Statistical Analysis Framework Research Articles

A method for prioritizing the modification of ergonomic and physical aspects of the workplace to enhance overall worker satisfaction in control centre buildings

This article aims to develop a method for prioritizing indoor environmental quality parameters in the workplace (i.e., temperature, lighting, acoustics, air quality, layout, furnishing, cleanliness and maintenance) to enhance occupants’ workspace satisfaction. Data were collected using a web-based survey of 12 Iranian control centre buildings (CCBs) of combined cycle power plants. The results showed that fewer than half of occupants are satisfied with their workplace. Corrective measures would cost the owners an exorbitant amount of money if they were to try to address all of the parameters. Therefore, a statistical analysis framework was applied to determine each parameter’s importance in relation to overall workspace satisfaction. Based on detailed analysis, two levels of importance have been defined for ergonomic modification of each CCB. The statistical approach developed in this study can be applied to all kinds of buildings to determine where ergonomic modification is most likely to produce higher workspace satisfaction.

ANALYZING SECOND ORDER STOCHASTICITY OF NEURAL SPIKING UNDER STIMULI-BUNDLE EXPOSURE.

Conventional analysis of neuroscience data involves computing average neural activity over a group of trials and/or a period of time. This approach may be particularly problematic when assessing the response patterns of neurons to more than one simultaneously presented stimulus. in such cases the brain must represent each individual component of the stimuli bundle, but trial-and-time-pooled averaging methods are fundamentally unequipped to address the means by which multiitem representation occurs. We introduce and investigate a novel statistical analysis framework that relates the firing pattern of a single cell, exposed to a stimuli bundle, to the ensemble of its firing patterns under each constituent stimulus. Existing statistical tools focus on what may be called "first order stochasticity" in trial-to-trial variation in the form of unstructured noise around a fixed firing rate curve associated with a given stimulus. our analysis is based upon the theoretical premise that exposure to a stimuli bundle induces additional stochasticity in the cell's response pattern in the form of a stochastically varying recombination of its single stimulus firing rate curves. We discuss challenges to statistical estimation of such "second order stochasticity" and address them with a novel dynamic admixture point process (DAPP) model. DAPP is a hierarchical point process model that decomposes second order stochasticity into a Gaussian stochastic process and a random vector of interpretable features and facilitates borrowing of information on the latter across repeated trials through latent clustering. We illustrate the utility and accuracy of the DAPP analysis with synthetic data simulation studies. We present real-world evidence of second order stochastic variation with an analysis of monkey inferior colliculus recordings under auditory stimuli.

Renormalized site density functional theory

Site density functional theory (SDFT) provides a rigorous framework for statistical mechanics analysis of inhomogeneous molecular liquids. The key defining feature of these systems is the presence of two very distinct interactions scales (intra- and inter-molecular), and as such proper description of both effects is critical to the accuracy of the calculations. Current SDFT applications utilize the same approximation scheme for both interaction motifs, which negatively impacts the results. Dual space methodology, used in this work, alleviates this issue by providing the flexibility of evaluating part of the interactions in traditional field representation. For molecular liquid this translates into retaining density representation for inter-molecular interactions but describing stiff intra-molecular remainder by more appropriate conventional field based methods. This opens the way to decouple analysis of the two interactions scales—the idea which is developed further in this work for the case of homogeneous reference approximation of inter-molecular interactions. We demonstrate that by defining new collective variables, the behaviour of the original molecular liquid system at the inter-molecular level can be transformed to resemble that of an effective simple fluid mixture. The latter is linked to intra-molecular scale through renormalized interaction parameters. We illustrate this renormalization procedure for several types of diatomic liquids, showing that this approach cures many of the shortcomings of existing SDFT methods.

Discussion of "Statistical disease mapping for heterogeneous neuroimaging studies".

This is a discussion of Liu & Zhu (2021), which develops a novel statistical disease mapping framework for neuroimaging data analysis.

Discussion of “Statistical disease mapping for heterogeneous neuroimaging studies”

AbstractThis is a discussion of Liu & Zhu (2021), which develops a novel statistical disease mapping framework for neuroimaging data analysis.

