Statistical Interpretation Research Articles

Measurement of the total and differential Higgs boson production cross-sections at sqrt{s} = 13 TeV with the ATLAS detector by combining the H → ZZ*→ 4ℓ and H → γγ decay channels

The total and differential Higgs boson production cross-sections are measured through a combined statistical analysis of the H → ZZ* → 4ℓ and H → γγ decay channels. The results are based on a dataset of 139 fb−1 of proton–proton collisions at a centre-of-mass energy of 13 TeV, recorded by the ATLAS detector at the Large Hadron Collider. The measured total Higgs boson production cross-section is {55.5}_{-3.8}^{+4.0} pb, consistent with the Standard Model prediction of 55.6 ± 2.5 pb. All results from the two decay channels are compatible with each other, and their combination agrees with the Standard Model predictions. A combined statistical interpretation of the measured fiducial cross-sections as a function of the Higgs boson transverse momentum is performed in order to probe the Yukawa couplings to the bottom and charm quarks. A similar interpretation is performed by including also the constraints from the measurements of Higgs boson production in association with a W or Z boson in the H → boverline{b} and coverline{c} decay channels.

Assessment of Prediction Uncertainty Quantification Methods in Systems Biology.

Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitably possess some degree of uncertainty. In practical applications it is important to quantify these parameter uncertainties as well as the resulting prediction uncertainty, which are uncertainties of potentially time-dependent model characteristics. Unfortunately, estimating prediction uncertainties accurately is nontrivial, due to the nonlinear dependence of model characteristics on parameters. While a number of numerical approaches have been proposed for this task, their strengths and weaknesses have not been systematically assessed yet. To fill this knowledge gap, we apply four state of the art methods for uncertainty quantification to four case studies of different computational complexities. This reveals the trade-offs between their applicability and their statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully.

Trends of Hepatorenal Variations in Hepatitis-C Patients Visiting THQ Level Hospital in Arifwala Punjab

Hepatitis C virus (HCV) is a source of kidney illness and liver pathogenesis such as cirrhosis and hepatocellular carcinoma (HCC). Objective: To evaluate variations in hepatic and renal profile of hepatitis-C patients. Methods: For this purpose, blood samples of 94 participants were collected. Out of which 64 were hepatitis-C patients and 30 were healthy controls. All the participants were enlisted from Tehsil Headquarter (THQ) Hospital, Arifwala. Both males and females were included in the HCV Patients group. The serum concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST) urea, creatinine, albumin, bilirubin, glucose and gamma-glutamyl transferase (GGT) were estimated by chemistry analyzer. Unpaired Student “t” test was applied to analyze the data of biochemical variations in hepatitis-C patients as compared to normal persons with significance level of p ≤ 0.05. For statistical interpretation, Graph pad prism version 6.0 software was utilized. Results: Significant elevation in the levels of glucose (p = 0.0002), ALP (p = 0.01), ALT (p = 0.0009), AST (p = 0.002) and GGT (p < 0.0001), whereas, non-significant increase in the levels of creatinine (p = 0.9) and bilirubin (p = 0.51) was evidenced in hepatitis-C patients as compared to healthy controls. While, significant decrease in the level of albumin (p = 0.0008) was observed in hepatitis-C patients as compared to healthy controls. Conclusions: The fluctuations in these parameters suggest that HCV has a significant impact on hepatic health markers.

A deep learning-based approach for statistical robustness evaluation in proton therapy treatment planning: a feasibility study

Objective. Robustness evaluation is critical in particle radiotherapy due to its susceptibility to uncertainties. However, the customary method for robustness evaluation only considers a few uncertainty scenarios, which are insufficient to provide a consistent statistical interpretation. We propose an artificial intelligence–based approach that overcomes this limitation by predicting a set of percentile dose values at every voxel and allows for the evaluation of planning objectives at specific confidence levels. Approach. We built and trained a deep learning (DL) model to predict the 5th and 95th percentile dose distributions, which corresponds to the lower and upper bounds of a two-tailed 90% confidence interval (CI), respectively. Predictions were made directly from the nominal dose distribution and planning computed tomography scan. The data used to train and test the model consisted of proton plans from 543 prostate cancer patients. The ground truth percentile values were estimated for each patient using 600 dose recalculations representing randomly sampled uncertainty scenarios. For comparison, we also tested whether a common worst-case scenario (WCS) robustness evaluation (voxel-wise minimum and maximum) corresponding to a 90% CI could reproduce the ground truth 5th and 95th percentile doses. Main results. The percentile dose distributions predicted by DL yielded excellent agreements with the ground truth dose distributions, with mean dose errors below 0.15 Gy and average gamma passing rates (GPR) at 1 mm/1% above 93.9, which were substantially better than the WCS dose distributions (mean dose error above 2.2 Gy and GPR at 1 mm1% below 54). We observed similar outcomes in a dose-volume histogram error analysis, where the DL predictions generally yielded smaller mean errors and standard deviations than the WCS evaluation doses. Significance. The proposed method produces accurate and fast predictions (∼2.5 s for one percentile dose distribution) for a given confidence level. Thus, the method has the potential to improve robustness evaluation.

