scholarly journals Simple Degree-of-Freedom Modeling of the Random Fluctuation Arising in Human–Bicycle Balance

2019 ◽  
Vol 9 (10) ◽  
pp. 2154 ◽  
Author(s):  
Katsutoshi Yoshida ◽  
Keishi Sato ◽  
Yoshikazu Yamanaka
Keyword(s):  
Angular Displacement ◽  
Degree Of Freedom ◽  
Training Data ◽  
Statistical Hypothesis ◽  
Random Fluctuation ◽  
Model Parameters ◽  
Statistical Hypothesis Testing ◽  
Measured Time ◽  
Kolmogorov Smirnov ◽  
Fluctuation Model

In this study, we propose a new simple degree-of-freedom fluctuation model that accurately reproduces the probability density functions (PDFs) of human–bicycle balance motions as simply as possible. First, we measure the time series of the roll angular displacement and velocity of human–bicycle balance motions and construct their PDFs. Next, using these PDFs as training data, we identify the model parameters by means of particle swarm optimization; in particular, we minimize the Kolmogorov–Smirnov distance between the human PDFs from the participants and the PDFs simulated by our model. The resulting PDF fitnesses were over 98.7 % for all participants, indicating that our simulated PDFs were in close agreement with human PDFs. Furthermore, the Kolmogorov–Smirnov statistical hypothesis testing was applied to the resulting human–bicycle fluctuation model, showing that the measured time responses were much better supported by our model than the Gaussian distribution.

scholarly journals Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lukas Vlcek ◽  
Shize Yang ◽  
Yongji Gong ◽  
Pulickel Ajayan ◽  
Wu Zhou ◽  
...  
Keyword(s):  
Effective Interaction ◽  
Mean Field ◽  
Interaction Model ◽  
Optimization Techniques ◽  
Statistical Hypothesis ◽  
Structure Property ◽  
Statistical Hypothesis Testing ◽  
Stochastic Representation ◽  
Scanning Transmission ◽  
Complex Structural

AbstractExploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of MoxRe1-xS2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

scholarly journals Cutaneous Squamous Cell Carcinoma in Patients with Hidradenitis Suppurativa

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1153
Author(s):  
Elysia Racanelli ◽  
Abdulhadi Jfri ◽  
Amnah Gefri ◽  
Elizabeth O’Brien ◽  
Ivan Litvinov ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Hidradenitis Suppurativa ◽  
Case Reports ◽  
Rare Complication ◽  
Cox Proportional Hazards ◽  
Cutaneous Squamous Cell Carcinoma ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing

Background: Cutaneous squamous cell carcinoma (cSCC) is a rare complication of hidradenitis suppurativa (HS). Objectives: To conduct a systematic review and an individual patient data (IPD) meta-analysis to describe the clinical characteristics of HS patients developing cSCC and determine predictors of poor outcome. Methods: Medline/PubMed, Embase, and Web of Science were searched for studies reporting cSCC arising in patients with HS from inception to December 2019. A routine descriptive analysis, statistical hypothesis testing, and Kaplan–Meier survival curves/Cox proportional hazards regression models were performed. Results: A total of 34 case reports and series including 138 patients were included in the study. The majority of patients were males (81.6%), White (83.3%), and smokers (n = 22/27 reported) with a mean age of 53.5 years. Most patients had gluteal (87.8%), Hurley stage 3 HS (88.6%). The mean time from the diagnosis of HS to the development of cSCC was 24.7 years. Human papillomavirus was identified in 12/38 patients tested. Almost 50% of individuals had nodal metastasis and 31.3% had distant metastases. Half of the patients succumbed to their disease. Conclusions: cSCC is a rare but life-threatening complication seen in HS patients, mainly occurring in White males who are smokers with severe, long-standing gluteal HS. Regular clinical examination and biopsy of any suspicious lesions in high-risk patients should be considered. The use of HPV vaccination as a preventive and possibly curative method needs to be explored.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

High‐resolution velocity determination: Statistical phase correlation and image processing

Geophysics ◽  
1996 ◽  
Vol 61 (4) ◽  
pp. 1115-1127 ◽  
Author(s):  
Igor B. Morozov ◽  
Scott B. Smithson
Keyword(s):  
Image Processing ◽  
High Resolution ◽  
Phase Correlation ◽  
Statistical Hypothesis ◽  
Original Form ◽  
Velocity Spectrum ◽  
Statistical Hypothesis Testing ◽  
Simple Estimate ◽  
Optimal Velocity ◽  
Velocity Spectra

We address three areas of the problem of the stacking velocity determination: (1) the development of a new high‐resolution velocity determination technique, (2) the choice of an optimal velocity trial scenario, and (3) a unified approach to the comparison of time‐velocity spectra produced by various methods. We present a class of high‐resolution coherency measures providing five‐eight times better velocity resolution than conventional measures. The measure is based on the rigorous theory of statistical hypothesis testing and on the statistics of directional data. In its original form, our method analyzes only the phase distributions of the data, thus making unnecessary careful spherical divergence corrections and other normalization procedures. Besides the statistical one, we develop an “instantaneous” version of the conventional coherency measure. This measure is based on the concept of the trace envelope, thus eliminating the need for an averaging procedure. Finally, we design a hybrid high‐resolution coherency measure, incorporating the latter and the statistical one. Carrying out a systematic comparison of various measures of coherency, we present a simple estimate of an attainable velocity resolution. Based on this estimate, we define an optimal velocity grid, providing uniform coverage of all details of the time‐velocity spectrum. To facilitate quantitative comparisons of different coherency functions, we develop a unified normalization approach, based on techniques known in image processing. Described methods are tested on synthetic and field data. In both cases, we obtained a remarkable improvement in the time‐velocity resolution. The methods are general, very simple in implementation, and robust and reliable in application.

Improved resolution of recalcitrant nodes in the animal phylogeny through the analysis of genome gene content and morphology

2021 ◽  
Author(s):  
Ksenia Juravel ◽  
Luis Porras ◽  
Sebastian Hoehna ◽  
Davide Pisani ◽  
Gert Wörheide
Keyword(s):  
Common Ancestor ◽  
Sister Group ◽  
The Other ◽  
Gene Content ◽  
Statistical Hypothesis ◽  
Phylogenetic Position ◽  
Last Common Ancestor ◽  
Statistical Hypothesis Testing ◽  
Animal Phylogeny ◽  
Phylogenetic Hypotheses

An accurate phylogeny of animals is needed to clarify their evolution, ecology, and impact on shaping the biosphere. Although multi-gene alignments of up to several hundred thousand amino acids are nowadays routinely used to test hypotheses of animal relationships, some nodes towards the root of the animal phylogeny are proving hard to resolve. While the relationships of the non-bilaterian lineages, primarily sponges (Porifera) and comb jellies (Ctenophora), have received much attention since more than a decade, controversies about the phylogenetic position of the worm-like bilaterian lineage Xenacoelomorpha and the monophyly of the "Superphylum" Deuterostomia have more recently emerged. Here we independently analyse novel genome gene content and morphological datasets to assess patterns of phylogenetic congruence with previous amino-acid derived phylogenetic hypotheses. Using statistical hypothesis testing, we show that both our datasets very strongly support sponges as the sister group of all the other animals, Xenoacoelomorpha as the sister group of the other Bilateria, and largely support monophyletic Deuterostomia. Based on these results, we conclude that the last common animal ancestor may have been a simple, filter-feeding organism without a nervous system and muscles, while the last common ancestor of Bilateria might have been a small, acoelomate-like worm without a through gut.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

Sign in / Sign up

Export Citation Format

Share Document