Towards Real-World Applications of ServiceX, an Analysis Data Transformation System

One of the biggest challenges in the High-Luminosity LHC (HLLHC) era will be the significantly increased data size to be recorded and analyzed from the collisions at the ATLAS and CMS experiments. ServiceX is a software R&D project in the area of Data Organization, Management and Access of the IRIS-HEP to investigate new computational models for the HLLHC era. ServiceX is an experiment-agnostic service to enable on-demand data delivery specifically tailored for nearly-interactive vectorized analyses. It is capable of retrieving data from grid sites, on-the-fly data transformation, and delivering user-selected data in a variety of different formats. New features will be presented that make the service ready for public use. An ongoing effort to integrate ServiceX with a popular statistical analysis framework in ATLAS will be described with an emphasis of a practical implementation of ServiceX into the physics analysis pipeline.

MEScan: a powerful statistical framework for genome-scale mutual exclusivity analysis of cancer mutations.

Cancer somatic driver mutations associated with genes within a pathway often show a mutually exclusive pattern across a cohort of patients. This mutually exclusive mutational signal has been frequently used to distinguish driver from passenger mutations and to investigate relationships among driver mutations. Current methods for de novo discovery of mutually exclusive mutational patterns are limited because the heterogeneity in background mutation rate can confound mutational patterns, and the presence of highly mutated genes can lead to spurious patterns. In addition, most methods only focus on a limited number of pre-selected genes and are unable to perform genome-wide analysis due to computational inefficiency. We introduce a statistical framework, MEScan, for accurate and efficient mutual exclusivity analysis at the genomic scale. Our framework contains a fast and powerful statistical test for mutual exclusivity with adjustment of the background mutation rate and impact of highly mutated genes, and a multi-step procedure for genome-wide screening with the control of false discovery rate. We demonstrate that MEScan more accurately identifies mutually exclusive gene sets than existing methods and is at least two orders of magnitude faster than most methods. By applying MEScan to data from four different cancer types and pan-cancer, we have identified several biologically meaningful mutually exclusive gene sets. MEScan is available as an R package at https://github.com/MarkeyBBSRF/MEScan. Supplementary data are available at Bioinformatics online.

A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies with the emerging pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information for the purpose of assessing the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors for quantifying intervention impacts at a finer granularity. Then we developed a data assimilation framework for estimating these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is then built to quantify the impact of intervention strategies by monitoring the evolution of these estimated parameters. By Investigating the impacts of intervention measures of European countries, the United States and Wuhan with the framework, we reveal the effects of interventions in these countries and the resurgence risk in the USA.

Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach

This study presents a steady-state Markov chain model to predict the long-term probability of drought conditions. The research aims to propose a rigorous framework for statistical analysis of drought characteristics and its trends over time for a large area of aquifers and plains in Iran. For this purpose, two meteorological indicators called the Standardized Precipitation Index (SPI), and the Groundwater Resource Index (GRI) are examined. The groundwater drought study includes more than 26,000 wells in about 600 meteorological stations over 20 years being surveyed daily. This study discusses the spatial interpolation of drought steady-state probabilities based on recorded SPI and GRI data at three intervals, i.e., 1994 to 2004, 2005–2015, and 1994 to 2015. The final zoning of the system results in an average increase in the steady-state constant of the SPI index in the first half of the whole study period to approximately 62%. While in the second period of study, the average percentage of the steady-state climatic drought was calculated to be 75%. The average amount of drought in the extended study area of the country was found to be up to 46%.

Operational Bayesian GLS Regression for Regional Hydrologic Analyses

AbstractThis paper develops the quasi‐analytic Bayesian analysis of the generalized least squares (GLS) (B‐GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R2, pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region‐of‐influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B‐GLS and the new diagnostic statistics are illustrated with a regional log‐space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method‐of‐moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B‐GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140). B‐GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal.

Handling confounding variables in statistical shape analysis - application to cardiac remodelling.

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, such as demographics might invalidate the analysis if not taken into consideration. Despite the methodological advances in the field, providing new methods that are able to capture complex and regional shape differences, the relationship between non-imaging information and shape variability has been overlooked. We present a linear statistical shape analysis framework that finds shape differences unassociated to a controlled set of confounding variables. It includes two confounding correction methods: confounding deflation and adjustment. We applied our framework to a cardiac magnetic resonance imaging dataset, consisting of the cardiac ventricles of 89 triathletes and 77 controls, to identify cardiac remodelling due to the practice of endurance exercise. To test robustness to confounders, subsets of this dataset were generated by randomly removing controls with low body mass index, thus introducing imbalance. The analysis of the whole dataset indicates an increase of ventricular volumes and myocardial mass in athletes, which is consistent with the clinical literature. However, when confounders are not taken into consideration no increase of myocardial mass is found. Using the downsampled datasets, we find that confounder adjustment methods are needed to find the real remodelling patterns in imbalanced datasets.

