Standard Set Of Parameters Research Articles

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T1-weighted and T2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (), with an average AUC of . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Parameters for Simulation of Adult Subjects During Mechanical Ventilation.

Simulation studies are often used to examine ventilator performance. However, there are no standards for selecting simulation parameters. This study collected data in passively-ventilated adult human subjects and summarized the results as a set of parameters that can be used for simulation studies of intubated, passive, adult subjects with normal lungs, COPD, or ARDS. Consecutive adult patients admitted to the ICU were included if they were deeply sedated and mechanically ventilated for <48 h without any spontaneous breathing activity. Subjects were classified as having normal lungs, COPD, or ARDS. Respiratory mechanics variables were collected once per subject. Static compliance was calculated as the ratio between tidal volume and driving pressure. Inspiratory resistance was measured by the least-squares fitting method. The expiratory time constant was estimated by the tidal volume/flow ratio. Of the 359 subjects included, 138 were classified as having normal lungs, 181 as ARDS, and 40 as COPD. Median (interquartile range) static compliance was significantly lower in ARDS subjects as compared with normal lung and COPD subjects (39 [32-50] mL/cm H2O vs 54 [44-64] and 59 [43-75] mL/cm H2O, respectively, P < .001). Inspiratory resistance was significantly higher in COPD subjects as compared with normal lung and ARDS subjects (22 [16-33] cm H2O/L/s vs 13 [10-15] and 12 [9-14] cm H2O/L/s, respectively, P < .001). The expiratory time constant was significantly different for each lung condition (0.60 [0.51-0.71], 1.07 [0.68-2.14], and 0.46 [0.40-0.55] s for normal lung, COPD, and ARDS subjects, respectively, P < .001). In the subgroup of subjects with ARDS, there were no significant differences in respiratory mechanics variables among mild, moderate, and severe ARDS. This study provides educators, researchers, and manufacturers with a standard set of practical parameters for simulating the respiratory system's mechanical properties in passive conditions.

The expression of emotion in the singing voice: Acoustic patterns in vocal performance.

There has been little research on the acoustic correlates of emotional expression in the singing voice. In this study, two pertinent questions are addressed: How does a singer's emotional interpretation of a musical piece affect acoustic parameters in the sung vocalizations? Are these patterns specific enough to allow statistical discrimination of the intended expressive targets? Eight professional opera singers were asked to sing the musical scale upwards and downwards (using meaningless content) to express different emotions, as if on stage. The studio recordings were acoustically analyzed with a standard set of parameters. The results show robust vocal signatures for the emotions studied. Overall, there is a major contrast between sadness and tenderness on the one hand, and anger, joy, and pride on the other. This is based on low vs high levels on the components of loudness, vocal dynamics, high perturbation variation, and a tendency for high low-frequency energy. This pattern can be explained by the high power and arousal characteristics of the emotions with high levels on these components. A multiple discriminant analysis yields classification accuracy greatly exceeding chance level, confirming the reliability of the acoustic patterns.

The Mira-Titan Universe. II. Matter Power Spectrum Emulation

Abstract We introduce a new cosmic emulator for the matter power spectrum covering eight cosmological parameters. Targeted at optical surveys, the emulator provides accurate predictions out to a wavenumber Mpc−1 and redshift . In addition to covering the standard set of ΛCDM parameters, massive neutrinos and a dynamical dark energy of state are included. The emulator is built on a sample set of 36 cosmological models, carefully chosen to provide accurate predictions over the wide and large parameter space. For each model, we have performed a high-resolution simulation, augmented with 16 medium-resolution simulations and TimeRG perturbation theory results to provide accurate coverage over a wide k-range; the data set generated as part of this project is more than 1.2Pbytes. With the current set of simulated models, we achieve an accuracy of approximately 4%. Because the sampling approach used here has established convergence and error-control properties, follow-up results with more than a hundred cosmological models will soon achieve accuracy. We compare our approach with other prediction schemes that are based on halo model ideas and remapping approaches. The new emulator code is publicly available.

The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions.

