Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.

Predicting partition coefficients for the SAMPL7 physical property challenge using the ClassicalGSG method

The prediction of [Formula: see text] values is one part of the statistical assessment of the modeling of proteins and ligands (SAMPL) blind challenges. Here, we use a molecular graph representation method called Geometric Scattering for Graphs (GSG) to transform atomic attributes to molecular features. The atomic attributes used here are parameters from classical molecular force fields including partial charges and Lennard-Jones interaction parameters. The molecular features from GSG are used as inputs to neural networks that are trained using a "master" dataset comprised of over 41,000 unique [Formula: see text] values. The specific molecular targets in the SAMPL7 [Formula: see text] prediction challenge were unique in that they all contained a sulfonyl moeity. This motivated a set of ClassicalGSG submissions where predictors were trained on different subsets of the master dataset that are filtered according to chemical types and/or the presence of the sulfonyl moeity. We find that our ranked prediction obtained 5th place with an RMSE of 0.77 [Formula: see text] units and an MAE of 0.62, while one of our non-ranked predictions achieved first place among all submissions with an RMSE of 0.55 and an MAE of 0.44. After the conclusion of the challenge we also examined the performance of open-source force field parameters that allow for an end-to-end [Formula: see text] predictor model: General AMBER Force Field (GAFF), Universal Force Field (UFF), Merck Molecular Force Field 94 (MMFF94) and Ghemical. We find that ClassicalGSG models trained with atomic attributes from MMFF94 can yield more accurate predictions compared to those trained with CGenFF atomic attributes.

An idea to explore: Visualization of ionization of amino acids using Mathematica

Ionization of amino acids (AA) is very important concept in biochemistry. We integrate the mathematical concept of probability with biochemically relevant process of AA ionization. We visualize the ionization process with Mathematica software discussing intramolecular interactions between weakly acidic/basic functional groups and charge-pH variation of amino acids in water solution. The visualizations rely on the notion of probability of ionization of functional groups and demonstrate how the extent of ionization and charge varies with pH of the solution. The examples described include amino acids and weak diprotic acids and bases. The aim is to help students better appreciate the importance and consequences of AA ionization and correct some misconceptions.

Predictors of literacy in adulthood: Evidence from 33 countries

What makes a literate person? What leads to literacy gains and losses within and between individuals and countries? This paper provides new evidence that helps answer these questions. The present comparative analysis of literacy is based on large representative samples from the Survey of Adult Skills conducted in 33 countries, with 25-65 year old participants. We provide, for the first time, estimates of relative importance for a comprehensive set of experiential factors, motivations, incentives, parental influence, demands of workplace, and other predictors of influence. We sketch a configuration of factors that predicts an "ideal" reader, i.e., the optimal literacy performance. Moreover, we discover a pivotal role of the age effect in predicting variability between countries. Countries with the highest literacy scores are the ones where literacy decreases with age the most strongly. We discuss this finding against current accounts of aging effects, cohort effects and others. Finally, we provide methodological recommendations for experimental studies of aging in cognitive tasks like reading.

Estimation of the Number of General Anesthesia Cases Based on a Series of Nationwide Surveys on Twitter during COVID-19 Pandemic in Japan: A Statistical Analysis

Background and objectives: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread to more than 200 countries. In light of this situation, the Japanese Government declared a state of emergency in seven regions of Japan on 7 April 2020 under the provisions of the law. The medical care delivery system has been under pressure. Although various surgical societies have published guidelines on which to base their surgical decisions, it is not clear how general anesthesia has been performed and will be performed in Japan. Materials and Methods: One of the services provided by the social network service Twitter is a voting function—Twitter Polls—through which anonymous surveys were conducted. We analyzed the results of a series of surveys 17 times over 22 weeks on Twitter on the status of operating restrictions using quadratic programming to solve the mathematical optimizing problem, and public data provided by the Japanese Government were used to estimate the current changes in the number of general anesthesia performed in Japan. Results: The minimum number of general anesthesia cases per week was estimated at 67.1% compared to 2015 on 27 April 2020. The timeseries trend was compatible with the results reported by the Japanese Society of Anesthesiologists (correlation coefficient r = 0.69, p < 0.001). Conclusions: The number of general anesthesia was reduced up to two-thirds during the pandemic of COVID-19 in Japan and was successfully quantitatively estimated using a quick questionnaire on Twitter.

Minimizing disease spread on a quarantined cruise ship: A model of COVID-19 with asymptomatic infections

On February 5 the Japanese government ordered the passengers and crew on the Diamond Princess to start a two week quarantine after a former passenger tested positive for COVID-19. During the quarantine the virus spread rapidly throughout the ship. By February 20, there were 651 cases. We model this quarantine with a SEIR model including asymptomatic infections with differentiated shipboard roles for crew and passengers. The study includes the derivation of the basic reproduction number and simulation studies showing the effect of quarantine with COVID-19 or influenza on the total infection numbers. We show that quarantine on a ship with COVID-19 will lead to significant disease spread if asymptomatic infections are not identified. However, if the majority of the crew and passengers are immune or vaccinated to COVID-19, then quarantine would slow the spread. We also show that a disease similar to influenza, even with a ship with a fully susceptible crew and passengers, could be contained through quarantine measures.

Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

BACKGROUND

Multiple methods have been developed to assess what happens between and within time series. In a particular type of these series, the previous values of the currently observed series are contingent on the lagged values of another series. These cases can commonly be addressed by regression. However, a model selection criteria should be employed to evaluate the compromise between the amount of information provided and the model complexity. This is the basis for the development of the Matrix of Lags (MoL), a tool to study dependent time series.

METHODS

For each input, multiple regressions were applied to produce a model for each lag and a model selection criterion identifies the lags that will populate an auxiliary matrix. Afterwards, the energy of the lags (that are in the auxiliary matrix) was used to define a row of the MoL. Therefore, each input corresponds to a row of the MoL. To test the proposed tool, the heart rate variability and the electrocardiogram derived respiration were employed to perform the indirect estimation of the electroencephalography cyclic alternating pattern (CAP) cycles. Therefore, a support vector machine was fed with the MoL to perform the CAP cycle classification for each input signal. Multiple tests were carried out to further examine the proposed tool, including the effect of balancing the datasets, application of other regression methods and employment of two feature section models. The first was based on sequential backward selection while the second examined characteristics of a return map.

RESULTS

The best performance of the subject independent model was attained by feeding the lags, selected by sequential backward selection, to a support vector machine, achieving an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 77%, 71%, 82% and 0.77.

CONCLUSIONS

The developed model allows to perform a measurement of a characteristic marker of sleep instability (the CAP cycle) and the results are in the upper bound of the specialist agreement range with visual analysis. Thus, the developed method could possibly be used for clinical diagnosis.

The Spielberger trait anxiety inventory measures more than anxiety

Objective

Researchers tried to explain the overlap between anxiety and depression by suggesting that some items of self-administered questionnaires were badly selected and that both constructs should rather be considered as multidimensional. Thus, we hypothesise that the Spielberger trait anxiety inventory (TAI) includes items related to depression.

Method

A non-clinical sample of 193 subjects filled out the TAI and the Hospitalised Anxiety–Depression Scale. Factors were postulated on the basis of item content and submitted to confirmatory factor analysis (CFA).

Results

We found five factors: a 10-item anxiety factor containing three factors, a four-item unsuccessfulness factor correlated with the HADS anhedonia factor, and a six-item happiness factor.

Conclusion

The TAI scale encompasses measures of anxiety, depression and well-being. Consequently, the overlap with other measures of depression may result from item selection. This work awaits replication in independent normal and pathological samples.

Optical development in the zebrafish eye lens

The optics of the eye is the key to a functioning visual system. The exact nature of the correlation between ocular optics and eye development is not known because of the paucity of knowledge about the growth of a key optical element, the eye lens. The sophisticated optics of the lens and its gradient of refractive index provide the superior optical quality that the eye needs and which, it is thought, has a major influence on the development of proper visual function. The nature of a gradient refractive index lens, however, renders accurate measurements of its development difficult to make and has been the reason why the influence of lens growth on visual function remains largely unknown. Novel imaging techniques have made it possible to investigate growth of the eye lens in the zebrafish. This study shows measurements using X-ray Talbot interferometry of three-dimensional gradient index profiles in eye lenses of zebrafish from late larval to adult stages. The zebrafish lens shows evidence of a gradient of refractive index from the earliest stages measured and its growth suggests an apparent coincidence between periods of rapid increase in refractive index in the lens nucleus and increased expression of a particular crystallin protein group.

Introduction to Metamodeling for Reducing Computational Burden of Advanced Analyses with Health Economic Models: A Structured Overview of Metamodeling Methods in a 6-Step Application Process

Metamodels can be used to reduce the computational burden associated with computationally demanding analyses of simulation models, although applications within health economics are still scarce. Besides a lack of awareness of their potential within health economics, the absence of guidance on the conceivably complex and time-consuming process of developing and validating metamodels may contribute to their limited uptake. To address these issues, this article introduces metamodeling to the wider health economic audience and presents a process for applying metamodeling in this context, including suitable methods and directions for their selection and use. General (i.e., non-health economic specific) metamodeling literature, clinical prediction modeling literature, and a previously published literature review were exploited to consolidate a process and to identify candidate metamodeling methods. Methods were considered applicable to health economics if they are able to account for mixed (i.e., continuous and discrete) input parameters and continuous outcomes. Six steps were identified as relevant for applying metamodeling methods within health economics: 1) the identification of a suitable metamodeling technique, 2) simulation of data sets according to a design of experiments, 3) fitting of the metamodel, 4) assessment of metamodel performance, 5) conducting the required analysis using the metamodel, and 6) verification of the results. Different methods are discussed to support each step, including their characteristics, directions for use, key references, and relevant R and Python packages. To address challenges regarding metamodeling methods selection, a first guide was developed toward using metamodels to reduce the computational burden of analyses of health economic models. This guidance may increase applications of metamodeling in health economics, enabling increased use of state-of-the-art analyses (e.g., value of information analysis) with computationally burdensome simulation models.

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice

We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Hence, longitudinal comparisons can be easily made based on simple summaries, such as proportion correct, sum score, etc. As an illustration of the method, an example is provided using data from a computer-adaptive practice mathematical environment.

