Independent Sources Research Articles

Using natural language processing to generate a large-scale database of aortic stenosis with long-term follow-up: the CASPER (cogstack aortic stenosis patient electronic registry) database

Abstract Background Aortic stenosis (AS) is one of the most common heart valve diseases in the Western World, with a prevalence of approximately 4% in patients over 70 years old. High quality observational data can provide insight into characteristics that define patient trajectories and inform the design of appropriately powered randomised trials. Objective The aim of this study is to develop a data pipeline to generate a large database containing information of an AS cohort in a hospital by analysing both structured and unstructured (free text) data in routinely collected electronic heath records (EHR). The unstructured data are processed using natural language processing (NLP, an artificial intelligence technique that allows machines to recognize clinical concepts in EHR with contextualisation and linked to SNOMED ontology). Methods The open-source CogStack retrieval system is used to collect clinical notes of AS patients. The natural language processing toolkit, MedCAT, is used to identify clinical concepts in the form of SNOMED terms in clinical notes. In addition to patient demographic data (including ethnicity and markers of social deprivation), a range of clinical characteristics, co-morbidities, medications and procedural interventions are extracted. Validation of the dataset was performed using 3 existing, independent data sources (structured echocardiogram reports, surgical aortic valve replacement (AVR) operation notes, transcatheter intervention (TAVI) reports). Results We identified 7,451 patients with AS from our EHR, of whom 5,754 had an echocardiogram at our centre. The mean duration of follow up was 5 years. Of those with an echocardiogram at our centre, we were able to extract data on AS severity in 91% and left ventricular function in 89%. Manual validation of procedural data suggests a sensitivity of 98% to detect TAVI and 97% to detect AVR procedures. Kaplan-Meier curves for automated detection of patient mortality are consistent with the known survival data for patients with AS. Conclusion We developed and validated an automated NLP-enabled data pipeline to identify and characterise a single retrospective centre cohort of AS patients from routinely collected EHR across the spectrum of severity, with long term mortality data. This resource has the potential to provide insights toward several outstanding questions relating to progression of AS, and the influence of ethnicity and social deprivation without the need for conventional dedicated databases. The EHR-based database also has the potential to guide the design of future randomised control trials.Data Pipeline

Noise-modulated nonlocal coupling: Chimera suppression and beyond

The spatio-temporal dynamics of networks of chaotic discrete-time systems is studied numerically in the presence of colored Gaussian noise-modulated nonlocal coupling between elements. As individual nodes of the networks, a modified Ricker map and a cubic map are used. Without noise, the networks demonstrate various types of chimera states, such as phase or double-well chimeras. The nonlocal coupling is modulated by either a common noise source or independent noise sources. The impact of noise-modulated coupling (coupling noise) on the dynamics of the networks is analyzed for a number of different random initial conditions and noise realizations. It is shown that the network dynamics and settled spatio-temporal structures can be controlled by varying the intensity and spectral characteristics of the coupling noise.

Dex-Benchmark: datasets and code to evaluate algorithms for transcriptomics data analysis.

Many tools and algorithms are available for analyzing transcriptomics data. These include algorithms for performing sequence alignment, data normalization and imputation, clustering, identifying differentially expressed genes, and performing gene set enrichment analysis. To make the best choice about which tools to use, objective benchmarks can be developed to compare the quality of different algorithms to extract biological knowledge maximally and accurately from these data. The Dexamethasone Benchmark (Dex-Benchmark) resource aims to fill this need by providing the community with datasets and code templates for benchmarking different gene expression analysis tools and algorithms. The resource provides access to a collection of curated RNA-seq, L1000, and ChIP-seq data from dexamethasone treatment as well as genetic perturbations of its known targets. In addition, the website provides Jupyter Notebooks that use these pre-processed curated datasets to demonstrate how to benchmark the different steps in gene expression analysis. By comparing two independent data sources and data types with some expected concordance, we can assess which tools and algorithms best recover such associations. To demonstrate the usefulness of the resource for discovering novel drug targets, we applied it to optimize data processing strategies for the chemical perturbations and CRISPR single gene knockouts from the L1000 transcriptomics data from the Library of Integrated Network Cellular Signatures (LINCS) program, with a focus on understudied proteins from the Illuminating the Druggable Genome (IDG) program. Overall, the Dex-Benchmark resource can be utilized to assess the quality of transcriptomics and other related bioinformatics data analysis workflows. The resource is available from: https://maayanlab.github.io/dex-benchmark.