The physiological responses of simple and complex cells in the primary visual cortex (V1) have been studied extensively and modeled at different levels. At the functional level, the divisive normalization model (DNM; Heeger DJ. Vis Neurosci 9: 181-197, 1992) has accounted for a wide range of single-cell recordings in terms of a combination of linear filtering, nonlinear rectification, and divisive normalization. We propose standardizing the formulation of the DNM and implementing it in software that takes static grayscale images as inputs and produces firing rate responses as outputs. We also review a comprehensive suite of 30 empirical phenomena and report a series of simulation experiments that qualitatively replicate dozens of key experiments with a standard parameter set consistent with physiological measurements. This systematic approach identifies novel falsifiable predictions of the DNM. We show how the model simultaneously satisfies the conflicting desiderata of flexibility and falsifiability. Our key idea is that, while adjustable parameters are needed to accommodate the diversity across neurons, they must be fixed for a given individual neuron. This requirement introduces falsifiable constraints when this single neuron is probed with multiple stimuli. We also present mathematical analyses and simulation experiments that explicate some of these constraints.

Establishing a reliable gait evaluation method for rodent studies

BackgroundCatWalk is one of the most popular tools for evaluating gait recovery in preclinical research, however, there is currently no consensus on which of the many gait parameters captured by CatWalk can reliably model recovery. There are conflicting interpretations of results, along with many common but seldom reported problems such as heel walking and poor compliance. New methodWe developed a systematic manual classification method that overcomes common problems such as heel walking and poor compliance. By correcting automation errors and removing inconsistent gait cycles, we isolated stretches of recordings that are more reliable for analysis. Recovery outcome was also assessed by quantitative histomorphometric analysis of myelinated axons. ResultsWhile 40–60% of runs were erroneously classified without manual intervention, we corrected all errors with our new method, and showed that Stand Time, Duty Cycle, and Swing Speed are able to track significant differences over time and between experimental groups (all p<0.05). The usability of print area and intensity parameters requires further validation beyond the capabilities of CatWalk. Comparison with existing method(s)There is currently no strategy that addresses problems such as heel walking and poor compliance, and therefore no standard set of parameters that researchers can rely on to report their findings. ConclusionManual classification is a crucial step to generate reliable CatWalk data, and Stand Time, Duty Cycle, and Swing Speed are suitable parameters for evaluating gait recovery. Static parameters such as print area and intensity should be used with extreme caution.

A sensitivity analysis of RNA folding nearest neighbor parameters identifies a subset of free energy parameters with the greatest impact on RNA secondary structure prediction.

Nearest neighbor parameters for estimating the folding energy changes of RNA secondary structures are used in structure prediction and analysis. Despite their widespread application, a comprehensive analysis of the impact of each parameter on the precision of calculations had not been conducted. To identify the parameters with greatest impact, a sensitivity analysis was performed on the 291 parameters that compose the 2004 version of the free energy nearest neighbor rules. Perturbed parameter sets were generated by perturbing each parameter independently. Then the effect of each individual parameter change on predicted base-pair probabilities and secondary structures as compared to the standard parameter set was observed for a set of sequences including structured ncRNA, mRNA and randomized sequences. The results identify for the first time the parameters with the greatest impact on secondary structure prediction, and the subset which should be prioritized for further study in order to improve the precision of structure prediction. In particular, bulge loop initiation, multibranch loop initiation, AU/GU internal loop closure and AU/GU helix end parameters were particularly important. An analysis of parameter usage during folding free energy calculations of stochastic samples of secondary structures revealed a correlation between parameter usage and impact on structure prediction precision.

Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships.

In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally. In this paper we compare eXtreme Gradient Boosting (XGBoost) to random forest and single-task deep neural nets on 30 in-house data sets. While XGBoost has many adjustable parameters, we can define a set of standard parameters at which XGBoost makes predictions, on the average, better than those of random forest and almost as good as those of deep neural nets. The biggest strength of XGBoost is its speed. Whereas efficient use of random forest requires generating each tree in parallel on a cluster, and deep neural nets are usually run on GPUs, XGBoost can be run on a single CPU in less than a third of the wall-clock time of either of the other methods.

A systematic variant annotation approach for ranking genes associated with autism spectrum disorders.