A novel Chebyshev neural network approach for solving singular arbitrary order Lane-Emden equation arising in astrophysics

The motivation of this investigation is to develop a single-layer Chebyshev Neural Network (ChNN) model to handle singular fractional (arbitrary)-order Lane-Emden type equations. These equations are well-known application problems of astrophysics and quantum mechanics. Fractional Lane-Emden equations are singular so it is very difficult to solve analytically. Thus, an efficient method is required to handle the above equations. Here, our main aim is to use a single-layer ChNN model for solving fractional Lane-Emden equations. ChNN model is one kind of Functional Link Neural Network (FLNN) in which the hidden layer is replaced by a functional expansion block of the input pattern using orthogonalshifted Chebyshev polynomials (SChP). Thus, the network parameters of ChNN are less than the Multi-Layer Artificial Neural Network (MLANN). We have considered factional-order singular nonlinear problems of astrophysics to show the computational effort of the proposed method. Back Propagation algorithm of the unsupervised version has been considered for minimizing the error function and updating the weights of the ChNN model. Computed results are displayed in terms of tables and graphs.

Error metrics determination in functionally approximated circuits using SAT solvers

Approximate computing is an emerging design paradigm that offers trade-offs between output accuracy and computation efforts by exploiting some applications’ intrinsic error resiliency. Computation of error metrics is of paramount importance in approximate circuits to measure the degree of approximation. Most of the existing techniques for evaluating error metrics apply simulations which may not be effective for evaluation of large complex designs because of an immense increase in simulation runtime and a decrease in accuracy. To address these deficiencies, we present a novel methodology that employs SAT (Boolean satisfiability) solvers for fast and accurate determination of error metrics specifically for the calculation of an average-case error and the maximum error rate in functionally approximated circuits. The proposed approach identifies the set of all errors producing assignments to gauge the quality of approximate circuits for real-life applications. Additionally, the proposed approach provides a test generation method to facilitate design choices, and acts as an important guide to debug the approximate circuits to discover and locate the errors. The effectiveness of the approach is demonstrated by evaluating the error metrics of several benchmark-approximated adders of different sizes. Experimental results on benchmark circuits show that the proposed SAT-based methodology accurately determines the maximum error rate and an average-case error within acceptable CPU execution time in one go, and further provides a log of error-generating input assignments.

A permutation method for detecting trend correlations in rare variant association studies

In recent years, there has been an increasing interest in detecting disease-related rare variants in sequencing studies. Numerous studies have shown that common variants can only explain a small proportion of the phenotypic variance for complex diseases. More and more evidence suggests that some of this missing heritability can be explained by rare variants. Considering the importance of rare variants, researchers have proposed a considerable number of methods for identifying the rare variants associated with complex diseases. Extensive research has been carried out on testing the association between rare variants and dichotomous, continuous or ordinal traits. So far, however, there has been little discussion about the case in which both genotypes and phenotypes are ordinal variables. This paper introduces a method based on the γ-statistic, called OV-RV, for examining disease-related rare variants when both genotypes and phenotypes are ordinal. At present, little is known about the asymptotic distribution of the γ-statistic when conducting association analyses for rare variants. One advantage of OV-RV is that it provides a robust estimation of the distribution of the γ-statistic by employing the permutation approach proposed by Fisher. We also perform extensive simulations to investigate the numerical performance of OV-RV under various model settings. The simulation results reveal that OV-RV is valid and efficient; namely, it controls the type I error approximately at the pre-specified significance level and achieves greater power at the same significance level. We also apply OV-RV for rare variant association studies of diastolic blood pressure.

Comparison of Different Electrocardiography with Vectorcardiography Transformations

This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.

Parameterized Analysis of Multiobjective Evolutionary Algorithms and the Weighted Vertex Cover Problem

Evolutionary multiobjective optimization for the classical vertex cover problem has been analysed in Kratsch and Neumann (2013) in the context of parameterized complexity analysis. This article extends the analysis to the weighted vertex cover problem in which integer weights are assigned to the vertices and the goal is to find a vertex cover of minimum weight. Using an alternative mutation operator introduced in Kratsch and Neumann (2013), we provide a fixed parameter evolutionary algorithm with respect to OPT, the cost of an optimal solution for the problem. Moreover, we present a multiobjective evolutionary algorithm with standard mutation operator that keeps the population size in a polynomial order by means of a proper diversity mechanism, and therefore, manages to find a 2-approximation in expected polynomial time. We also introduce a population-based evolutionary algorithm which finds a (1+ɛ)-approximation in expected time O(n·2min{n,2(1-ɛ)OPT}+n3).

A review of spline function procedures in R

BACKGROUND

With progress on both the theoretical and the computational fronts the use of spline modelling has become an established tool in statistical regression analysis. An important issue in spline modelling is the availability of user friendly, well documented software packages. Following the idea of the STRengthening Analytical Thinking for Observational Studies initiative to provide users with guidance documents on the application of statistical methods in observational research, the aim of this article is to provide an overview of the most widely used spline-based techniques and their implementation in R.

METHODS

In this work, we focus on the R Language for Statistical Computing which has become a hugely popular statistics software. We identified a set of packages that include functions for spline modelling within a regression framework. Using simulated and real data we provide an introduction to spline modelling and an overview of the most popular spline functions.

