Waltermacdonald3497

The best performance was obtained from Deformable Convolution, a variant of Faster R-CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility from Frank-Starling curves could potentially make it much easier to diagnose and manage circulatory failure.

This study combines non-additionally invasive model-based methods to estimate left ventricle end-diastolic volume (LEDV) and stroke volume (SV) during hemodynamic interventions in a pig trial (N=6). Agreement of model-based LEDV and measured admittance catheter LEDV is assessed. Model-based LEDV and SV are used to identify response to hemodynamic interventions and create Frank-Starling curves, from which Frank-Starling contractility (FSC) is identified as the gradient.

Model-based LEDV had good agreement with measured admittance catheter LEDV, with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 2.2ml [-13.8, 22.5]. Model LEDV and SV were used to identify non-responsive interventions with a good area under the receiver-operating characteristic (ROC) curve of 0.83. FSC was identified using model LEDV and SV with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 0.07 [-0.68, 0.56], with FSC from admittance catheter LEDV and aortic flow probe SV used as a reference method.

This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside.

This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside.The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used UK Radiotherapy Machine Learning Network.Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions.Mutations in K-Ras are involved in a large number of all human cancers, thus, K-Ras is regarded as a promising target for anticancer drug design. Understanding the target roles of K-Ras is important for providing insights on the molecular mechanism underlying the conformational transformation of the switch domains in K-Ras due to mutations. In this study, multiple replica Gaussian accelerated molecular (MR-GaMD) simulations and principal component analysis (PCA) were applied to probe the effect of G13A, G13D and G13I mutations on conformational transformations of the switch domains in GDP-associated K-Ras. The results suggest that G13A, G13D and G13I enhance the structural flexibility of the switch domains, change the correlated motion modes of the switch domains and strengthen the total motion strength of K-Ras compared with the wild-type (WT) K-Ras. Free energy landscape analyses not only show that the switch domains of the GDP-bound inactive K-Ras mainly exist as a closed state but also indicate that mutations evidently alter the free energy profile of K-Ras and affect the conformational transformation of the switch domains between the closed and open states. Analyses of hydrophobic interaction contacts and hydrogen bonding interactions show that the mutations scarcely change the interaction network of GDP with K-Ras and only disturb the interaction of GDP with the switch (SW1). In summary, two newly introduced mutations, G13A and G13I, play similar adjustment roles in the conformational transformations of two switch domains to G13D and are possibly utilized to tune the activity of K-Ras and the binding of guanine nucleotide exchange factors.When processing sparse-spectrum biomedical signals, traditional time-frequency (TF) analysis methods are faced with the defects of blurry energy concentration and low TF resolution caused by the Heisenberg uncertainty principle. The synchrosqueezing-based methods have demonstrated advanced TF performances in recent studies. However, these methods contain at least three drawbacks (1) existence of non-reassigned points (NRPs), (2) low noise robustness, and (3) low amplitude accuracy. In this study, the novel TF method, termed multi-synchrosqueezing extracting transform (MSSET), is proposed to address these limitations. The proposed MSSET is divided into three steps. First, multisynchrosqueezing transform (MSST) is performed with specific iterations. Second, a synch-extracting is applied to retain the TF distribution of MSST results that relate most to time-varying information of the raw signal; meanwhile, the other smeared TF energy is discarded. Finally, the MSSET result is obtained by rounding the adjacent results at the frequency plane. Numerical verification results show that the proposed MSSET method can effectively solve the NRPs problem and enhance noise robustness. Furthermore, while retaining superior energy concentration and signal reconstruction capability, the MSSET's amplitude accuracy reaches about 90%, significantly higher than other methods. Proteasome inhibitor In the same conditions, the MSSET even consumes less time than MSST and IMSST. It also achieves the best composite performance with the least amplitude accuracy-time cost ratio (ATCR) index. Actual application examples in the Bat signal and the electrocardiograph (ECG) signal also validate the excellent performances of our method. To conclude, our proposed MSSET is superior to state-of-the-art methods and is expected to be widely used in the sparse-spectrum biomedical signal.We present evidence on the impacts of a large-scale iodine supplementation program in Tanzania on individuals' long-term economic outcomes. Exploiting the timing and location of the intervention, we document that in utero exposure to the program increased completed years of education and income scores in adulthood. We find no increase in total employment, but a significant change in the occupational structure. Cohorts exposed to the program are less likely to work in agricultural self-employment and more likely to hold skilled jobs that typically demand higher levels of education. Together, these results demonstrate that iodine deficiency can have long-run implications for occupational choices and labor market incomes in low-income regions.

The aim of this study was to evaluate the impact of different therapy regimens, including sodium oxybate (SXB)-containing regimens, on patient-reported outcomes (PROs) in people with narcolepsy.

Online surveys were used to collect information from persons with narcolepsy in the Nexus Narcolepsy Registry. Surveys contained questionnaires assessing self-reported sleep quality (SQ; via single question), daytime sleepiness and function (Epworth Sleepiness Scale and Functional Outcomes of Sleep Questionnaire), health-related quality of life (HRQoL; 36-Item Short Form Health Survey [SF-36]), work productivity and impairment (Work Productivity and Activity Impairment Specific Health Problem), and history of injuries or motor vehicle accidents. Treatment with SXB (including monotherapy or combination therapy; SXB group) was compared with non-SXB therapy (No SXB group). The P values presented are nominal, as there are no adjustments for multiplicity.

From June 2015 through December 2017, 983 participants completed 1760 surveys. SQ and daytime functioning scores were better in the SXB group compared with the No SXB group (all P<0.001). HRQoL scores were better for the SXB group compared with the No SXB group for the SF-36 Physical Component (P=0.016), Mental Component (P<0.001), and all 8 subscales. Additionally, PROs were better for the SXB group for presenteeism, overall work and activity impairment, and risk of motor vehicle accidents (all P≤0.001).

Based on participants' self-assessments, treatment regimens with SXB were associated with better outcomes than regimens not containing SXB across many PROs, including SQ, HRQoL, work and activities, and risk of traffic accidents. CLINICALTRIALS.

NCT02769780.

NCT02769780.

A bi-directional relationship between technology use and adolescent sleep is likely, yet findings are mixed, and it is not known whether parental control of technology use can protect sleep. The current study examined bi-directionality between technology use on school nights and morning/eveningness, sleep duration and daytime sleepiness in early adolescents. We also examined whether time spent using technology mediated the relationship between parental control of technology and adolescent sleep.

Adolescents and their primary caregiver (96% mothers) completed questionnaire measures of sleep, technology use and parental control across three, annual waves Wave 1 (N=528, M

=11.18, SD=0.56, range=10-12, 51% male), Wave 2 (N=502, M

=12.19, SD=0.53, 52% male) and Wave 3 (N=478, M

=13.19, SD=0.53, 52% male).

When examining the direct relationship between sleep and technology use, cross-lagged panel models showed that time spent using technology predicted shorter sleep duration and greater daytime sleepiness in adolescence, and evening diurnal preference and shorter sleep duration contributed to increased technology use over time.