Analysis and Assessment of the Soil Environment Around a Plateau Municipal Solid Waste Incineration Plant

ABSTRACT To understand the source, distribution and degree of pollution of heavy metals in the soil around a plateau municipal solid waste incineration plant, a municipal solid waste incineration plant in Lhasa was taken as the research object in this study. The soil surrounding the facility was sampled, and the contents of heavy metals, including Cu, Pb, Zn, Cr, Ni, Cd, As, and Hg, in the soil were analyzed. Then, heavy metal pollution and risk assessments were conducted using the geoaccumulation index method, Nemerow index method, and potential ecological risk index method. Additionally, correlation and principal component analysis were used to determine the correlation between heavy metals in the soil and their sources of pollution. The results showed that based on the Nemerow comprehensive pollution index, the soil surrounding the incineration plant generally had a low level of pollution. Through the analysis of individual potential ecological risk indices, it was found that only the heavy metal Cd presented moderate potential risks, while the other seven heavy metals had mild potential risks. The average value of the total potential risk coefficient was 107.31, which was less than 150, indicating that there was a slight potential risk associated with the soil around the incineration plant. There was significant correlation between six heavy metals, namely, Cu, Pb, Zn, Cr, Ni, and Cd, in the soil around the incineration plant. Their pollution sources were similar, and their levels tended to be affected by the incineration plant. However, there was no significant correlation between the heavy metals As and Hg and the other six heavy metals. These two heavy metals had independent sources of pollution but were also influenced by human activities.

Multi-faceted analysis and prediction for the outbreak of pediatric respiratory syncytial virus.

Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons. This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features. We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05. Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.

Experimental Data-Analysis Methods Analyze Multiphase-Flow Data Sets

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32170, “Performance Mapping of Multiphase-Flow Model—Connecting the Dots From Experimental Data,” by Auzan A. Soedarmo, SPE, Eduardo Pereyra, SPE, and Cem Sarica, SPE, The University of Tulsa. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ In the complete paper, the authors present exploratory data analyses to evaluate comprehensively the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments. This effort provides wide-ranging insight that can reflect the current state of the art of multiphase-flow modeling and pinpoint areas where improvements are needed. Much of the complete paper is devoted to the literature relating to the authors’ work; these specific citations are not included in this synopsis. Introduction The emergence of “big data” has encouraged the use of data from various sources to enhance the decision-making process. The authors write that, unfortunately, multiphase-flow studies often are performed in theoretical silos, within which specific experiments were performed and upon which certain model improvements were proposed. Therefore, it is easy to lose sight of the larger context in terms of current understanding and modeling capability. This disconnected approach also has produced an ever-growing, potentially unmanageable list of closure relationships, which can be counterproductive for model development. Experimental Data-Analysis Methodology For their study, the authors collected near-horizontal (–5° to +5°) gas/liquid data from literature with a gas density of 5 kg/m3 or greater. Atmospheric data were excluded because gas and air at such conditions are typically very compressible, meaning that superficial gas velocity can vary considerably along the pipe. One also can argue that high-gas-density cases are more relevant to typical high-pressure field applications. The authors collected more than 2,000 data points for pressure drop, holdup, and flow patterns. This accumulated data set by no means can be categorized as “big data” in a traditional sense; however, the authors write that it is sufficient for demonstrating how exploratory data analysis may extract actionable insights from data sets from various independent sources. This data set is henceforth called the “HP data set.” No high-viscosity liquid (10 cP or higher) is included in the HP data set. When comparing experimental data collected under different conditions, it is helpful to reduce the problem’s dimensionality (down from 10 dimensions in two-phase gas/liquid flow, excluding the gravitational acceleration constant). A traditional approach would be to use dimensionless numbers such as Reynolds and Froude numbers. A single dimensionless number, however, can only account for limited parameters. For example, the Reynolds number only considers the inertia and viscous effects, while the Froude number only considers the inertia and gravity effects. Therefore, a mechanistic multiphase-flow model is used in this study that accounts for all dimensions of the multiphase-flow problems and reduces them into one dimension, namely the discrepancy between the predicted value and experimental data.

