Visualization 2.wav

The audio file of gathered signal with a 3-meter tail fiber (6-meter equivalent sensing length) in the experiment after noise reduction and filtration when applying 70~80 dB sound

Visualization 1.wav

The audio file of the source used in the experiment

Visualization 3.wav

The audio file of gathered signal with a 3-meter tail fiber (6-meter equivalent sensing length) in the experiment after noise reduction and filtration when applying 60~70 dB sound

Visualization 4.wav

The audio file of gathered signal with a 3-meter tail fiber (6-meter equivalent sensing length) in the experiment after noise reduction and filtration when applying 50~60 dB sound

Image_1_Elevated plasma D-dimer levels are associated with the poor prognosis of critically ill children.JPEG

BackgroundD-dimer has been shown as a valuable predictor for the prognosis of sepsis. But the prognostic association of an elevated D-dimer with adverse outcomes of all critical illnesses in pediatric intensive care unit (PICU) has received far less emphasis.MethodsThis was a single-center retrospective study, including 7,648 critical patients aged between 28 days and 18 years from the pediatric intensive care (PIC) database from 2010 to 2018. The primary outcome was the in-hospital mortality rate.ResultsHigher levels of D-dimer, INR, PT, APTT, and lower Fib were observed in the non-survivor group (all P ConclusionsWe found poorer coagulation function in the non-survivors compared with the survivors. Among the coagulation indicators, D-dimer was most strongly associated with in-hospital mortality of unselected critically ill children.</p

Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation

<p>Quality control of multivariate processes has been extensively studied in the past decades; however, fundamental challenges still remain due to the complexity and the decision-making challenges that require not only sensitive fault detection but also identification of the truly out-of-control variables. In existing approaches, fault detection and diagnosis are considered as two separate tasks. Recent developments have revealed that selective monitoring of the potentially out-of-control variables, identified by a variable selection procedure combined with the process monitoring method, could lead to promising performances. Following this line, we propose the diagnostic monitoring that takes an additional step on from the selective monitoring idea and directs the monitoring effort on the potentially out-of-control variables. The identification of the truly out-of-control variables can be achieved by integrating the process monitoring formulation with process cascade knowledge represented by a Bayesian Network. Computationally efficient algorithms are developed for solving the optimization formulation with connection to the Least Absolute Shrinkage and Selection Operator (LASSO) problem being identified. Both theoretical analysis and extensive experiments on a simulated data set and real-world applications are conducted that show the superior performance.</p

MVPA activities in Beijing school students by gender and grade.

<p>Abbreviations: MVPA, moderate to vigorous physical activity; IQR = interquartile range, computed for only those reporting any (>0 min).</p><p>^a Descriptive statistics were calculated for total reported time in MVPA (sum of in-school, and outside school). Descriptive statistics herein are therefore presented as the proportion reporting any defined MVPA and the median and interquartile range (IQR) of distribution of time (min/week) for those reporting any of MVPA. ^a gender difference regardless of grade</p><p>^b grade difference within gender</p><p>^c gender difference within grade.</p><p>* <i>P</i><0.05</p><p>** <i>P</i><0.01</p><p>*** <i>P</i><0.001</p><p>MVPA activities in Beijing school students by gender and grade.</p

Correlates of spending more than 2 hours/day of screen time outside school and spending more than 1 hour/day of MVPA in and outside school^a.

<p>Abbreviations: MVPA, moderate to vigorous physical activity.</p><p>^a Stepwise backward elimination SURVEYLOGISTCI regression was used to model the correlates initially from a full model with all variables in the left column in the Table</p><p>^b Computer use + TV/video watching + video games</p><p>^c The total MVPA in and outside of school</p><p>^d 0–100 scale, per 10 units</p><p>Correlates of spending more than 2 hours/day of screen time outside school and spending more than 1 hour/day of MVPA in and outside school<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133544#t005fn002" target="_blank">^a</a>.</p

Prevalence of physical activity and sedentary activity in Beijing school students by school.

<p>Abbreviations: MVPA, moderate to vigorous physical activity; TV, television.</p><p>^aTotal MVPA: the total MVPA in and outside of school</p><p>^b Screen time: computer use + TV/video watching + video games</p><p>*<i>P</i> < 0.05</p><p>** <i>P</i> < 0.01</p><p>*** <i>P</i> < 0.001</p><p>Prevalence of physical activity and sedentary activity in Beijing school students by school.</p

Additional file 1: of High-resolution and accelerated multi-parametric mapping with automated characterization of vessel disease using intravascular MRI

Supplementary file. (PDF 2140 kb