Enumeration of synergistic relationship between carbon dioxide evaluation and nutrient budget during vermicomposting of cereal grain processing industry sludge

Despite the growing concerns about global warming issues, very little emphasis is being given to understand the dynamics of CO2 emission in a vermicomposting system. The present study was designed to investigate the synergistic relationship between nutrient budget and CO2 evaluation rate during the vermistabilization of cereal grain processing industry sludge employing Perionyx excavatus. The results obtained from Principal Component Analysis and correlation studies revealed a negative correlation among both variables indicating that a periodical increase in nutrient budget decreases the CO2 evaluation rate during vermicomposting. The vermicomposting treatment containing 5 % Cow dung was deemed superior in terms of physico-chemical, maturity, phytotoxicity as well as nutritional parameters (pH, Conductivity, Total organic carbon, exchangeable acidity, heavy metal content, BOD, COD, NPK). Finally, statistical interpretation coupled with apropos analysis was employed to conclude that enriching the raw materials prior to any vermicomposting practice is key to reducing the emission of CO2.

Uncovering the organization of neural circuits with Generalized Phase Locking Analysis.

Despite the considerable progress of in vivo neural recording techniques, inferring the biophysical mechanisms underlying large scale coordination of brain activity from neural data remains challenging. One obstacle is the difficulty to link high dimensional functional connectivity measures to mechanistic models of network activity. We address this issue by investigating spike-field coupling (SFC) measurements, which quantify the synchronization between, on the one hand, the action potentials produced by neurons, and on the other hand mesoscopic "field" signals, reflecting subthreshold activities at possibly multiple recording sites. As the number of recording sites gets large, the amount of pairwise SFC measurements becomes overwhelmingly challenging to interpret. We develop Generalized Phase Locking Analysis (GPLA) as an interpretable dimensionality reduction of this multivariate SFC. GPLA describes the dominant coupling between field activity and neural ensembles across space and frequencies. We show that GPLA features are biophysically interpretable when used in conjunction with appropriate network models, such that we can identify the influence of underlying circuit properties on these features. We demonstrate the statistical benefits and interpretability of this approach in various computational models and Utah array recordings. The results suggest that GPLA, used jointly with biophysical modeling, can help uncover the contribution of recurrent microcircuits to the spatio-temporal dynamics observed in multi-channel experimental recordings.

Age, comorbidities, c-reactive protein and procalcitonin as predictors of severity in confirmed COVID-19 patients in the Philippines

BackgroundThe Coronavirus Disease 2019 (COVID-19) pandemic has been affecting people globally, and the Philippines is one of the countries greatly struck by the virus. The continued rise of new positive cases has drawn attention to the urgent need for healthcare management to cope with this challenge. Severity prediction could help improve medical decision-making and optimise the patient's treatment plan with a good clinical outcome. This study aimed to identify the determinants of COVID-19 disease severity. MethodsDemographic characteristics and laboratory findings were collected from electronic medical records and paper forms of all confirmed COVID-19 cases reported by the University of Perpetual Help DALTA Medical Center between the September 1, 2020 and the October 31, 2021. We performed statistical analyses and interpretation of data to compare severe and non-severe groups. Results5,396 confirmed cases were examined. Most of the severe cases were elderly, male, had blood type A, and with comorbidities. Cycle threshold (Ct) values were lower in the severe group. Most patients had higher-than-normal levels of all blood parameters except platelet, white blood cell (WBC), neutrophil, and lymphocyte counts. Age, sex, ABO blood groups, comorbidities, open reading frame 1 ab (ORF1ab) and nucleocapsid (N) gene Ct values, ferritin, C-reactive protein (CRP), procalcitonin (PCT), D-dimer, white blood cell (WBC) count, neutrophil count, and lymphocyte count were significantly associated with disease severity. In multivariate analysis, age groups >60 and 30–59 years, presence of comorbidities, CRP level >5 ng/mL, and PCT >0.05 ng/mL were identified as disease severity predictors. ConclusionsBased on our results, age, comorbidities, CRP, and PCT level may be utilised as primary assessment factors for possible hospital admission and close monitoring upon testing. Early detection of these risk factors may provide strategic interventions that help reduce mortality, hospital admissions, and more expensive and extensive treatments.

