Global Statistical Approach Research Articles

Riluzole for Degenerative Cervical Myelopathy

The modified Japanese Orthopaedic Association (mJOA) scale is the most common scale used to represent outcomes of degenerative cervical myelopathy (DCM); however, it lacks consideration for neck pain scores and neglects the multidimensional aspect of recovery after surgery. To use a global statistical approach that incorporates assessments of multiple outcomes to reassess the efficacy of riluzole in patients undergoing spinal surgery for DCM. This was a secondary analysis of prespecified secondary end points within the Efficacy of Riluzole in Surgical Treatment for Cervical Spondylotic Myelopathy (CSM-PROTECT) trial, a multicenter, double-blind, phase 3 randomized clinical trial conducted from January 2012 to May 2017. Adult surgical patients with DCM with moderate to severe myelopathy (mJOA scale score of 8-14) were randomized to receive either riluzole or placebo. The present study was conducted from July to December 2023. Riluzole (50 mg twice daily) or placebo for a total of 6 weeks, including 2 weeks prior to surgery and 4 weeks following surgery. The primary outcome measure was a difference in clinical improvement from baseline to 1-year follow-up, assessed using a global statistical test (GST). The 36-Item Short Form Health Survey Physical Component Score (SF-36 PCS), arm and neck pain numeric rating scale (NRS) scores, American Spinal Injury Association (ASIA) motor score, and Nurick grade were combined into a single summary statistic known as the global treatment effect (GTE). Overall, 290 patients (riluzole group, 141; placebo group, 149; mean [SD] age, 59 [10.1] years; 161 [56%] male) were included. Riluzole showed a significantly higher probability of global improvement compared with placebo at 1-year follow-up (GTE, 0.08; 95% CI, 0.00-0.16; P = .02). A similar favorable global response was seen at 35 days and 6 months (GTE for both, 0.07; 95% CI, -0.01 to 0.15; P = .04), although the results were not statistically significant. Riluzole-treated patients had at least a 54% likelihood of achieving better outcomes at 1 year compared with the placebo group. The ASIA motor score and neck and arm pain NRS combination at 1 year provided the best-fit parsimonious model for detecting a benefit of riluzole (GTE, 0.11; 95% CI, 0.02-0.16; P = .007). In this secondary analysis of the CSM-PROTECT trial using a global outcome technique, riluzole was associated with improved clinical outcomes in patients with DCM. The GST offered probability-based results capable of representing diverse outcome scales and should be considered in future studies assessing spine surgery outcomes.

Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data.

Direct coupling analysis (DCA) is a global statistical approach that uses information encoded in protein sequence data to predict spatial contacts in a three-dimensional structure of a folded protein. DCA has been widely used to predict the monomeric fold at amino acid resolution and to identify biologically relevant interaction sites within a folded protein. Going beyond single proteins, DCA has also been used to identify spatial contacts that stabilize the interaction in protein complex formation. However, extracting this higher order information necessary to predict dimer contacts presents a significant challenge. A DCA evolutionary signal is much stronger at the single protein level (intraprotein contacts) than at the protein-protein interface (interprotein contacts). Therefore, if DCA-derived information is to be used to predict the structure of these complexes, there is a need to identify statistically significant DCA predictions. We propose a simple Z-score measure that can filter good predictions despite noisy, limited data. This new methodology not only improves our prediction ability but also provides a quantitative measure for the validity of the prediction.

Koopman analysis in oscillator synchronization.

Synchronization is an important dynamical phenomenon in coupled nonlinear systems, which has been studied extensively in recent years. However, analysis focused on individual orbits seems hard to extend to complex systems, while a global statistical approach is overly cursory. Koopman operator technique seems to balance well the two approaches. In this paper we extend Koopman analysis to the study of synchronization of coupled oscillators by extracting important eigenvalues and eigenfunctions from the observed time series. A renormalization group analysis is designed to derive an analytic approximation of the eigenfunction in the case of weak coupling that dominates the oscillation. For moderate or strong couplings, numerical computation further confirms the importance of the average frequencies and the associated eigenfunctions. The synchronization transition points could be located with quite high accuracy by checking the correlation of neighboring eigenfunctions at different coupling strengths, which is readily applied to other nonlinear systems.