The effect of self-education on teachers’ competitiveness

Lifelong learning skills and building competencies necessary for teachers’ competitiveness are urgent issues. This work aimed to study the relationship between teachers’ self-education and competitiveness. The research method used a questionnaire survey of teachers and principals of general secondary education institutions, as well as the method of comparison and mathematical methods. The results showed that there was a relationship between teachers’ motivation factors for independent lifelong learning, directions and sources with the achieved results of self-education was established. According to the framework of competitive competencies, the teacher’s qualities’ dependence on motivation, directions, sources and results of self-education was also revealed. The results of this study can be helpful as they arrange knowledge and research methods of the self-education process, taking into account motivation, directions, sources, and outcomes. Therefore, teachers should engage in self-education throughout their professional life to improve their competitiveness. They should also constantly update their knowledge of the fields related to teaching. They must develop synchronously with the evolution of science and technology and have a creative approach to learning. Teachers should supplement their knowledge of subject teaching methods, psychology, and health care. Further research should focus on other factors influencing improving teachers’ competitiveness.

Post-quantum nonlocality in the minimal triangle scenario

We investigate network nonlocality in the triangle scenario when all three parties have no input and binary outputs. Through an explicit example, we prove that this minimal scenario supports nonlocal correlations compatible with no-signaling and independence of the three sources, but not with realisations based on independent quantum or classical sources. This nonlocality is robust to noise. Moreover, we identify the equivalent to a Popescu-Rohrlich box in the minimal triangle scenario.

AN ANALYSIS OF INDIVIDUAL INVESTORS’ BEHAVIOR: A CASE OF THE DHAKA STOCK EXCHANGE

The objective of this research is to ascertain the factors associated with financial indicators, independent sources of information, financial necessities, advocate recommendations, and personal reputation/firm reputation compliance that can influence the behavior of the Dhaka Stock Exchange (DSE) individual investors. The research was carried out by questionnaire method. The survey was conducted with 300 investors trading on DSE. The survey includes only gender as a demographic factor of the participants, 5 main variables, and 27 sub-factors related to them. The obtained data were analyzed with SPSS 26 program. In the research, descriptive analysis was used to achieve the research objective. The findings of the research present that financial indicators, independent sources of information, and personal reputation/firm reputation compliance have a significant impact while financial necessities have a moderate impact, and advocate recommendations have very little impact on DSE individual investors’ behavior. The research provides guidance to individual investors, firms, policymakers, and investment advisors.

Social Infrastructure of Soviet Enterprises in the 1950s-1980s: Sources and Mechanisms of Financing (Example of Chelyabinsk Tractor Plant)

The Sources and Mechanisms of Financing the Social Infrastructure of the Chelyabinsk Tractor Plant in the 1950s-1980s. This study examines the sources and mechanisms of financing the social infrastructure of the Chelyabinsk Tractor Plant, a large Soviet industrial enterprise, during the period of 1950s-1980s. The research is based on archival documents from the plant and its trade union organization, which are stored in the United State Archive of the Chelyabinsk Region. It was found that the plant had a significant amount of assets, including housing and a network of social, cultural, and sports facilities. Financing the social infra-structure of Soviet enterprises was a complex system that involved multiple independent sources. The plant made direct payments from its economic stimulation funds, covered the maintenance costs of various facilities transferred to the trade union organization, and covered losses from the housing and communal services system. In addition, funding for these facilities also came from the state budget, social insurance budget, trade union organization budget, targeted subsidies from the All-Russian Central Council of Trade Unions, revenues from self-supporting cultural and sports institutions, and others. The Chelyabinsk Tractor Plant was required to allocate up to 10 % of its profits towards maintaining its social infra-structure, while other sources of funding con-tributed comparable amounts.

Peran Program Karawang Berseri dalam Upaya Pemberdayaan Masyarakat, Pencegahan dan Penanganan Kekerasan Terhadap Perempuan dan Anak

The increased recognition of the significance of sustainable development has led business communities in Indonesia to fulfill Corporate Social Responsibility (CSR). One example is the implementation of a corporate social responsibility program by PT Pertamina EP Zone 7 Tambun Field which focuses on efforts to eradicate violence against women and children in Karawang, known as "Karawang Berseri" or "Karawang Free from Violence, Growing and Independent". This research will analyze the implementation of the Karawang Berseri program from the perspective of community empowerment, starting from planning, implementation, and ultimately generating impacts and benefits for the community, especially in Karawang Kulon Sub-District. This research uses a qualitative approach to literature sources related to the program. The Karawang Berseri Program has had a positive impact not only in dealing with the problem of violence against women and children, but also in seeking program sustainability by creating an independent source of financing from hydroponic cultivation activities and joint economic initiatives. The effectiveness of the program is also supported by innovations through the program such as redesigning the case-handling system, standardization of networking service procedure, creation of a Desa Berseri Module, and the Gender School Discussion Series. Apart from that, participation from the local community is the main key in the sustainable success of the Karawang Berseri program because they are involved as beneficiaries and implementers of activities, so they are independently motivated. The achievements of the Karawang Berseri program have proven a successful and sustainable model of community empowerment in overcoming the problem of violence against women and children.