RESULTS

We present a series of simple scenarios of univariate data, where different basis functions are used to identify the correct functional form of an independent variable. Even in simple data, using routines from different packages would lead to different results.

CONCLUSIONS

This work illustrate challenges that an analyst faces when working with data. Most differences can be attributed to the choice of hyper-parameters rather than the basis used. In fact an experienced user will know how to obtain a reasonable outcome, regardless of the type of spline used. However, many analysts do not have sufficient knowledge to use these powerful tools adequately and will need more guidance.

Cobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network

Scoliosis is a common spinal condition where the spine curves to the side and thus deforms the spine. Curvature estimation provides a powerful index to evaluate the deformation severity of scoliosis. In current clinical diagnosis, the standard curvature estimation method for assessing the curvature quantitatively is done by measuring the Cobb angle, which is the angle between two lines, drawn perpendicular to the upper endplate of the uppermost vertebra involved and the lower endplate of the lowest vertebra involved. However, manual measurement of spine curvature requires considerable time and effort, along with associated problems such as interobserver and intraobserver variations. In this article, we propose an automatic system for measuring spine curvature using the anterior-posterior (AP) view spinal X-ray images. Due to the characteristic of AP view images, we first reduced the image size and then used horizontal and vertical intensity projection histograms to define the region of interest of the spine which is then cropped for sequential processing. Next, the boundaries of the spine, the central spinal curve line, and the spine foreground are detected by using intensity and gradient information of the region of interest, and a progressive thresholding approach is then employed to detect the locations of the vertebrae. In order to reduce the influences of inconsistent intensity distribution of vertebrae in the spine AP image, we applied the deep learning convolutional neural network (CNN) approaches which include the U-Net, the Dense U-Net, and Residual U-Net, to segment the vertebrae. Finally, the segmentation results of the vertebrae are reconstructed into a complete segmented spine image, and the spine curvature is calculated based on the Cobb angle criterion. In the experiments, we showed the results for spine segmentation and spine curvature; the results were then compared to manual measurements by specialists. The segmentation results of the Residual U-Net were superior to the other two convolutional neural networks. The one-way ANOVA test also demonstrated that the three measurements including the manual records of two different physicians and our proposed measured record were not significantly different in terms of spine curvature measurement. Looking forward, the proposed system can be applied in clinical diagnosis to assist doctors for a better understanding of scoliosis severity and for clinical treatments.

Hypervolume Subset Selection with Small Subsets

The hypervolume subset selection problem (HSSP) aims at approximating a set of n multidimensional points in Rd with an optimal subset of a given size. The size k of the subset is a parameter of the problem, and an approximation is considered best when it maximizes the hypervolume indicator. This problem has proved popular in recent years as a procedure for multiobjective evolutionary algorithms. Efficient algorithms are known for planar points (d=2), but there are hardly any results on HSSP in larger dimensions (d≥3). So far, most algorithms in higher dimensions essentially enumerate all possible subsets to determine the optimal one, and most of the effort has been directed toward improving the efficiency of hypervolume computation. We propose efficient algorithms for the selection problem in dimension 3 when either k or n-k is small, and extend our techniques to arbitrary dimensions for k≤3.

Search Dynamics on Multimodal Multiobjective Problems

We continue recent work on the definition of multimodality in multiobjective optimization (MO) and the introduction of a test bed for multimodal MO problems. This goes beyond well-known diversity maintenance approaches but instead focuses on the landscape topology induced by the objective functions. More general multimodal MO problems are considered by allowing ellipsoid contours for single-objective subproblems. An experimental analysis compares two MO algorithms, one that explicitly relies on hypervolume gradient approximation, and one that is based on local search, both on a selection of generated example problems. We do not focus on performance but on the interaction induced by the problems and algorithms, which can be described by means of specific characteristics explicitly designed for the multimodal MO setting. Furthermore, we widen the scope of our analysis by additionally applying visualization techniques in the decision space. This strengthens and extends the foundations for Exploratory Landscape Analysis (ELA) in MO.

Dose equivalent transmission data for shielding industrial x-ray facilities up to 800 kV

The transmission factors used to calculate radiation shielding around an industrial x-ray device are determined using the MCNP6 code. The transmission factors are given for high voltages ranging between 120 and 800 kV for lead and between 200 and 800 kV for concrete. In view of the high usage intensity of industrial devices, the transmission factors are evaluated up to 1.10^-10. The parameters used in the classic equation of Archer et al are derived from the transmission data calculated here. This type of data exists in the literature, but only for voltages lower than 150 kV to meet the design demands for facilities used in the medical field. In addition, this study markedly supplements the existing data, in particular for industrial and research installations.

Automatic measurement of global retinal circulation in fluorescein angiography

Examination of the retinal circulation in patients with retinal diseases is a clinical routine for ophthalmologists. In the present work, an automatic method is proposed for measuring the global retinal circulation in fluorescein angiography (FA). First, the perfusion region in FA images is segmented using a multiscale line detector. Then, the time evolution of the perfusion area is modeled using damped least-squares regression. Based on the perfusion area profile, some circulation parameters are defined to describe quantitatively the global retinal circulation. The effectiveness of the proposed method is tested using our own and public datasets, with reasonable results and satisfactory accuracy compared with manual measurement. The proposed method has good computing efficiency and thus has potential to be used in clinical practice for evaluation of global retinal circulation.