Multivariate groundwater drought analysis using copulas

Abstract Drought characteristics are among major inputs in the planning and management of water resources. Although numerous studies on probabilistic aspects of meteorological drought characteristics and their joint distribution functions have been reported, multivariate analysis of groundwater (GW) drought is rarely available. In this paper, while proposing a framework for statistical analysis of disturbed hydrological systems, copula-based multivariate GW drought analysis was performed in an over-drafted aquifer. For this purpose, a 1,000-year synthetic time series of naturalized GW level was produced. GW drought was monitored via the Standardized GW Index (SGI) index while the multivariate GW drought probability and return period were determined via copulas. Comparison between the copula and empirical GW drought probabilities using statistical goodness-of-fit tests proved sufficient accuracy of copula models in multivariate drought analysis. The results showed strong dependence among GW drought characteristics. Generally speaking, multivariate GW drought analysis incorporates major drought characteristics and provides concrete scientific basis for planning drought management strategies.

Modeling the density gradient of 3D nanofiber scaffolds fabricated by divergence electrospinning

Following recent insights on structure-cell-function interactions and the critical role of the extracellular matrix (ECM), the latest biofabrication approaches have increasingly focused on designing materials with biomimetic microarchitecture. Divergence electrospinning is a novel fabrication method for three-dimensional (3D) nanofiber scaffolds. It is introduced to produce 3D nanofiber mats that have numerous applications in regenerative medicine and tissue engineering. One of the most important characteristics of 3D nanofiber mats is the density gradient. This study provides a statistical analysis and response surface modeling framework based on experimental data to evaluate the manner by which the geometric designs of double-bevel collectors influence the fiber density gradient. Specifically, variance of analysis and sensitivity analysis were performed to identify parameters that had significant effects, and a response surface model embedded with seven location indicators was developed to predict the spatial distribution of fiber density for different collector designs. It was concluded that the collector height, bevel angle, and their interactions were significant factors influencing the density gradient. This study revealed the sensitivity of system configuration and provided an optimization tool for process controllability of microstructure gradients.

A Statistical Framework for Performance Analysis of Diversity Framed Slotted Aloha With Interference Cancellation

As the Internet of Things (IoT)/machine-to-machine market is rapidly growing, a special role is expected to be played by satellite communications in that they offer ubiquitous coverage and therefore enable typical monitoring, telemetry, and control services also in areas with a poor terrestrial infrastructure. In this respect, the case of massive IoT devices deployment calls for random access solutions, which have been long analyzed by the scientific satellite community in the last ten years. This article further elaborates on the relation between the normalized offered load and the achievable performance in terms of packet loss rate, which was not much addressed so far at high loads. The proposed theoretical framework has been validated through extensive simulation campaigns, which show an excellent match at different loads and number of interfering packets configurations, by significantly improving the results achievable through other existing theoretical frameworks.

Statistical analysis for predicting location-specific data center PUE and its improvement potential

This paper presents a statistical framework for predictive analysis of data center power usage effectiveness (PUE), with a focus on hyperscale data centers (HDCs). Thermodynamics-based PUE models considering representative economizer choices are proposed, taking climate variables and energy system parameters as inputs for robust PUE predictions. Sobol’s method is used to assess total order sensitivity indices of key modeling parameters, suggesting that climate variables and uninterruptible power supply (UPS) efficiencies are the most important parameters. The PUE values of 17 HDCs operated by Google and Facebook were predicted, considering location-specific weather conditions, and uncertainties in energy system parameters and economizer choices. Results were verified using reported PUE values, indicating the model’s effectiveness in capturing regional and seasonal PUE variations, and in generating point-estimations for macro-level data center (DC) energy models. Finally, achievable PUE values were computed through differential evolution, identifying minimum practical PUE values that could be obtained with state-of-the-art technologies. The framework can be applied in predictions of location-specific PUE values, PUE improvement analysis, and PUE target-setting by policy makers.

Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease.

Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of porA in Campylobacter jejuni using spatiotemporal analysis over 30 years. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This framework, termed μPathML, defined intestinal and extraintestinal groups that have differential allelic porA variants that cause abortion. Divergent variants containing indels that defeated automated annotation were rescued using biological context and knowledge that resulted in defining rare, divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms.