BackgroundThe search for genetic factors underlying autism spectrum disorders (ASD) has led to the identification of hundreds of genes containing thousands of variants that differ in mode of inheritance, effect size, frequency, and function. A major challenge involves assessing the collective evidence in an unbiased, systematic manner for their functional relevance.MethodsHere, we describe a scoring algorithm for prioritization of candidate genes based on the cumulative strength of evidence for each ASD-associated variant cataloged in AutDB (also known as SFARI Gene). We retrieved data from 889 publications to generate a dataset of 2187 rare and 711 common variants distributed across 461 genes implicated in ASD. Each individual variant was manually annotated with multiple attributes extracted from the original report, followed by score assignment using a set of standardized parameters yielding a single score for each gene.ResultsThere was a wide variation in scores; SHANK3, CHD8, and ADNP had distinctly higher scores than all other genes in the dataset. Our gene scores were significantly correlated with other recently published rankings of ASD genes (R Spearman = 0.40–0.63; p< 0.0001), providing support for our scoring algorithm.ConclusionsThis new resource, which is freely available, for the first time aggregates on one-platform variants identified from various study types (simplex, multiplex, multigenerational, and consanguineous families), from both common and rare variants, and also incorporates their putative functional consequences to arrive at a genetically and biologically driven ranking scheme. This work represents a major step in moving from simply cataloging autism variants to using data-driven approaches to gain insight into their significance.Electronic supplementary materialThe online version of this article (doi:10.1186/s13229-016-0103-y) contains supplementary material, which is available to authorized users.

Eye movements and information geometry.

The human visual system uses eye movements to gather visual information. They act as visual scanning processes and can roughly be divided into two different types: small movements around fixation points and larger movements between fixation points. The processes are often modeled as random walks, and recent models based on heavy tail distributions, also known as Levý flights, have been used in these investigations. In contrast to these approaches we do not model the stochastic processes, but we will show that the step lengths of the movements between fixation points follow generalized Pareto distributions (GPDs). We will use general arguments from the theory of extreme value statistics to motivate the usage of the GPD and show empirically that the GPDs provide good fits for measured eye tracking data. In the framework of information geometry the GPDs with a common threshold form a two-dimensional Riemann manifold with the Fisher information matrix as a metric. We compute the Fisher information matrix for the GPDs and introduce a feature vector describing a GPD by its parameters and different geometrical properties of its Fisher information matrix. In our statistical analysis we use eye tracker measurements in a database with 15 observers viewing 1003 images under free-viewing conditions. We use Matlab functions with their standard parameter settings and show that a naive Bayes classifier using the eigenvalues of the Fisher information matrix provides a high classification rate identifying the 15 observers in the database.

Multicenter data sharing for collaboration in sleep medicine

Sleep is a fundamental biological process crucial for survival and health of (not only) humans. But many circumstances like physiological and mental disorders, environment and lifestyle may affect healthy sleep. To date, 88 different sleep disorders are internationally recognized. They cover a broad field of medical areas. Analysis of human sleep is typically based on multidimensional biosignal recordings, so called polysomnographies (PSG). Therefore research often includes digital signal processing. Clinical sleep research is an inherent multidisciplinary field. Inter-institutional and interdisciplinary collaborations are required to address the complexity of sleep regulation and disturbance. But to date, collaborative sleep research is poorly supported by IT systems. In particular, the management and processing of PSGs is challenging. A large variety of PSG devices, data formats, measurement procedures and quality variations impedes consistent biosignal data processing. In this manuscript we introduce a virtual research platform supporting inter-institutional data sharing and processing. The infrastructure is based on XNAT—a free and open source neuroimaging research platform, a loosely coupled service oriented architecture and scalable virtualization in the back end. The system is capable of local pseudonymization of biosignal data, mapping to a standardized set of parameters and automatic quality assessment. Terms and quality measures are derived from the “Manual for the Scoring of Sleep and Associated Events” of the American Academy of Sleep Medicine (AASM), the de facto standard for diagnostic biosignal analysis in sleep medicine.

The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.

Aerodynamic Design of Separate-Jet Exhausts for Future Civil Aero-engines—Part I: Parametric Geometry Definition and Computational Fluid Dynamics Approach