Small Universal Bacteria and Plasmid Computing Systems

Bacterial computing is a known candidate in natural computing, the aim being to construct "bacterial computers" for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate the computational power of BP systems with finite numbers of bacteria and plasmids. Specifically, it is obtained in a constructive way that a BP system with 2 bacteria and 34 plasmids is Turing universal. The results provide a theoretical cornerstone to construct powerful bacterial computers and demonstrate a concept of paradigms using a "reasonable" number of bacteria and plasmids for such devices.

Radiobiological Models of Tissue Response to Radiation in Treatment Planning Systems

Aims

To present several biological concepts and models of tissue response to fractionated radiotherapy. To describe practical implementation of these models in three-dimensional treatment planning systems.

Methods

Models of cell survival, Equivalent Uniform Dose (EUD) and Tumor Control Probability (TCP) are discussed. These models are based on the target-cell hypothesis which assumes that response of organs and tissues to radiation therapy can be explained and mathematically described in terms of survival of the specific target-cells.

Results

Several formulae for deriving and calculating EUD and TCP for a given three-dimensional dose distribution are presented and discussed.

Conclusions

Biological models of tissue response to radiation, when used wisely, have a potential to be useful in radiation therapy treatment planning. The models can advance our understanding of the underlying biological mechanisms, and may help in designing new and better treatment strategies. They should be particularly useful in modern conformai radiotherapy where treatment strategy for each patient can be individualized and optimized according to patient characteristics and available technology of delivering sophisticated treatment plans.

Two Quality Control Procedures on Radiotherapy Beam Calibration and Treatment Planning System Implementation

New challenging dosimetric approaches, such as narrow beams and 3D algorithms, are being used in radiotherapy. In this paper two quality control (QC) procedures are reported. The first one concerns the QC of the dosimetry of small x-ray beams, generally carried out by using silicon detectors. The comparison of dose values obtained by a silicon diode, a diamond detector, and radiochromic films shows that for x-ray beams of high energy, the silicon diode can give an overestimation of the output factors in phantom, up to 4%. This is due to the higher than unit density silicon diode and the surrounding envelope that restore the lateral electron equilibrium. About the 3D algorithms for breast treatment planning, a quality control test has been adopted to verify the accuracy of the computed dosimetry when “loss of scatter” occurs. The results show a sensible agreement (within 1.5%) between computed and experimental data.

Humanitarian health computing using artificial intelligence and social media: A narrative literature review

INTRODUCTION

According to the World Health Organization (WHO), over 130 million people are in constant need of humanitarian assistance due to natural disasters, disease outbreaks, and conflicts, among other factors. These health crises can compromise the resilience of healthcare systems, which are essential for achieving the health objectives of the sustainable development goals (SDGs) of the United Nations (UN). During a humanitarian health crisis, rapid and informed decision making is required. This is often challenging due to information scarcity, limited resources, and strict time constraints. Moreover, the traditional approach to digital health development, which involves a substantial requirement analysis, a feasibility study, and deployment of technology, is ill-suited for many crisis contexts. The emergence of Web 2.0 technologies and social media platforms in the past decade, such as Twitter, has created a new paradigm of massive information and misinformation, in which new technologies need to be developed to aid rapid decision making during humanitarian health crises.

OBJECTIVE

Humanitarian health crises increasingly require the analysis of massive amounts of information produced by different sources, such as social media content, and, hence, they are a prime case for the use of artificial intelligence (AI) techniques to help identify relevant information and make it actionable. To identify challenges and opportunities for using AI in humanitarian health crises, we reviewed the literature on the use of AI techniques to process social media.

METHODOLOGY

We performed a narrative literature review aimed at identifying examples of the use of AI in humanitarian health crises. Our search strategy was designed to get a broad overview of the different applications of AI in a humanitarian health crisis and their challenges. A total of 1459 articles were screened, and 24 articles were included in the final analysis.

RESULTS

Successful case studies of AI applications in a humanitarian health crisis have been reported, such as for outbreak detection. A commonly shared concern in the reviewed literature is the technical challenge of analyzing large amounts of data in real time. Data interoperability, which is essential to data sharing, is also a barrier with regard to the integration of online and traditional data sources. Human and organizational aspects that might be key factors for the adoption of AI and social media remain understudied. There is also a publication bias toward high-income countries, as we identified few examples in low-income countries. Further, we did not identify any examples of certain types of major crisis, such armed conflicts, in which misinformation might be more common.

CONCLUSIONS

The feasibility of using AI to extract valuable information during a humanitarian health crisis is proven in many cases. There is a lack of research on how to integrate the use of AI into the work-flow and large-scale deployments of humanitarian aid during a health crisis.