Atmospheric CO2 Isotopic Variations, with Estimation of Ocean and Plant Source Contributions

This analysis uses both atmospheric CO2 concentrations and the accompanying δ13C isotopic measurements of CO2 over 40 years from 1978 to 2015 observed at ten different latitudes from 90° S to 82 °N. Atmospheric CO2 is separated into two components of CO2 attributable to deep ocean and to plant (including fossil fuel) sources. The isotopic values assigned to the two components are δ13C = 0‰ and −26‰, respectively. The latitude variations in residual source component CO2 show the ocean source component peaking at the equator. This contrasts with the residual plant source component that peaks in the Arctic Circle region. Seasonal comparisons show no change in the ocean component peaking at the equator and no significant changes in its variation with latitude, while the plant component shows seasonal changes of the order of 15 ppm at high latitudes. The ocean component shows clear anomalous behavior in the three years following the 1989 Pacific Ocean regime shift (a shift independently identified from the changed biological time series). By contrast, the residual plant component shows a correlation in the timing of maxima in its annual variations with the timing of El Nino events over the time span of 1985–2015. It also shows a discontinuity in annual variation coinciding with the 1995 AMO phase change. We conclude that the ocean and plant components of atmospheric CO2 relate to independent sources of atmospheric CO2 and have approximately equal magnitudes. The observations are consistent with a hypothesis that variations in the ocean components have an origin from upwelling water from deep ocean currents, and variations in plant components are dominated by a combination of fossil fuel CO2, phytoplankton productivity, and forest and peat fires, which primarily occur in the northern hemisphere.

Will different land uses affect heavy metal pollution in soils of roadside trees? An empirical study from Shanghai.

Heavy metal pollution in roadside soil may harm humans, animals, plants, and local ecosystems. This study aimed to explore the sources and potential ecological risks of heavy metals in soils of roadside trees under different land uses, using soil samples collected from 136 roads across 16 administrative districts in Shanghai. The contents, pollution characteristics, potential ecological risks, and sources of seven heavy metals were analyzed, including Cr, Ni, Cd, Pb, As, Cu, and Zn. Results showed that (1) land use patterns affected the heavy metal contents, with industrial and construction areas showing higher contents while agricultural and forestry areas lower; (2) the ranking of heavy metal pollution levels was Cd > As > Pb > Cu > Ni > Cr > Zn. Cd exhibited the highest potential ecological risk, falling within the moderate to considerable potential ecological risk interval; (3) the sources of Cu, Zn, Cr, Ni, Cd, and Pb were associated with traffic emissions, whereas As had independent other sources and Pb in industrial and construction areas was also influenced by industrial emissions. These results provide valuable references on the control of heavy metal pollutants and the management of land uses in megacities.

The asset pricing implications of global oil price uncertainty: Evidence from the cross-section of Chinese stock returns

This study examines the role of global oil price uncertainty in the cross-sectional pricing of individual stocks in the Chinese stock market, motivated by the intertemporal capital asset pricing model. Using both Fama-Macbeth regressions and multifactor time-series regressions, we find that stocks with higher oil price uncertainty betas have significantly lower expected returns. This finding is consistent with the asset pricing implications of the demand of investors for stocks with high potential to hedge against oil price uncertainty. We further conduct an industry analysis and find that the negative association between the oil price uncertainty beta and expected returns is pronounced, primarily in the Manufacturing and Transportation & Communication industries. Moreover, we demonstrate that the return predictability of the oil price uncertainty beta cannot be subsumed by those of other oil-related betas and well-known macro-uncertainty betas, thus supporting that global oil price uncertainty is an independent source of systematic risk in the Chinese stock market.