Microbial community analysis with a specific statistical approach after a record breaking snowfall in Southern Italy

PurposeSnow and ice ecosystems present unexpectedly high microbial abundance and diversity. Although arctic and alpine snow environments have been intensively investigated from a microbiological point of view, few studies have been conducted in the Apennines. Accordingly, the main purpose of this research was to analyze the microbial communities of the snow collected in two different locations of Capracotta municipality (Southern Italy) after a snowfall record occurred on March 2015 (256 cm of snow in less than 24 h).MethodsBacterial communities were analyzed by the Next-Generation Sequencing techniques. Furthermore, a specific statistical approach for taxonomic hierarchy data was introduced, both for the assessment of diversity within microbial communities and the comparison between different microbiotas. In general, diversity and similarity indices are more informative when computed at the lowest level of the taxonomic hierarchy, the species level. This is not the case with microbial data, for which the species level is not necessarily the most informative. Indeed, the possibility to detect a large number of unclassified records at every level of the hierarchy (even at the top) is very realistic due to both the partial knowledge about the cultivable fraction of microbial communities and limitations to taxonomic assignment connected to the quality and completeness of the 16S rRNA gene reference databases. Thus, a global approach considering information from the whole taxonomic hierarchy was adopted in order to obtain a more consistent assessment of the biodiversity.ResultThe main phyla retrieved in the investigated snow samples were Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes. Interestingly, DNA from bacteria adapted to thrive at low temperatures, but also from microorganisms normally associated with other habitats, whose presence in the snow could be justified by wind-transport, was found. Biomolecular investigations and statistical data analysis showed relevant differences in terms of biodiversity, composition, and distribution of bacterial species between the studied snow samples.ConclusionThe relevance of this research lies in the expansion of knowledge about microorganisms associated with cold environments in contexts poorly investigated such as the Italian Apennines, and in the development of a global statistical approach for the assessment of biological diversity and similarity of microbial communities as an additional tool to be usefully combined with the barcoding methods.

PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN

In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.

Global statistical features-based approach for Acoustic Event Detection

The analysis of acoustic data typically discusses the problem of segmenting the acoustic events into non-overlapping acoustically compact categories. In Acoustic Event Detection (AED), an acoustic event is categorized into speech and non-speech events. Detection of non-speech sounds such as scream, gun shots, explosions, and glass break events is very helpful in acoustic surveillance, multimedia information retrieval, and acoustic forensic applications. In this paper, we propose global statistical features-based representation for multi-variate varying length acoustic data. A discriminative model-based classifier is then used to classify different acoustic events. The proposed representation is of very less dimension. The proposed approach is evaluated on surveillance-oriented AED datasets such as CICESE (recorded from a smart room scenario), Environmental Sound Classification (ESC), and IEEE AASP/DCASE2013 (Office environment) datasets. The proposed approach gives a better performance when compared with the conventional Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM) approaches.

New axion and hidden photon constraints from a solar data global fit

We present a new statistical analysis that combines helioseismology (sound speed, surface helium and convective radius) and solar neutrino observations (the 8B and 7Be fluxes) to place upper limits to the properties of non standard weakly interacting particles. Our analysis includes theoretical and observational errors, accounts for tensions between input parameters of solar models and can be easily extended to include other observational constraints. We present two applications to test the method: the well studied case of axions and axion-like particles and the more novel case of low mass hidden photons. For axions we obtain an upper limit at 3σ for the axion-photon coupling constant of gaγ < 4.1 · 10−10 GeV−1. For hidden photons we obtain the most restrictive upper limit available accross a wide range of masses for the product of the kinetic mixing and mass of χ m < 1.8 ⋅ 10−12 eV at 3σ. Both cases improve the previous solar constraints based on the Standard Solar Models showing the power of using a global statistical approach.

Improved contact predictions using the recognition of protein like contact patterns.