This paper presents the development of an integrated approach which targets the aerodynamic design of separate-jet exhaust systems for future gas-turbine aero-engines. The proposed framework comprises a series of fundamental modeling theories which are applicable to engine performance simulation, parametric geometry definition, viscous/compressible flow solution, and design space exploration (DSE). A mathematical method has been developed based on class-shape transformation (CST) functions for the geometric design of axisymmetric engines with separate-jet exhausts. Design is carried out based on a set of standard nozzle design parameters along with the flow capacities established from zero-dimensional (0D) cycle analysis. The developed approach has been coupled with an automatic mesh generation and a Reynolds averaged Navier–Stokes (RANS) flow-field solution method, thus forming a complete aerodynamic design tool for separate-jet exhaust systems. The employed aerodynamic method has initially been validated against experimental measurements conducted on a small-scale turbine powered simulator (TPS) nacelle. The developed tool has been subsequently coupled with a comprehensive DSE method based on Latin-hypercube sampling. The overall framework has been deployed to investigate the design space of two civil aero-engines with separate-jet exhausts, representative of current and future architectures, respectively. The inter-relationship between the exhaust systems' thrust and discharge coefficients has been thoroughly quantified. The dominant design variables that affect the aerodynamic performance of both investigated exhaust systems have been determined. A comparative evaluation has been carried out between the optimum exhaust design subdomains established for each engine. The proposed method enables the aerodynamic design of separate-jet exhaust systems for a designated engine cycle, using only a limited set of intuitive design variables. Furthermore, it enables the quantification and correlation of the aerodynamic behavior of separate-jet exhaust systems for designated civil aero-engine architectures. Therefore, it constitutes an enabling technology toward the identification of the fundamental aerodynamic mechanisms that govern the exhaust system performance for a user-specified engine cycle.

Establishing quality parameters for Australian walnuts

Australian walnuts are perceived to be of high quality; however, there has been no systematic process to describe walnut quality in Australia. A set of standard parameters could provide credible evidence for any claim of 'superior quality', as well as increasing the value of the industry by producing more nuts of the desired quality. A first step in the production of high quality walnuts is to develop a better understanding of what 'quality' represents and how that quality can be achieved. A project involving six walnut producers selected from different growing regions of Australia, aimed to identify factors that may influence walnut quality. Nuts were assessed at harvest for physical attributes (size, weight, kernel colour, shell and kernel defects) and at harvest and various times during postharvest storage for hedonistic qualities (texture, taste, rancidity, aftertaste). Tests for free fatty acids and peroxide value were conducted independently and approximately 3, 6 and 9 months after harvest. All peroxide values were below 2 and all free fatty acid values below or close to 0.2 indicating that the initial high quality had not deteriorated. Storage up to 6 months appears to have no adverse effect on walnut quality if the nuts remain in-shell. Some decline in walnut quality was evident by 9 months, suggesting that storage beyond that period may become an issue. Participants now have some baseline information for developing benchmarks for Australian walnut quality and a basis from which to further develop and establish walnut quality parameters.

Interrater Reliability in Analysis of Laryngoscopic Features for Unilateral Vocal Fold Paresis

The diagnosis of paresis in patients with vocal fold motion impairment remains a challenge. In particular, laryngoscopy examination may result in significant disagreement in diagnosis among providers. We hypothesize that systematically evaluating for a standard set of clinical parameters will increase the diagnostic concordance among providers. Prospective case series conducted at a Tertiary referral Laryngology office. Two laryngologists (rater 1) and two trainees (rater 2) rated laryngoscopy findings in 19 patients suspected of paresis. The diagnosis was confirmed with laryngeal electromyogram. A standard set of 27 ratings was used for each examination that included movement, laryngeal configuration, and stroboscopy signs. A kappa coefficient was calculated for agreement in laryngoscopy findings and effectiveness in predicting the laterality of paresis. A substantial agreement (kappa coefficient>0.61) existed between the raters for vocal fold length, vocal fold thickness, bowing, and reduction in movement. A moderate agreement (kappa coefficient>0.41) existed between raters for piriform opening and reduced kinesis. The senior author was accurately able to diagnose the side of paresis in 89.5% of cases for a kappa coefficient of 0.78, whereas the trainees correctly predicted the side of paresis in 63.1% for a kappa coefficient of 0.35. The raters agreed on the diagnosis in 73.7% of cases for a kappa coefficient of 0.50. Using a standard set of laryngoscopy findings may improve the provider's ability to identify the laterality of vocal fold paresis and increase interrater reliability compared with other series.

