Deployment of entropy information theory in the Indian Sundarban region using hydrogeochemical parameters and GIS for assessment of irrigation suitability.

The evaluation of irrigation suitability plays a crucial role for the socio-economic development of the society, especially in the region of Sundarban. For sustainable agricultural practices, groundwater quality must be suitable for irrigation; otherwise, it can degrade soil and diminish crop yield. The entropy information theory, several irrigational indices, multivariate statistics, GIS, and geostatistics are used in this work to evaluate the geographical distribution and quality of groundwater in the Indian Sundarban region. In total, 33 groundwater samples were collected in 2018 (April and May), and they were evaluated for major cations, anions, as well as other parameters like electrical conductivity (EC), soluble sodium percentage (SSP), potential salinity (PS), total dissolved solids (TDS), Kelly ratio (KR), sodium absorption ratio (SAR), permeability index (PI), residual sodium carbonate (RSC), magnesium hazard (MH), and residual sodium bicarbonate (RSBC). The overall trend of the principal cations and anions is in the sequence of Na+ ≥ Mg2+ ≥ Ca2+ ≥ K2+ and HCO3- ≥ Cl- ≥ NO3- ≥ SO42- ≥ F-, respectively, whereas the spatial variation of %Na, SAR, RSBC, and MH demonstrate very poor irrigation water quality, and spatial variation of KR, RSC, SSP, PI, and PS signifies that the irrigation water quality is excellent to good. In order to identify the specific association and potential source of the dissolved chemical in the groundwater, statistical techniques like correlation and principal component analysis were also employed. The hydrochemical facies indicates that mixed type makes up the bulk (51.51%) of the water samples. Following the Wilcox plot, more than 75% of the water samples are good to doubtful; however, by the US salinity hazard map, roughly 60.60% of the samples had high salinity (C3-S1 zone). The EWQII reports that no samples fall into the very good (no restriction) category, whereas 30.30%, 30.30%, and 39.40% of the sample wells record good (low restriction), average (moderate restriction), and poor (severe restriction) irrigation water quality, respectively. Based on this study, the bulk of the groundwater samples taken from the study area are unsuitable for cultivation. The findings of this study will also help decision-makers develop adequate future plans for irrigation and groundwater resource management.

Digital finance and performance in innovation: Evidence from universities.

This study evaluated 72 universities' performance innovation during 2011 to 2019 of panel data, using the data envelopment analysis-Malmquist method. The study used benchmark regression to analyse the relationship between digital finance and the universities' innovation performance. The aim was to improve innovation performance and promote national innovation across countries. According to the results of the empirical analysis, digital finance positively affects innovation performance. That finding was confirmed through advanced robustness test evaluation, such as limited information maximum likelihood, two-stage least squares, and interactive fixed effects. Moreover, based on information theory, the digital finance influence mechanism improves credit demand and financial efficiency. Additionally, innovation performance survived spatial overflow effects. Lastly, the paper concludes with some implications for improving digital financial coverage and constructing innovation networks among universities.

How innovation funding leads enterprises to engage in research and development: Small and medium enterprises' perspective.

Technology-based small and medium enterprises (SMEs) are the driving force behind China's economic and technological development. However, these enterprises often face challenges in financing their research and development (R&D) activities due to limited financing opportunities. Previous research has primarily focused on the resource attributes of government innovation subsidies, which serve as a crucial funding source for these SMEs. This paper aims to explore the impact of government innovation subsidies on firms from a novel perspective, considering the signaling characteristics of these subsidies. The theoretical foundation of this study lies in the asymmetric information theory and the signaling mechanism through which government subsidies send signals about enterprises. The study uses enterprise data from 2012 to 2019 to investigate the effect of government subsidies on the R&D investment of enterprises listed on the SMEs Board in Chinese stock market. The results reveal a significantly positive effect of government subsidies on the R&D investment of SME Board-listed enterprises and verify the mediating role of financing constraints in this effect. The extent to which government subsidies influence the R&D investment of SME Board-listed enterprises is associated with the enterprises' ownership characteristics, debt ratios, and times interest earned ratios. This study contributes to the literature on the SMEs Board market and may provide the Chinese government insights into developing industry policies that maximize the effectiveness of government subsidies.

An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China.

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Risk spillover networks in financial system based on information theory.