The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments

Alzheimer's disease (AD) is a neurodegenerative disorder characterised by a slow progressive deterioration of cognitive capacity. Drugs are urgently needed for the treatment of AD and unfortunately almost all clinical trials of AD drug candidates have failed or been discontinued to date. Mathematical, computational and statistical tools can be employed in the construction of clinical trial simulators to assist in the improvement of trial design and enhance the chances of success of potential new therapies. Based on the analysis of a set of clinical data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) we developed a simple stochastic mathematical model to simulate the development and progression of Alzheimer's in a longitudinal cohort study. We show how this modelling framework could be used to assess the effect and the chances of success of hypothetical treatments that are administered at different stages and delay disease development. We demonstrate that the detection of the true efficacy of an AD treatment can be very challenging, even if the treatment is highly effective. An important reason behind the inability to detect signals of efficacy in a clinical trial in this therapy area could be the high between- and within-individual variability in the measurement of diagnostic markers and endpoints, which consequently results in the misdiagnosis of an individual's disease state.

Spatial landscape model to characterize biological diversity using R statistical computing environment

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.

Control measurements for lasers in physiotherapy

OBJECTIVES

In physical rehabilitation, is diffused the skin irradiation with near infrared laser at a fluence below 140 J/cm2, achieving a bio-stimulating effect that is due to the absorption of radiation in mitochondria rather than the simple heating of tissues.

METHODS

In order to deliver radiation without thermal damage of the skin, are used radiation pulses which duration does not allow heat accumulation and propagation far from the irradiated target; this requires laser sources with average power below 10 W implying a safety classification as "potentially dangerous for eye and skin", or "class 4" according to the applicable international standards. In this paper, 6 laser therapy devices, of 5 different manufactures and models have been analyzed from the point of view of actual radiation output and user safety. In each case, one or more of the characteristic declared by the manufacturer in the user manual have been found different from the actual measured value.

RESULTS

The actual accessible energy levels have been found to be complying with risk class 3B. The impact of the new version of the Standard IEC 60825-1 (2014), is also discussed, considering in particular the possible classification in the new class 1C, and the maximum permissible levels for pulsed lasers.

CONCLUSIONS

An extension of the measurement protocols is proposed in order to assure effective and safe use of laser in physical therapy.

Hypoglycemia and Clinical Outcomes in Hospitalized Patients With Diabetes: Does Association With Adverse Outcomes Remain When Number of Glucose Tests Performed Is Accounted For?

BACKGROUND

Hypoglycemia is associated with increased length of stay in hospital patients, but previous studies have not considered the confounding effect of increased hypoglycemia detection associated with increased capillary blood glucose (CBG) measurement in prolonged admissions. We aimed to determine the effect of recorded hypoglycemia on length of stay of hospital inpatients (LOS) when this mathematical association is subtracted.

METHODS

CBG data were analyzed for inpatients within our health board area (01/2009-01/2015). A simulated CBG data set was generated for each patient with an identical sampling frequency to the measured CBG data set. The mathematical component of increased LOS was determined using the simulated data set. Subtraction of this confounding mathematical association was used to provide measurement of the true clinical association between recorded hypoglycemia (CBG < 4 mmol [< 72mg/dl]) and LOS.

RESULTS

A total of 196 962 admissions of 52 475 individuals with known diabetes were analyzed. 68 809 admissions of 29 551 individuals had >4 CBG measurements made and were included in analysis. After subtraction of the mathematical association of increased sample number, the clinical effect of recorded hypoglycemia is reduced-but persists-compared to previous studies. 1-2 days with recorded hypoglycemia has a relatively minor effect on LOS. LOS increases rapidly if there are ≥3 days with recorded hypoglycemia, with an increase of 0.75 days LOS per additional day with hypoglycemia.

CONCLUSIONS

This technique increases accuracy of economic modeling of the impact of hypoglycemia on health care systems. This could assist study of the impact of hypoglycemia on other outcomes by factoring for bias of increased sample numbers.

The critical role of logarithmic transformation in Nernstian equilibrium potential calculations

The membrane potential, arising from uneven distribution of ions across cell membranes containing selectively permeable ion channels, is of fundamental importance to cell signaling. The necessity of maintaining the membrane potential may be appreciated by expressing Ohm's law as current = voltage/resistance and recognizing that no current flows when voltage = 0, i.e., transmembrane voltage gradients, created by uneven transmembrane ion concentrations, are an absolute requirement for the generation of currents that precipitate the action and synaptic potentials that consume >80% of the brain's energy budget and underlie the electrical activity that defines brain function. The concept of the equilibrium potential is vital to understanding the origins of the membrane potential. The equilibrium potential defines a potential at which there is no net transmembrane ion flux, where the work created by the concentration gradient is balanced by the transmembrane voltage difference, and derives from a relationship describing the work done by the diffusion of ions down a concentration gradient. The Nernst equation predicts the equilibrium potential and, as such, is fundamental to understanding the interplay between transmembrane ion concentrations and equilibrium potentials. Logarithmic transformation of the ratio of internal and external ion concentrations lies at the heart of the Nernst equation, but most undergraduate neuroscience students have little understanding of the logarithmic function. To compound this, no current undergraduate neuroscience textbooks describe the effect of logarithmic transformation in appreciable detail, leaving the majority of students with little insight into how ion concentrations determine, or how ion perturbations alter, the membrane potential.