Consumers’ Behavior in the Field of Organic Agriculture and Food Products during the COVID-19 Pandemic in the Czech Republic: Focus on a Comparison of Hyper-, Super- and Farmers’ Markets and Direct Purchases from Producers

The aim of this paper is to assess Czech food consumers’ behavior when buying organic products during the COVID-19 pandemic, with an emphasis on the place of purchase of organic agriculture and food products—especially those purchases with the shortest logistics value chain, i.e., purchase at farmers’ markets, or directly from the producer—and a comparison with the current most common places of purchase of organic products in the Czech Republic, supermarkets and hypermarkets. Categorical data analysis methods were used to create a profile of the consumer according to the most frequent purchase locations. To create mathematical–statistical models and interpretations, the methods of logistic regression, correspondence analysis and contingency table analysis were chosen. According to the results of the survey, respondents under 25 years of age are the least likely to make purchases at farmers’ markets or directly from the producer. Consumers aged 26–35 and with a university degree are the most likely to buy organic agriculture and food products at this location, followed closely by older respondents in the categories 36–45 and 46+ and with a secondary education. It is important for manufacturers to have an overview of where, in what quantities, and for what reasons consumers buy their products, especially for reasons of production optimization and planning, ecological concerns, rural development, and the impact on local areas and the value chain.

Незаконный оборот наркотиков в регионе ответственности государств – членов Организации Договора о коллективной безопасности как самостоятельный вид преступности

Introduction. The article provides criminological characteristics of drug trafficking in the region of responsibility of the member states of the Collective Security Treaty Organization (hereinafter referred to as the Organization, the CSTO). This characteristic reflects one of the main criminological problems - constantly growing drug trafficking, the source of which is the territory of Afghanistan. It is indicated that the Organization is an important actor of international law enforcement activities that counteract the illicit trafficking of narcotic drugs of Afghan origin. The article reveals the peculiarities of drug trafficking in the region of responsibility of the CSTO member states.
 Methods. The methodological basis of this study was formed by such methods as formal-logical, sociological, statistical, historical and scientific interpretation of the crime model proposed by D.A. Shestakov.
 Results. According to the results of the study, the author proposes the concept of «drug crime in the region of responsibility of the CSTO member states» and indicates the danger of drug trafficking (drug crime) as a socio and legal phenomenon in this region. Based on the multilevel model of understanding crime («Shestakov funnel»), the author’s dynamic model of the «funnel» of drug crimes in this region, which consists of five levels and three trends, is proposed. The proposed model of drug crimes has a dynamic character and may vary depending on the period under review. The results of the study can also be used by law enforcement officers and scientists dealing with this issue. The identified features contribute to understanding the directions of preventive impact on the considered type of crime.

Nonlocal advection diffusion equations and the two-slit experiment in quantum mechanics

We analyze a partial differential equation that models the two-slit experiment of quantum mechanics. The state variable of the equation is the probability density function of particle positions. The equation has a diffusion term corresponding to the random movement of particles, and a nonlocal advection term corresponding to the movement of particles in the transverse directionperpendicular to their forward movement. The model is compared to the Schrodinger equation model of the experiment. The model supports the ensemble interpretation of quantum mechanics.
 See also https://ejde.math.txstate.edu/special/02/w1/abstr.html

On the Fractal Measures and Dimensions of Image Measures on a Class of Moran Sets

In this work, we focus on the centered Hausdorff measure, the packing measure, and the Hewitt–Stromberg measure that determines the modified lower box dimension Moran fractal sets. The equivalence of these measures for a class of Moran is shown by having a strong separation condition. We give a sufficient condition for the equality of the Hewitt–Stromberg dimension, Hausdorff dimension, and packing dimensions. As an application, we obtain some relevant conclusions about the Hewitt–Stromberg measures and dimensions of the image measure of a τ-invariant ergodic Borel probability measures. Moreover, we give some statistical interpretation to dimensions and corresponding geometrical measures.