Solar Powered IoT Sensors to Increase the Network Longevity

A wide variety of industries, such as the manufacturing business, the transportation industry, the environmental industry, and even the medical industry, are beginning to comprehend the significance of wireless sensor networks. This problem is being further amplified by the rapidly increasing use of devices connected to the Internet of Things across a wide variety of enterprises. Because of this, having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance, both of which are essential to the widespread adoption of the technology, the successful application of the technology, and the long-term viability of the technology. In this paper we enable a solar powered IoT node communication using energy management system. Having an independent source of energy is essential because it determines the adaptability of placement and the simplicity of maintenance. The creation of energy-aware wireless sensor nodes has become increasingly feasible as a result of recent advancements in fields such as the manufacturing of a large number of highly efficient energy converters and the reduction of energy consumption in hardware, software, and communication protocols.

EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM

Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements.

Material Deposits at Sacred Sites—the Many Faces of Modern Paganism in Estonia

The popularity of natural sacred places has increased significantly across Europe during the last decades. Hills, stones, springs, and groves—places named sacred in oral tradition—have become focal points for practices of contemporary paganism and spiritualism. In Estonia, this movement’s rise in popularity has been marked by an increase in the number of and type of deposits left at sacred sites. This article examines the objects left at sacred sites in order to analyze the vernacular religious practices and convictions of the people who use these places. Based on case studies at five sacred sites across Estonia, it is argued that these material objects must be regarded as an independent source of information about contemporary paganism and its interpretations by people who approve pagan worldview. Taken as such, these material objects complicate existing understandings of pagan religious communities in Estonia. While traditional accounts describe paganism as a cohesive body of devotees united by their participation in ancient traditions derived from folklore, the material culture of sacred sites in Estonia reveals the users of sacred sites to be a loosely structured community who engage in shifting vernacular practices related to history, nature, ethnicity, and paganism.

HUBUNGAN SUMBER INFORMASI, PENGETAHUAN DAN PERAN TENAGA KESEHATAN DENGAN PEMERIKSAAN TRIPLE ELIMINASI

The Triple Elimination Examination (hepatitis, HIV, syphilis) is an effort to eliminate infection with three infectious diseases directly from mother to child, namely HIV/AIDS, Syphilis and Hepatitis B infection which is directly integrated into the maternal and child health program. This type of research was an analytic observational study with a cross sectional design or approach . The population in this study were all pregnant women who came to visit the health center. The sample in this study amounted to 75 respondents. The type of data uses primary data, the dependent variable is the implementation of the Triple Elimination Examination (hepatitis, HIV, syphilis), the independent variable Information Sources, Knowledge. The results of bivariate research on the relationship between sources of information, knowledge and the role of health workers with triple elimination examinations (hepatitis, HIV, syphilis) based on the chi square statistical test, sources of information, p-value 0.022 &lt;0.05, knowledge p-value 0.00 &lt;0.05 and support from health workers p-value 0.000 &lt;0.05, it can be concluded that there is a relationship between sources of information, knowledge and support from health workers with the Implementation of Triple Elimination Examination (hepatitis, HIV, syphilis). As for suggestions to the Koto Baru Health Center, it is hoped that health workers will be more active and prioritize Educational Information Communication (IEC) in providing triple elimination information to pregnant women

DcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping.

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

Information-Theoretic Models for Physical Observables.

This work addresses J.A. Wheeler's critical idea that all things physical are information-theoretic in origin. In this paper, we introduce a novel mathematical framework based on information geometry, using the Fisher information metric as a particular Riemannian metric, defined in the parameter space of a smooth statistical manifold of normal probability distributions. Following this approach, we study the stationary states with the time-independent Schrödinger's equation to discover that the information could be represented and distributed over a set of quantum harmonic oscillators, one for each independent source of data, whose coordinate for each oscillator is a parameter of the smooth statistical manifold to estimate. We observe that the estimator's variance equals the energy levels of the quantum harmonic oscillator, proving that the estimator's variance is definitively quantized, being the minimum variance at the minimum energy level of the oscillator. Interestingly, we demonstrate that quantum harmonic oscillators reach the Cramér-Rao lower bound on the estimator's variance at the lowest energy level. In parallel, we find that the global probability density function of the collective mode of a set of quantum harmonic oscillators at the lowest energy level equals the posterior probability distribution calculated using Bayes' theorem from the sources of information for all data values, taking as a prior the Riemannian volume of the informative metric. Interestingly, the opposite is also true, as the prior is constant. Altogether, these results suggest that we can break the sources of information into little elements: quantum harmonic oscillators, with the square modulus of the collective mode at the lowest energy representing the most likely reality, supporting A. Zeilinger's recent statement that the world is not broken into physical but informational parts.