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.

Radar Backscatter Modeling Based on Global TanDEM-X Mission Data

Radar backscatter knowledge is a valuable input for many remote sensing applications, which are based on synthetic aperture radar (SAR) systems. The amount of spaceborne data acquired within the TanDEM-X mission allows for the characterization of X-band backscatter on a global scale. The objective of this paper is to present a method for the characterization of radar backscatter behavior using a global statistical approach. The worldwide data set of images acquired within the TanDEM-X mission is taken into account, having the chance to exploit the unique high-quality topography information associated to it. The input measurements are differently assessed, by using a quality-based approach. A series of models can be derived, focusing on the backscatter dependence on polarization, incidence angle, ground classification, and seasonal time. The developed approach for the algorithm's verification is presented as well, together with some preliminary results obtained from TanDEM-X mission data. The generation of up-to-date backscatter models for X-band will provide a useful database for the development of a large number of scientific applications and for the optimization of future radar systems.

Calibration and Improved Prediction of Computer Models by Universal Kriging

This paper addresses the use of experimental data for calibrating a computer model and improving its predictions of the underlying physical system. A global statistical approach is proposed in which the bias between the computer model and the physical system is modeled as a realization of a Gaussian process. The application of classical statistical inference to this statistical model yields a rigorous method for calibrating the computer model and for adding to its predictions a statistical correction based on experimental data. This statistical correction can substantially improve the calibrated computer model for predicting the physical system on new experimental conditions. Furthermore, a quantification of the uncertainty of this prediction is provided. Physical expertise on the calibration parameters can also be taken into account in a Bayesian framework. Finally, the method is applied to the thermal-hydraulic code FLICA 4, in a single-phase friction model framework. It allows significant improvement of the predictions of FLICA 4.

Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis

Signal transduction proteins such as bacterial sensor histidine kinases, designed to transition between multiple conformations, are often ruled by unstable transient interactions making structural characterization of all functional states difficult. This study explored the inactive and signal-activated conformational states of the two catalytic domains of sensor histidine kinases, HisKA and HATPase. Direct coupling analyses, a global statistical inference approach, was applied to >13,000 such domains from protein databases to identify residue contacts between the two domains. These contacts guided structural assembly of the domains using MAGMA, an advanced molecular dynamics docking method. The active conformation structure generated by MAGMA simultaneously accommodated the sequence derived residue contacts and the ATP-catalytic histidine contact. The validity of this structure was confirmed biologically by mutation of contact positions in the Bacillus subtilis sensor histidine kinase KinA and by restoration of activity in an inactive KinA(HisKA):KinD(HATPase) hybrid protein. These data indicate that signals binding to sensor domains activate sensor histidine kinases by causing localized strain and unwinding at the end of the C-terminal helix of the HisKA domain. This destabilizes the contact positions of the inactive conformation of the two domains, identified by previous crystal structure analyses and by the sequence analysis described here, inducing the formation of the active conformation. This study reveals that structures of unstable transient complexes of interacting proteins and of protein domains are accessible by applying this combination of cross-validating technologies.

A statistical approach for the evaluation of mechanical properties of silica/epoxy nanocomposite: Verification by experiments

In this study, we have developed a global statistical approach for the characterization of the elastic properties of silica/epoxy nanocomposites. In this approach, we selected a series of scanning electron microscopy (SEM) images of a nanocomposite sample as a representation of the microstructure (representative volume element, RVE). In the next step, we used statistical continuum theory by strong-contrast method for the evaluation of the effective elastic modulus of each SEM image. In the final step, we used 2-dimensional (2D) finite element modeling for the evaluation of the effective elastic modulus of the nanocomposite sample consisting of the preselected set of SEM images (RVE). In addition to the global statistical approach, we have also developed a 3-dimensional (3D) finite element model with the assumption of perfect dispersion of silica nanoparticles inside the epoxy matrix. Comparison between the experimental measurements for the nanocomposite elastic modulus and modeling results by the global statistical approach reveals a fine agreement. We also found that the predictions by the 3D finite element method with perfect dispersion of particles underestimate the experimental results.

