Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods.

Pressure support ventilation (PSV) should be titrated considering the pressure developed by the respiratory muscles (Pmusc) to prevent under- and over-assistance. The esophageal pressure (Pes) is the clinical gold standard for Pmusc assessment, but its use is limited by alleged invasiveness and complexity. The least square fitting method and the end-inspiratory occlusion method have been proposed as non-invasive alternatives for Pmusc assessment. The aims of this study were: (1) to compare the accuracy of Pmusc estimation using the end-inspiration occlusion (Pmusc,index) and the least square fitting (Pmusc,lsf) against the reference method based on Pes; (2) to test the accuracy of Pmusc,lsf and of Pmusc,index to detect overassistance, defined as Pmusc ≤ 1 cmH2O. We studied 18 patients at three different PSV levels. At each PSV level, Pmusc, Pmusc,lsf, Pmusc,index were calculated on the same breaths. Differences among Pmusc, Pmusc,lsf, Pmusc,index were analyzed with linear mixed effects models. Bias and agreement were assessed by Bland-Altman analysis for repeated measures. The ability of Pmusc,lsf and Pmusc,index to detect overassistance was assessed by the area under the receiver operating characteristics curve. Positive and negative predictive values were calculated using cutoff values that maximized the sum of sensitivity and specificity. At each PSV level, Pmusc,lsf was not different from Pmusc (p = 0.96), whereas Pmusc,index was significantly lower than Pmusc. The bias between Pmusc and Pmusc,lsf was zero, whereas Pmusc,index systematically underestimated Pmusc of 6 cmH2O. The limits of agreement between Pmusc and Pmusc,lsf and between Pmusc and Pmusc,index were ± 12 cmH2O across bias. Both Pmusc,lsf ≤ 4 cmH2O and Pmusc,index ≤ 1 cmH2O had excellent negative predictive value [0.98 (95% CI 0.94-1) and 0.96 (95% CI 0.91-0.99), respectively)] to identify over-assistance. The inspiratory effort during PSV could not be accurately estimated by the least square fitting or end-inspiratory occlusion method because the limits of agreement were far above the signal size. These non-invasive approaches, however, could be used to screen patients at risk for absent or minimal respiratory muscles activation to prevent the ventilator-induced diaphragmatic dysfunction.

A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India.

We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 30May 2020. The predictive capability of the model has been validated with real data of infection cases reported during June 1-10, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India, as a whole, is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India, as a whole, could see the peak and end of the epidemic in the month of July 2020 and March 2021 respectively as per the current trend in the data. Active infected cases in India may touch 2 lakhs or little above at the peak time and total infected cases may reach over 19 lakhs as per current trend. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from the national COVID19 dash board on daily basis and updates the model input parameters and predictions instantaneously. This real time application can be accessed from the link: https://docs.google.com/spreadsheets/d/1fCwgnQ-dz4J0YWVDHUcbEW1423wOJjdEXm8TqJDWNAk/edit?usp=sharing and can serve as a practical tool for policy makers to track peak time and maximum active infected cases based on latest trend in data for medical readiness and taking epidemic management decisions.

The Region of Interest in Boundary Calibration for Palatal Rugae Image of Forensic Identification.

ABSTRACT: Objective To examine the method of least-square fitting for calibrating the palatal rugae boundary. Methods According to the distribution of the teeth, the feature points were selected; when they were fit, the boundary of the palatal rugae area was created, thereby constructing a mask. The mask was used to remove the part located outside the boundary and filter out the interference. Three samples were utilized for the experiments and analyses to come. Results To evaluate the quantitative results of the fitting curves, the correlation coefficients （r） of the samples and the relationship between the actual mean value and ideal mean value was obtained through six fitting processes. The differences between the actual mean and ideal mean were found to be significantly small （from 0.285 7 to 2.500 0） in the six fitting processes, with the range of r close to 1 （from 0.989 6 to 0.999 5）. Conclusion The effect of the cubic polynomial fitting method adopted in this study was stable.The proposed boundary calibration method can effectively locate the palatal rugae boundary and remove the interference area, further promoting the practice of forensic identification.