On the Transition-State theory approach to the Jones-Dole’s viscosity B-coefficient: A novel molecular-based interpretation, assessment of its implications, and experimental evidence

We report a novel statistical mechanics interpretation of the frequently invoked Feakins’ transition-state theory approach to the Jones − Dole’s Bi- coefficient, and its temperature–pressure derivatives, in the context of their suitability as structure-making/breaking markers for the behavior of solutes in dilute electrolyte solutions. We provide additional rigorous molecular-based thermodynamic evidence to highlight the non-uniqueness of the Bi- and ∂Bi/∂TP- responses to the solute–solvent intermolecular asymmetries in these systems, and to illustrate the resulting outcomes according to a fundamental structure-making/breaking function involving the available experimental data for aqueous electrolyte solutions. Finally, we discuss the implications of our findings while providing a cautionary outlook on the pervasive use of the viscosity-based structural markers.

Investigation of eco-friendly fluorescence quenching probes for assessment of acemetacin using silver nanoparticles and acriflavine reagent

The non-steroidal anti-inflammatory medication acemetacin was assessed via two straightforward green spectrofluorimetric techniques. The quenching-dependent derivatizing spectrofluorimetric reactions are the master point of this study. Acriflavine-based method (Method I) depends on forming an ion association complex between acriflavine and the drug in a ratio of 1:1, decreasing the former's fluorescence intensity. Acriflavine or Ag NP's intensity-related quenching action goes linearly with the acemetacin concentration in the 2.0–20.0 µg/mL and 1.0–16.0 µg/mL ranges, respectively. The second quenching mechanism depends on using the silver nanoparticles (Ag NP's) as a fluorescence probe (Method II); Ag NP's were prepared from reducing silver nitrate using sodium borohydride. Both methods could be applied to determine pure and pharmaceutical dosage forms of acemetacin. The methods proved valid according to the international conference on harmonization (ICH) guidelines. In addition to this, this work has been estimated under green criteria assessment tools. There is no significant difference between the proposed and the comparison methods after the statistical interpretation.

A New Expression for the Coulomb Potential Corresponding to the Product of Two Exponential Functions Based on the Properties of the Integral Representations of the Bessel Functions

The calculation of the Coulomb Potential corresponding to the product of two Exponential Type Functions, inherently has numerical challenges that must be resolved. In order to address these problems, in this paper it is presented a new partition of the Coulomb Potential. The proposed partition involves two terms. One of the terms is a one-dimensional integral, which allows geometrical and statistical interpretations. The other term is proportional to a Modified Bessel Function and it is obtained from a two-step procedure. As a first step, a Non-Rational Function is used for approximating one of the two integrals involved. Then, the remaining improper integral can be identified with an integral representation of an appropriate Modified Bessel Function. The existence of such a Non-Rational Approximant is proved and its numerical performance is shown through some examples.

A statistical field theory underlying the thermodynamics of Ricci flow and gravity

This paper proposes a statistical field theory of quantum reference frame underlying Perelman’s analogies between his formalism of the Ricci flow and the thermodynamics. The theory is based on a [Formula: see text] quantum nonlinear sigma model (NLSM), interpreted as a quantum reference frame system which a to-be-studied quantum system is relative to. The statistic physics and thermodynamics of the quantum frame fields is studied by the density matrix obtained by the Gaussian approximation quantization. The induced Ricci flow of the frame fields and the Ricci–DeTurck flow of the frame fields associated with the density matrix are deduced. In this framework, the diffeomorphism anomaly of the theory has a deep thermodynamic interpretation. The trace anomaly is related to a Shannon entropy in terms of the density matrix, which monotonically flows and achieves its maximal value at the flow limit, called the Gradient Shrinking Ricci Soliton (GSRS), corresponding to a thermal equilibrium state of spacetime. A relative Shannon entropy with respect to the maximal entropy gives a statistical interpretation to Perelman’s partition function, which is also monotonic and gives an analogous H-theorem to the statistical frame fields system. A temporal static three-space of a GSRS four-spacetime is also a GSRS in lower three-dimension, we find that it is in a thermal equilibrium state, and Perelman’s analogies between his formalism and the thermodynamics of the frame fields in equilibrium can be explicitly given in the framework. By extending the validity of the Equivalence Principle to the quantum level, the quantum reference frame fields theory at low energy gives an effective theory of gravity, a scale-dependent Einstein–Hilbert action plus a cosmological constant is recovered. As a possible underlying microscopic theory of the gravitational system, the theory is also applied to understand the thermodynamics of the Schwarzschild black hole.