Correlating Strain and Acoustic Emission Signals of Metallic Component Using Global Signal Statistical Approach

This study clarifies the fatigue properties using global signal statistical approach during the crack initiation stage for metallic component. Strain loading and acoustic emission (AE) signatures are captured simultaneously in the form of signal waves recorded by strain gauge and AE transducer. An initial study was carried out in the laboratory on medium carbon AISI 1045 steel specimens at constant amplitude cyclic loading condition, which lead to the fatigue failure characteristics. This study was carried out to investigate the relationship between strain and AE signals in order to make sure that AE technique also can be used as a detecting and monitoring crack initiation in metallic specimens. To achieve the goal, three different loads were applied on three specimens to capture the differences of the signals. The specific data acquisition systems were used to collect strain and AE signatures. For the purpose of analysis, the method of root mean square (r.m.s) and the kurtosis were used. The r.m.s value was used to quantify the overall vibrational energy content whereas the kurtosis was then used because of its sensitivity to high amplitude events. Based on these statistical approaches, the correlation patterns between both signals are expected to give a meaningful baseline to predict and monitor crack initiation of a metallic specimen.

Correlating Strain and Acoustic Emission Signals of Metallic Component Using Global Signal Statistical Approach

This study clarifies the fatigue properties using global signal statistical approach during the crack initiation stage for metallic component. Strain loading and acoustic emission (AE) signatures are captured simultaneously in the form of signal waves recorded by strain gauge and AE transducer. An initial study was carried out in the laboratory on medium carbon AISI 1045 steel specimens at constant amplitude cyclic loading condition, which lead to the fatigue failure characteristics. This study was carried out to investigate the relationship between strain and AE signals in order to make sure that AE technique also can be used as a detecting and monitoring crack initiation in metallic specimens. To achieve the goal, three different loads were applied on three specimens to capture the differences of the signals. The specific data acquisition systems were used to collect strain and AE signatures. For the purpose of analysis, the method of root mean square (r.m.s) and the kurtosis were used. The r.m.s value was used to quantify the overall vibrational energy content whereas the kurtosis was then used because of its sensitivity to high amplitude events. Based on these statistical approaches, the correlation patterns between both signals are expected to give a meaningful baseline to predict and monitor crack initiation of a metallic specimen.

An Improved Retrieval Algorithm for Water Vapor Retrieval: Application to the Envisat Microwave Radiometer

Algorithms proposed to retrieve water vapor are usually developed using statistical regression. Recent results have shown that this global statistical approach creates systematic geographical errors implying systematic biases. In the particular case of the wet tropospheric correction for altimetry missions, these biases directly distort the sea level estimation. After an in-depth analysis of the retrieval errors, we propose a new approach that significantly improves the quality of the retrieval algorithm.

Remote sensing for runoff predetermination in Sahel

Rainfall-runoff modelling is basically used to simulate the response of gauged calibrated catchments. However, the problem usually is to predict an output discharge without prior calibration hence from the catchment physical and geomorphologic characteristics. Because remote sensing is essentially limited to the assessment of the ground surface characteristics, it appears particularly suited to sahelian catchments for which stormflow is generally accepted to be controlled by the soil toplayer. This paper presents the potential contribution of remote sensing to predetermine the discharge at the outlet of small sahelian catchments on an event basis. Results from a distributed approach that aims to upscale runoff from local remotely-sensed «infiltration excesså are discussed, and possible extensions are proposed. Finally, it is suggested that a complementary global statistical approach tending to downscale relevant hydrologic parameters derived from remote sensing could be very valuable from an operational point of view.

A global approach to the n-dimensional traveling salesman problem: application to the optimization of crystallographic data collection

An algorithm for the optimization of data collection time has been written and a subsequent computer program tested for diffractometer systems. The program, which utilizes a global statistical approach to the traveling salesman problem, yields reasonable solutions in a relatively short time. The algorithm has been successful in treating very large data sets (up to 4000 points) in three dimensions with subsequent time savings of ca 30%.