An open library of relativistic core electron density function for the QTAIM analysis with pseudopotentials.

Based on two-component relativistic atomic calculations, a free electron density function (EDF) library has been developed for nearly all the known ECPs of the elements Li (Z = 3) up to Ubn (Z = 120), which can be interfaced into modern quantum chemistry programs to save the .wfx wavefunction file. The applicability of this EDF library is demonstrated by the analyses of the quantum theory of atoms in molecules (QTAIM) and other real space functions on HeCuF, PtO42+, OgF4 , and TlCl3 (DMSO)2 . When a large-core ECP is used, it shows that the corrections by EDF may significantly improve the properties of some density-derived real space functions, but they are invalid for the wavefunction-depending real space functions. To classify different chemical bonds and especially some nonclassical interactions, a list of universal criteria has also been proposed. © 2018 Wiley Periodicals, Inc.

A fast cross-validation method for alignment of electron tomography images based on Beer-Lambert law.

In electron tomography, accurate alignment of tilt series is an essential step in attaining high-resolution 3D reconstructions. Nevertheless, quantitative assessment of alignment quality has remained a challenging issue, even though many alignment methods have been reported. Here, we report a fast and accurate method, tomoAlignEval, based on the Beer-Lambert law, for the evaluation of alignment quality. Our method is able to globally estimate the alignment accuracy by measuring the goodness of log-linear relationship of the beam intensity attenuations at different tilt angles. Extensive tests with experimental data demonstrated its robust performance with stained and cryo samples. Our method is not only significantly faster but also more sensitive than measurements of tomogram resolution using Fourier shell correlation method (FSCe/o). From these tests, we also conclude that while current alignment methods are sufficiently accurate for stained samples, inaccurate alignments remain a major limitation for high resolution cryo-electron tomography.

An improved linear prediction evolution algorithm based on topological opposition-based learning for optimization.

Prediction-based evolutionary algorithm is one of the emerging category of meta-heuristic optimization techniques. The improved linear prediction evolution algorithm (ILPE) is a recently developed meta-heuristic optimization technique that draws inspiration from non-linear least-square fitting models. This article implements the concept of topological opposition-based learning, which was first applied in grey prediction evolutionary algorithms to the ILPE. In traditional evolutionary algorithms, after employing the mutation and crossover operator, it generates trial populations. The proposed algorithm constructs a new reproduction operator using the non-linear least square fitting model with topological opposition-based learning to generate trial individuals. This reproduction operator considers the population series as a time series and uses the topological opposition-based non-linear least square fitting model to predict the next generation of populations. The efficiency and accuracy of the algorithm are demonstrated through numerical experiments on CEC2014 and CEC2017 benchmark functions. The results of these experiments show that the proposed algorithm is highly effective in solving optimization problems.•An improved linear prediction evolution algorithm based on topological opposition based learning (TILPE) is proposed.•The proposed strategy treat the the population series as a time series.•To validate the efficacy of TILPE, CEC2014 and CEC2017 benchmark functions are used.

Variances of Cylinder Parameters Fitted to Range Data.

Industrial pipelines are frequently scanned with 3D imaging systems (e.g., LADAR) and cylinders are fitted to the collected data. Then, the fitted as-built model is compared with the as-designed model. Meaningful comparison between the two models requires estimates of uncertainties of fitted model parameters. In this paper, the formulas for variances of cylinder parameters fitted with Nonlinear Least Squares to a point cloud acquired from one scanning position are derived. Two different error functions used in minimization are discussed: the orthogonal and the directional function. Derived formulas explain how some uncertainty components are propagated from measured ranges to fitted cylinder parameters.

Optimal Validated Multi-Factorial Climate Change Risk Assessment for Adaptation Planning and Evaluation of Infectious Disease: A Case Study of Dengue Hemorrhagic Fever in Indonesia.