Since the financial system has illustrated an increasingly prominent characteristic of inextricable connections, information theory is gradually utilized to study the financial system. By collecting the daily data of industry index (2005-2020) and region index (2012-2020) listed in China as samples, this paper applies an innovative measure named partial mutual information on mixed embedding to generate directed networks. Based on the analysis of nonlinear relationships among sectors, this paper realizes the accurate construction of "time-varying" financial network from the perspective of risk spillover. The results are presented as follow: (1) interactions can be better understood through the nonlinear networks among distinct sectors, and sectors in the networks could be classified into different types according to their topological properties connected to risk spillover; (2) in the rising stage, information is transmitted rapidly in the network, so the risk is fast diffused and absorbed; (3) in the declining stage, the network topology is more complex and panic sentiments have long term impact leading to more connections; (4) The US market, Japan market and Hongkong market have significant affect on China's market. The results suggest that this nonlinear measure is an effective approach to develop financial networks and explore the mechanism of risk spillover.

Assessment of the information theory approach to evaluating time-to-event surrogate and true endpoints in a meta-analytic setting.

In many disease areas, commonly used long-term clinical endpoints are becoming increasingly difficult to implement due to long follow-up times and/or increased costs. Shorter-term surrogate endpoints are urgently needed to expedite drug development, the evaluation of which requires robust and reliable statistical methodology to drive meaningful clinical conclusions about the strength of relationship with the true long-term endpoint. This paper uses a simulation study to explore one such previously proposed method, based on information theory, for evaluation of time to event surrogate and long-term endpoints, including the first examination within a meta-analytic setting of multiple clinical trials with such endpoints. The performance of the information theory method is examined for various scenarios including different dependence structures, surrogate endpoints, censoring mechanisms, treatment effects, trial and sample sizes, and for surrogate and true endpoints with a natural time-ordering. Results allow us to conclude that, contrary to some findings in the literature, the approach provides estimates of surrogacy that may be substantially lower than the true relationship between surrogate and true endpoints, and rarely reach a level that would enable confidence in the strength of a given surrogate endpoint. As a result, care is needed in the assessment of time to event surrogate and true endpoints based only on this methodology.

Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.

BACKGROUND: Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual's risk of developing metastasis. Therefore, identifying critical risk factors for MBC continues to be a major research imperative, and one which can lead to advances in breast cancer clinical care. The objective of this research is to leverage Bayesian Networks (BN) and information theory to identify key risk factors for breast cancer metastasis from data. METHODS: We develop the Markov Blanket and Interactive risk factor Learner (MBIL) algorithm, which learns single and interactive risk factors having a direct influence on a patient's outcome. We evaluate the effectiveness of MBIL using simulated datasets, and compare MBIL with the BN learning algorithms Fast Greedy Search (FGS), PC algorithm (PC), and CPC algorithm (CPC). We apply MBIL to learn risk factors for 5 year breast cancer metastasis using a clinical dataset we curated. We evaluate the learned risk factors by consulting with breast cancer experts and literature. We further evaluate the effectiveness of MBIL at learning risk factors for breast cancer metastasis by comparing it to the BN learning algorithms Necessary Path Condition (NPC) and Greedy Equivalent Search (GES). RESULTS: The averages of the Jaccard index for the simulated datasets containing 2000 records were 0.705, 0.272, 0.228, and 0.147 for MBIL, FGS, PC, and CPC respectively. MBIL, NPC, and GES all learned that grade and lymph_nodes_positive are direct risk factors for 5 year metastasis. Only MBIL and NPC found that surgical_margins is a direct risk factor. Only NPC found that invasive is a direct risk factor. MBIL learned that HER2 and ER interact to directly affect 5 year metastasis. Neither GES nor NPC learned that HER2 and ER are direct risk factors. DISCUSSION: The results involving simulated datasets indicated that MBIL can learn direct risk factors substantially better than standard Bayesian network learning algorithms. An application of MBIL to a real breast cancer dataset identified both single and interactive risk factors that directly influence breast cancer metastasis, which can be investigated further.

High-Order Entropy-Based Population Diversity Measures in the Traveling Salesman Problem.

To maintain the population diversity of genetic algorithms (GAs), we are required to employ an appropriate population diversity measure. However, commonly used population diversity measures designed for permutation problems do not consider the dependencies between the variables of the individuals in the population. We propose three types of population diversity measures that address high-order dependencies between the variables to investigate the effectiveness of considering high-order dependencies. The first is formulated as the entropy of the probability distribution of individuals estimated from the population based on an m-th--order Markov model. The second is an extension of the first. The third is similar to the first, but it is based on a variable order Markov model. The proposed population diversity measures are incorporated into the evaluation function of a GA for the traveling salesman problem to maintain population diversity. Experimental results demonstrate the effectiveness of the three types of high-order entropy-based population diversity measures against the commonly used population diversity measures.