A unified view of feature selection based on Hilbert-Schmidt independence criterion

Feature selection is a challenging and increasing important task in the machine learning and data mining community. According to different learning scenarios such as the availability of supervision information, feature selection can be broadly categorized as supervised, unsupervised and semi-supervised methods. Traditionally, feature selection algorithms are scenario-specific, i.e., different types of feature selection algorithms were proposed and investigated separately for specific learning scenario. In this paper, we aim to develop a unified view of supervised, unsupervised and semi-supervised feature selection methods. Based on the Hilbert-Schmidt independence criterion (HSIC), we first show that supervised, unsupervised and semi-supervised feature selection methods share the same objective. In discussing the connection between the unified framework and HSIC least absolute shrinkage and selection operator (HSIC Lasso), we find that the proposed framework not only has a clear statistical interpretation that minimum redundant features with maximum dependence on the target output variable can be found in terms of the HSIC, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Under this unified view, a new unsupervised feature selection algorithm is also proposed and demonstrated with several benchmark examples.

Generating the Right Evidence at the Right Time: Principles of a New Class of Flexible Augmented Clinical Trial Designs.

To support informed decision making, clear descriptions of the beneficial and harmful effects of a treatment are needed by various stakeholders. The current paradigm is to generate evidence sequentially through different experiments. However, data generated later, perhaps through observational studies, can be difficult to compare with earlier randomized trial data, resulting in confusion in understanding and interpretation of treatment effects. Moreover, the scientific questions these later experiments can serve to answer often remain vague. We propose Flexible Augmented Clinical Trial for Improved eVidence gEneration (FACTIVE), a new class of study designs enabling flexible augmentation of confirmatory randomized controlled trials with concurrent and close-to-real-world elements. Our starting point is to use clearly defined objectives for evidence generation, which are formulated through early discussion with health technology assessment (HTA) bodies and are additional to regulatory requirements for authorization of a new treatment. These enabling designs facilitate estimation of certain well-defined treatment effects in the confirmatory part and other complementary treatment effects in a concurrent real-world part. Each stakeholder should use the evidence that is relevant within their own decision-making framework. High quality data are generated under one single protocol and the use of randomization ensures rigorous statistical inference and interpretation within and between the different parts of the experiment. Evidence for the decision making of HTA bodies could be available earlier than is currently the case.

On the analytical continuation of lattice Liouville theory

The path integral of Liouville theory is well understood only when the central charge c ∈ [25, ∞). Here, we study the analytical continuation the lattice Liouville path integral to generic values of c, with a particular focus on the vicinity of c ∈ (−∞, 1]. We show that the c ∈ [25, ∞) lattice path integral can be continued to one over a new integration cycle of complex field configurations. We give an explicit formula for the new integration cycle in terms of a discrete sum over elementary cycles, which are a direct generalization of the inverse Gamma function contour. Possible statistical interpretations are discussed. We also compare our approach to the one focused on Lefschetz thimbles, by solving a two-site toy model in detail. As the parameter equivalent to c varies from [25, ∞) to (−∞, 1], we find an infinite number of Stokes walls (where the thimbles undergo topological rearrangements), accumulating at the destination point c ∈ (−∞, 1], where the thimbles become equivalent to the elementary cycles.

Empirical evaluation of the transfer of information resources in active learning

Active learning is an alternative to traditional learning, using the capabilities of the global network to access remote information resources. The purpose of the article is to analyse the sources and factors of delay in the implementation of network services and the possibilities of access to distributed information objects depending on their size and remoteness. The research was conducted by collecting empirical data (monitoring) on the transfer of resources between client and server. A research organization model was developed and an implementation application was created. Experiments were carried out, and the obtained results were summarized and presented through statistical evaluations and graphical interpretation.

Innovative features in ”Sonata for violin and piano” by Boris Dubosarschi

The article examines the Sonata for violin and piano by B. Dubosarsky in terms of content, form and interpretative treatment. The author aims to reveal the innovative features of this work, which are manifested primarily in the interpretation of the form, combining the features of a three-part cycle and a large one-part composition. The intonation-thematic structure of the sonata, the ratio of the violin and piano parts are analyzed in detail, performing recommendations are offered for developing a creative version of the interpretation. As a result, it is concluded that the Sonata for Violin and Piano by B. Dubosarsky is an original example of an individual solution of a genre form, the innovation of which lies in the synthesis of a deep philosophical content, an original composition and a convincing ensemble interpretation of the violin and piano.