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user’s Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user’s QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

What Is Morphological Computation? On How the Body Contributes to Cognition and Control

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as "offloading computation from the brain to the body," where the body is said to perform "morphological computation." Our investigation of four characteristic cases of morphological computation in animals and robots shows that the "offloading" perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control-how it contributes to the overall orchestration of intelligent behavior.

Methods for Modeling Brassinosteroid-Mediated Signaling in Plant Development

Mathematical modeling of biological processes is a useful tool to draw conclusions that are contained in the data, but not directly reachable, as well as to make predictions and select the most efficient follow-up experiments. Here we outline a method to model systems of a few proteins that interact transcriptionally and/or posttranscriptionally, by representing the system as Ordinary Differential Equations and to study the model dynamics and stationary states. We exemplify this method by focusing on the regulation by the brassinosteroid (BR) signaling component BRASSINOSTEROID INSENSITIVE1 ETHYL METHYL SULFONATE SUPPRESSOR1 (BES1) of BRAVO, a quiescence-regulating transcription factor expressed in the quiescent cells of Arabidopsis thaliana roots. The method to extract the stationary states and the dynamics is provided as a Mathematica code and requires basic knowledge of the Mathematica software to be executed.

Resampling Permutation Probability Values for Six Measures of Qualitative Variation

An algorithm and associated FORTRAN program are provided for six common measures of ordinal association: Kendall's τ a and τ b, Stuart's τ c, Goodman and Kruskal's γ, and Somers' dyx and dxy. Program ROMA reports the observed data table, the values for the six test statistics, and the resampling upper- and lower-tail probability values associated with each test statistic.

Psychometrics and Psychometricians in the 20th and 21st Centuries: How it was in the 20th Century and How it is Now

This article is based largely upon the author's invited address at the 113th annual convention of the American Psychological Convention, Washington, DC, as the 2005 recipient of the Samuel J. Messick Award bestowed by APA Division 5 and the Educational Testing Service. The author summarizes the growth of graduate training in psychometrics and quantitative psychology, in the years prior to and following the end of WWII. He then opines the steady decline in the training of psychometricians and quantitative psychologists beginning in the 1970s and continuing into the 20th century. Likely causes of the decline are inferred and prospects for strengthening the quantitative skills of doctorates are discussed, including recommendations for reversing the downward trend.

A Fortran Program for Computing the Exact Variance of Weighted Kappa

An algorithm and associated FORTRAN program are provided for the exact variance of weighted kappa. Program VARKAP provides the weighted kappa test statistic, the exact variance of weighted kappa, a Z score, one-sided lower- and upper-tail N(0,1) probability values, and the two-tail N(0,1) probability value.

Role of Adolescent Female Volleyball Players' Psychophysiological Properties and Body Build in Performance of Different Elements of the Game

Body-build peculiarities (49 body measurements) and psychophysiological properties (21 computerized tests) of 32 adolescent female volleyballers were studied to assess their significance in performance at competitions. Games were recorded by the original computer program Game, and an index of proficiency representative elements of the game was calculated for each player. Regression analysis was applied to predict the best psychophysiological and anthropometric models for serve, reception, block, feint, and attack. Seven tests of psychophysiological indices and 14 anthropometric variables explained 38–98% and 32–83% of skill performance, respectively. Consequently, to improve young volleyball players' performance, everyday coaching should be complemented by detailed assessment of their body build and psychophysiological characteristics.

Long-Range Memory in Literary Texts: On the Universal Clustering of the Rare Words

A fundamental problem in linguistics is how literary texts can be quantified mathematically. It is well known that the frequency of a (rare) word in a text is roughly inverse proportional to its rank (Zipf’s law). Here we address the complementary question, if also the rhythm of the text, characterized by the arrangement of the rare words in the text, can be quantified mathematically in a similar basic way. To this end, we consider representative classic single-authored texts from England/Ireland, France, Germany, China, and Japan. In each text, we classify each word by its rank. We focus on the rare words with ranks above some threshold Q and study the lengths of the (return) intervals between them. We find that for all texts considered, the probability S_Q(r) that the length of an interval exceeds r, follows a perfect Weibull-function, S_Q(r) = exp(−b(β)r^β), with β around 0.7. The return intervals themselves are arranged in a long-range correlated self-similar fashion, where the autocorrelation function C_Q(s) of the intervals follows a power law, C_Q(s) ∼ s^−γ, with an exponent γ between 0.14 and 0.48. We show that these features lead to a pronounced clustering of the rare words in the text.

Persistent Memory in Single Node Delay-Coupled Reservoir Computing

Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.

Marine protected area design patterns in the Mediterranean Sea: Implications for conservation

Mediterranean marine protected area (MPA) design patterns regarding geographic distribution, size, spacing and shape were analysed as a proxy of the region's MPA's ecological effectiveness and a first step towards an ecologically coherent MPA network. Results for legally designated MPAs and ecologically functional MPAs accounting for overlaps are presented. Geographically, Mediterranean MPA area is very unevenly distributed, with four-fifths concentrated in just three countries of the north-western part of the basin. Average distance between functional MPAs lies within recommended ecological thresholds, which suggests adequate potential connectivity of the Mediterranean MPA system. Mediterranean designated MPAs are larger than MPAs worldwide on average, although they are generally smaller than international guidance suggests at different levels: ecoregion, country and designation category. On average, Mediterranean designated and functional MPAs have relatively high compactness, which makes them prone to spillover and adequate viability, and less vulnerable to edge effects.