Gamma-Ray Dark Matter Searches in Milky Way Satellites—A Comparative Review of Data Analysis Methods and Current Results

If dark matter is composed of weakly interacting particles with mass in the GeV-TeV range, their annihilation or decay may produce gamma rays that could be detected by gamma-ray telescopes. Observations of dwarf spheroidal satellite galaxies of the Milky Way (dSphs) benefit from the relatively accurate predictions of dSph dark matter content to produce robust constraints to the dark matter properties. The sensitivity of these observations for the search for dark matter signals can be optimized thanks to the use of advanced statistical techniques able to exploit the spectral and morphological peculiarities of the expected signal. In this paper, I review the status of the dark matter searches from observations of dSphs with the current generation of gamma-ray telescopes: Fermi-LAT, H.E.S.S, MAGIC, VERITAS and HAWC. I will describe in detail the general statistical analysis framework used by these instruments, putting in context the most recent experimental results and pointing out the most relevant differences among the different particular implementations. This will facilitate the comparison of the current and future results, as well as their eventual integration in a multi-instrument and multi-target dark matter search.

Verifying molecular clusters by 2-color localization microscopy and significance testing

While single-molecule localization microscopy (SMLM) offers the invaluable prospect to visualize cellular structures below the diffraction limit of light microscopy, its potential has not yet been fully capitalized due to its inherent susceptibility to blinking artifacts. Particularly, overcounting of single molecule localizations has impeded a reliable and sensitive detection of biomolecular nanoclusters. Here we introduce a 2-Color Localization microscopy And Significance Testing Approach (2-CLASTA), providing a parameter-free statistical framework for the qualitative analysis of two-dimensional SMLM data via significance testing methods. 2-CLASTA yields p-values for the null hypothesis of random biomolecular distributions, independent of the blinking behavior of the chosen fluorescent labels. The method is parameter-free and does not require any additional measurements nor grouping of localizations. We validated the method both by computer simulations as well as experimentally, using protein concatemers as a mimicry of biomolecular clustering. As the new approach is not affected by overcounting artifacts, it is able to detect biomolecular clustering of various shapes at high sensitivity down to a level of dimers.

How Are Managers Making Tree Species Selection Decisions in the Pacific Northwest of the United States?

Trees provide an array of social, economic, and ecological benefits; furthermore, trees on public land are critical for providing those benefits to people who cannot afford their own trees. It is important to know how managers make trade-offs and prioritize different tree selection criteria in order to target educational campaigns at the state or regional level. Primary contacts for Tree City USA designated cities were surveyed across the Pacific Northwest. Of these municipalities, 79 out of 151 responded (52.3% response rate), with 6 municipalities providing responses from different departments for a total of 85 responses. Currently, there are primarily descriptive statistics in relation to tree species selection. This study provides a framework for future statistical analysis and greater exploration of how municipalities and managers are selecting tree species. Results were analyzed with a Mann-Whitney U test to compare International Society of Arboriculture (ISA) Certified Arborists® to those who are not certified across various tree species selection criteria. Another Mann-Whitney U test was used to compare small (≤ 50,000) and large (> 50,000) municipalities across the same criteria. ISA Certified Arborists® showed statistically significant differences from those who are not certified in a number of tree species selection criteria. ISA Certified Arborists® also differed in urban forest management on a city-wide scale, particularly in favoring greater tree species diversity. The differences in urban forest management between ISA Certified Arborists® and noncertified—and between municipality sizes—can help to influence future educational campaigns targeted toward increasing urban forest health and resiliency.

Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters.

Parameters of mathematical models used in biology may be genotype-specific and regarded as new traits. Therefore, an accurate estimation of these parameters and the association mapping on the estimated parameters can lead to important findings regarding the genetic architecture of biological processes. In this study, a statistical framework for a joint analysis (JA) of model parameters and genome-wide marker effects on these parameters was proposed and evaluated. In the simulation analyses based on different types of mathematical models, the JA inferred the model parameters and identified the responsible genomic regions more accurately than the independent analysis (IA). The JA of real plant data provided interesting insights into photosensitivity, which were uncovered by the IA. The statistical framework is provided by the R package GenomeBasedModel available at https://github.com/Onogi/GenomeBasedModel. All R and C++ scripts used in this study are also available at the site. Supplementary data are available at Bioinformatics online.