(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate the risk assessment model by best-fitting it with the number of dengue cases per province using the least-square fitting method. Seven out of thirty-four provinces in Indonesia were chosen (North Sumatra, Jakarta Capital, West Java, Central Java, East Java, Bali and East Kalimantan). (3) Results: A risk assessment based on the number of dengue cases showed an increased risk in 2010, 2015 and 2016 in which the effects of El Nino and La Nina extreme climates occurred. North Sumatra, Bali, and West Java were more influenced by the vulnerability component, in line with their risk analysis that tends to be lower than the other provinces in 2010, 2015 and 2016 when El Nino and La Nina occurred. (4) Conclusion: Based on data from the last ten years, in Jakarta Capital, Central Java, East Java and East Kalimantan, dengue risks were mainly influenced by the climatic hazard component while North Sumatra, Bali and West Java were more influenced by the vulnerability component.

Empirical formulation for small circular electron fields.

An empirical model for small circular electron fields was developed. This can be of great help in the treatment planning process for small circular electron fields. A complete dosimetric analysis of the circular fields defined by electron cutouts diameters (2 cm-9 cm) was done for nominal electron energies ranging between 6 MeV and 20 MeV using a 3D water phantom and a pin-point ion chamber. Properties studied included depth dose, in-air inverse-square fall-off, and beam profiles. The Varian Clianc 2100 C accelerator was modelled, benchmarked and Monte Carlo simulations were performed using the EGSnrc/BEAMnrc code for the small circular cutouts. A simple exponential model was found to accurately predict the very important therapeutic depth (90% of Dmax) for the small circular field size within an accuracy of better than 2 mm in most cases. The model has only two parameters (d90and 'b'). Also, the penumbra widths (90% of the off-axis profiles) of these small circular electron fields were studied and least square fitted to a simple quadratic model. Full dosimetric profiles of these small circular electron fields were further studied using the benchmarked Monte Carlo simulations. This study presents a simple model to predict the very important therapeutic depth (90% of Dmax) and a recipe to develop such an electron treatment model for any linear accelerator system. Such predictions can be extremely valuable and time saving prior to treatment planning involving not only small circular shaped electron fields but also irregularly shaped electron fields.

A Mathematical Method for Electromyography Analysis of Muscle Functions during Yogasana.

CONTEXT: For the past few decades, the number of people practicing yoga is increasing in number. Yogasanas need smooth body movements in the process of attaining defined postures that the person must hold on to activate specific muscles of the body related to that asana. Yogasanas should be performed with perfection to derive maximum benefits. OBJECTIVE: The objective of this study was to introduce a mathematical method to understand muscle functionalities while doing Yogasanas. MATERIALS AND METHODS: Used Delsys surface electromyography (sEMG) - Trigno™ (Delsys Inc.) sensors for data recording and analyzing muscle activation patterns. RESULTS: Performance analysis was quantified using normalized sEMG signals. The sEMG data during final posture were fit to a straight line using linear regression analysis. CONCLUSION: The results suggested that the slope of the best fit line is a good metric for monitoring the muscle activity during Yoga performance. The advantages of this method are the slope of the line is a good indicator for monitoring the muscle activity while doing Yogasana and the method suggested in this study can be extended for analyzing other asanas as well.

Accelerated PET kinetic maps estimation by analytic fitting method.

In this work, we propose and test a new approach for non-linear kinetic parameters' estimation from dynamic PET data. A technique is discussed, to derive an analytical closed-form expression of the compartmental model used for kinetic parameters' evaluation, using an auxiliary parameter set, with the aim of reducing the computational burden and speeding up the fitting of these complex mathematical expressions to noisy TACs. Two alternative algorithms based on numeric calculations are considered and compared to the new proposal. We perform a simulation study aimed at (i) assessing agreement between the proposed method and other conventional ways of implementing compartmental model fitting, and (ii) quantifying the reduction in computational time required for convergence. It results in a speed-up factor of ∼120 when compared to a fully numeric version, or ∼38, with respect to a more conventional implementation, while converging to very similar values for the estimated model parameters. The proposed method is also tested on dynamic 3D PET clinical data of four control subjects. The results obtained supported those of the simulation study, and provided input and promising perspectives for the application of the proposed technique in clinical practice.