Single-Item vs Multiple-Item Measures of Stage of Change in Compliance with Prescribed Medications

The Stage of Change construct from the Transtheoretical Model of behavioral change has been widely utilized in the assessment of various health behaviors. The majority of these tests measure the Stage of Change construct using the single-item, multiple-choice format. This study validated the use of a single-item measure in measuring readiness to comply with taking a prescribed medication. A sample of 161 subjects tested the multiple-item Stage of Change measure, then a refined multiple-item survey was tested with 59 subjects. With the latter survey, discriminating subjects at the differing stages of change dimensions was difficult. A correlation of .91 was found for stage classifications between ratings on the single-item and multiple-item scales. The use of the single-item measure seems reasonable when assessing stage of change in compliance with prescribed medication.

Asymptotic Log-Linear Analysis: Some Cautions concerning Sparse Frequency Tables

Traditional asymptotic probability values resulting from log-linear analyses of sparse frequency tables are often much too large. Asymptotic probability values for chi-squared and likelihood-ratio statistics are compared to nonasymptotic and exact probability values for selected log-linear models. The asymptotic probability values are all too often substantially larger than the exact probability values for the analysis of sparse frequency tables. An exact nondirectional permutation method is presented to analyze combined independent multinomial distributions. Exact nondirectional permutation methods to analyze hypergeometric distributions associated with r-way frequency tables are confined to r = 2.

Calculation of Enzyme Fluctuograms from All-Atom Molecular Dynamics Simulation

In this work, a computational framework is presented to compute the time evolution of force constants for a coarse grained (CG) elastic network model along an all-atom molecular dynamics trajectory of a protein system. Obtained via matching distance fluctuations, these force constants represent strengths of mechanical coupling between CG beads. Variation of coupling strengths with time is hence termed the fluctuogram of protein dynamics. In addition to the schematic procedure and implementation considerations, several ways of combining force constants and data analysis are presented to illustrate the potential application of protein fluctuograms. The unique angle provided by the fluctuogram expands the scope of atomistic simulations and is expected to impact upon fundamental understanding of protein dynamics as well as protein engineering technologies.

Assessing Interorganizational Networks as a Dimension of Community Capacity: Illustrations From a Community Intervention to Prevent Lead Poisoning

Network analysis is often cited as a method for assessing collaboration among organizations as an indicator of community capacity. The purpose of this study was to (1) document patterns of collaboration in organizational networks related to lead poisoning prevention in a Native American community and (2) examine measurement issues in using organizational network analysis to assess community capacity. Interviews were conducted with representatives from 22 tribes, government agencies, schools, and community-based organizations in northeastern Oklahoma. Intensity, density, and reliability were assessed for several stages of collaboration. Intensity and density were greater for similar types of organizations than for the network as a whole and decreased as stage of collaboration increased. Network data were more reliable when responses were dichotomized than when intensities were compared. Mean reliability scores across two respondents from the same organization ranged from 60% to 90%. Results from network studies may help communities learn how to strengthen organizational networks to enhance community capacity.

Multivariate Tests of Means in Independent Groups Designs

Health evaluation research often employs multivariate designs in which data on several outcome variables are obtained for independent groups of subjects. This article examines statistical procedures for testing hypotheses of multivariate mean equality in two-group designs. The conventional test for multivariate means, Hotelling’s T2 , rests on certain assumptions about the distribution of the data and the population variances and covariances. When these assumptions are violated, which is often the case in applied health research, T 2 will result in invalid conclusions about the null hypothesis. This article describes parametric procedures that are robust, or insensitive, to assumption violations. A numeric example illustrates the statistical concepts that are presented and a computer program to implement these robust solutions is introduced.

Path Sampling Methods for Enzymatic Quantum Particle Transfer Reactions

The mechanisms of enzymatic reactions are studied via a host of computational techniques. While previous methods have been used successfully, many fail to incorporate the full dynamical properties of enzymatic systems. This can lead to misleading results in cases where enzyme motion plays a significant role in the reaction coordinate, which is especially relevant in particle transfer reactions where nuclear tunneling may occur. In this chapter, we outline previous methods, as well as discuss newly developed dynamical methods to interrogate mechanisms of enzymatic particle transfer reactions. These new methods allow for the calculation of free energy barriers and kinetic isotope effects (KIEs) with the incorporation of quantum effects through centroid molecular dynamics (CMD) and the full complement of enzyme dynamics through transition path sampling (TPS). Recent work, summarized in this chapter, applied the method for calculation of free energy barriers to reaction in lactate dehydrogenase (LDH) and yeast alcohol dehydrogenase (YADH). We found that tunneling plays an insignificant role in YADH but plays a more significant role in LDH, though not dominant over classical transfer. Additionally, we summarize the application of a TPS algorithm for the calculation of reaction rates in tandem with CMD to calculate the primary H/D KIE of YADH from first principles. We found that the computationally obtained KIE is within the margin of error of experimentally determined KIEs and corresponds to the KIE of particle transfer in the enzyme. These methods provide new ways to investigate enzyme mechanism with the inclusion of protein and quantum dynamics.