A robust blood gene expression-based prognostic model for castration-resistant prostate cancer

BackgroundCastration-resistant prostate cancer (CRPC) is associated with wide variations in survival. Recent studies of whole blood mRNA expression-based biomarkers strongly predicted survival but the genes used in these biomarker models were non-overlapping and their relationship was unknown. We developed a biomarker model for CRPC that is robust, but also captures underlying biological processes that drive prostate cancer lethality.MethodsUsing three independent cohorts of CRPC patients, we developed an integrative genomic approach for understanding the biological processes underlying genes associated with cancer progression, constructed a novel four-gene model that captured these changes, and compared the performance of the new model with existing gene models and other clinical parameters.ResultsOur analysis revealed striking patterns of myeloid- and lymphoid-specific distribution of genes that were differentially expressed in whole blood mRNA profiles: up-regulated genes in patients with worse survival were overexpressed in myeloid cells, whereas down-regulated genes were noted in lymphocytes. A resulting novel four-gene model showed significant prognostic power independent of known clinical predictors in two independent datasets totaling 90 patients with CRPC, and was superior to the two existing gene models.ConclusionsWhole blood mRNA profiling provides clinically relevant information in patients with CRPC. Integrative genomic analysis revealed patterns of differential mRNA expression with changes in gene expression in immune cell components which robustly predicted the survival of CRPC patients. The next step would be validation in a cohort of suitable size to quantify the prognostic improvement by the gene score upon the standard set of clinical parameters.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0442-0) contains supplementary material, which is available to authorized users.

Curvature correction term as a constraint for the Skyrme interaction

The curvature correction term to the surface tension is used as a criterion for the efficiency of the Skyrme interaction in describing surface properties. Based on the nuclear equation of state, the curvature correction term to the surface tension coefficient is calculated for 97 standard Skyrme interaction parameter sets in the vicinity of nuclear saturation density at zero temperature. The main idea is to find those parametrizations that give the Tolman's $\delta$ correction close to the available theoretical predictions from the statistical theory. Only 59 out of 97 models give satisfactory results. Comparison of the obtained results with the results of the implementation of different macroscopic and microscopic constraints to Skyrme parametrizations available in the literature allows us to select 4 models that satisfy all the constraints.

The Universal Lidar Error Model (Approved for Public Release: 15-144)

AbstractMethods to adjust multiple lidar datasets, to adjust lidar with other modalities, and to quantify lidar accuracy are limited. While lidar sensor modeling, error propagation, and data adjustment exist in literature, there are no known implementations supporting all three operations within existing file formats and processing architectures. The Universal Lidar Error Model (ULEM) has been developed to meet the community-sneed for rigorous error propagation and data adjustment. ULEM exploitation allows one to develop predicted error covariance at single points and full covariance among multiple points. It defines a standardized set of adjustable parameters, provides for the modeling and storage of correlations and cross-correlations among parameters, and stores the data within existing file formats. This paper provides an introduction to ULEM, its metadata requirements, and its model exploitation methods. It concludes with an example of ULEM error modeling, showing the predicted uncertainty agrees well with errors calculated from surveyed control.

What Do We Learn from Public Good Games About Voluntary Climate Action? Evidence from an Artefactual Field Experiment

Evidence from public good game experiments holds the promise of instructive and cost-effective insights to inform environmental policy-making, for example on climate change mitigation. To fulfill the promise, such evidence needs to demonstrate generalizability to the specific policy context. This paper examines whether and under which conditions such evidence generalizes to voluntary mitigation decisions. We observe each participant in two different decision tasks: a real giving task in which contributions are used to directly reduce CO2 emissions and a public good game. Through two treatment variations, we explore two potential shifters of generalizability in a within-subjects design: the structural resemblance of contribution incentives between the tasks and the role of the subject pool, students and non-students. Our findings suggest that cooperation in public good games is linked to voluntary mitigation behavior, albeit not in a uniform way. For a standard set of parameters, behavior in both tasks is uncorrelated. Greater structural resemblance of the public goods game leads to sizable correlations, especially for student subjects.

Simulations Based on the Cooptimization Procedure for Plated Contacts With a NiSi Interface

A cooptimization procedure has been developed for industrial solar cells metalized using a nickel seed layer thickened by a plated silver layer. A theoretical contact resistivity model for NiSi nickel silicide contacts was first computed and inserted into classical power loss equations. Optical, resistive, and recombination losses were then simulated with a standard set of parameters used in production. This procedure allows the generation of efficiency and silver consumption contour plots for homogeneous and selective emitters. For both, general guidelines are given for the optimization of the doping profile, the finger spacing, and the finger thickness.