Serum Prolactin ◽

Pituitary Neoplasms ◽

Characteristic Analysis ◽

Roc Curve Analysis ◽

Baseline Serum ◽

Receiver Operating

AbstractProlactinomas represent the most common type of secretory pituitary neoplasms, with a therapeutic management that varies considerably based on tumour size and degree of hyperprolactinemia. The aim of the current study was to evaluate the relationship between serum prolactin (PRL) concentrations and prolactinoma size, and to determine a cut-off PRL value that could differentiate micro- from macro-prolactinomas. A retrospective cohort study of 114 patients diagnosed with prolactinomas between 2007 and 2017 was conducted. All patients underwent gadolinium enhanced pituitary MRI and receiver operating characteristic (ROC) analyses were performed. 51.8% of patients in this study were men, with a mean age at the time of diagnosis of 42.32 ± 15.04 years. 48.2% of the total cohort were found to have microadenomas. Baseline serum PRL concentrations were strongly correlated to tumour dimension (r = 0.750, p = 0.001). When performing the ROC curve analysis, the area under the curve was 0.976, indicating an excellent accuracy of the diagnostic method. For a value of 204 μg/L (4338 mU/L), sensitivity and specificity were calculated at 0.932 and 0.891, respectively. When a cut off value of 204 μg/L (4338 mU/L) was used, specificity was 93.2%, and sensitivity 89.1%, acceptable to reliably differentiate between micro- and macro- adenomas.

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Prediction of anemia at delivery

Scientific Reports ◽

10.1038/s41598-021-85622-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Enav Yefet ◽

Avishag Yossef ◽

Zohar Nachum

Keyword(s):

Risk Factors ◽

Operating Characteristic ◽

Complete Blood Count ◽

Characteristic Curve ◽

Secondary Analysis ◽

Area Under The Curve ◽

Iron Supplement ◽

Independent Risk Factors ◽

Operating Characteristic Curve ◽

Study Database

AbstractWe aimed to assess risk factors for anemia at delivery by conducting a secondary analysis of a prospective cohort study database including 1527 women who delivered vaginally ≥ 36 gestational weeks. Anemia (Hemoglobin (Hb) < 10.5 g/dL) was assessed at delivery. A complete blood count results during pregnancy as well as maternal and obstetrical characteristics were collected. The primary endpoint was to determine the Hb cutoff between 24 and 30 gestational weeks that is predictive of anemia at delivery by using the area under the curve (AUC) of the receiver operating characteristic curve. Independent risk factors for anemia at delivery were assessed using stepwise multivariable logistic regression. Hb and infrequent iron supplement treatment were independent risk factors for anemia at delivery (OR 0.3 95%CI [0.2–0.4] and OR 2.4 95%CI [1.2–4.8], respectively; C statistics 83%). Hb 10.6 g/dL was an accurate cutoff to predict anemia at delivery (AUC 80% 95%CI 75–84%; sensitivity 75% and specificity 74%). Iron supplement was beneficial to prevent anemia regardless of Hb value. Altogether, Hb should be routinely tested between 24 and 30 gestational weeks to screen for anemia. A flow chart for anemia screening and treatment during pregnancy is proposed in the manuscript.Trial registration: ClinicalTrials.gov Identifier: NCT02434653.

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