Transfer entropy as a variable selection methodology of cryptocurrencies in the framework of a high dimensional predictive model.

We determine the number of statistically significant factors in a high dimensional predictive model of cryptocurrencies using a random matrix test. The applied predictive model is of the reduced rank regression (RRR) type; in particular, we choose a flavor that can be regarded as canonical correlation analysis (CCA). A variable selection of hourly cryptocurrencies is performed using the Symbolic estimation of Transfer Entropy (STE) measure from information theory. In simulated studies, STE shows better performance compared to the Granger causality approach when considering a nonlinear system and a linear system with many drivers. In the application to cryptocurrencies, the directed graph associated to the variable selection shows a robust pattern of predictor and response clusters, where the community detection was contrasted with the modularity approach. Also, the centralities of the network discriminate between the two main types of cryptocurrencies, i.e., coins and tokens. On the factor determination of the predictive model, the result supports retaining more factors contrary to the usual visual inspection, with the additional advantage that the subjective element is avoided. In particular, it is observed that the dynamic behavior of the number of factors is moderately anticorrelated with the dynamics of the constructed composite index of predictor and response cryptocurrencies. This finding opens up new insights for anticipating possible declines in cryptocurrency prices on exchanges. Furthermore, our study suggests the existence of specific-predictor and specific-response factors, where only a small number of currencies are predominant.

Optimal foraging and the information theory of gambling.

At a macroscopic level, part of the ant colony life cycle is simple: a colony collects resources; these resources are converted into more ants, and these ants in turn collect more resources. Because more ants collect more resources, this is a multiplicative process, and the expected logarithm of the amount of resources determines how successful the colony will be in the long run. Over 60 years ago, Kelly showed, using information theoretic techniques, that the rate of growth of resources for such a situation is optimized by a strategy of betting in proportion to the probability of pay-off. Thus, in the case of ants, the fraction of the colony foraging at a given location should be proportional to the probability that resources will be found there, a result widely applied in the mathematics of gambling. This theoretical optimum leads to predictions as to which collective ant movement strategies might have evolved. Here, we show how colony-level optimal foraging behaviour can be achieved by mapping movement to Markov chain Monte Carlo (MCMC) methods, specifically Hamiltonian Monte Carlo (HMC). This can be done by the ants following a (noisy) local measurement of the (logarithm of) resource probability gradient (possibly supplemented with momentum, i.e. a propensity to move in the same direction). This maps the problem of foraging (via the information theory of gambling, stochastic dynamics and techniques employed within Bayesian statistics to efficiently sample from probability distributions) to simple models of ant foraging behaviour. This identification has broad applicability, facilitates the application of information theory approaches to understand movement ecology and unifies insights from existing biomechanical, cognitive, random and optimality movement paradigms. At the cost of requiring ants to obtain (noisy) resource gradient information, we show that this model is both efficient and matches a number of characteristics of real ant exploration.

How Far can Neural Correlations Reduce Uncertainty? Comparison of Information Transmission Rates for Markov and Bernoulli Processes.

The nature of neural codes is central to neuroscience. Do neurons encode information through relatively slow changes in the firing rates of individual spikes (rate code) or by the precise timing of every spike (temporal code)? Here we compare the loss of information due to correlations for these two possible neural codes. The essence of Shannon's definition of information is to combine information with uncertainty: the higher the uncertainty of a given event, the more information is conveyed by that event. Correlations can reduce uncertainty or the amount of information, but by how much? In this paper we address this question by a direct comparison of the information per symbol conveyed by the words coming from a binary Markov source (temporal code) with the information per symbol coming from the corresponding Bernoulli source (uncorrelated, rate code). In a previous paper we found that a crucial role in the relation between information transmission rates (ITRs) and firing rates is played by a parameter s, which is the sum of transition probabilities from the no-spike state to the spike state and vice versa. We found that in this case too a crucial role is played by the same parameter s. We calculated the maximal and minimal bounds of the quotient of ITRs for these sources. Next, making use of the entropy grouping axiom, we determined the loss of information in a Markov source compared with the information in the corresponding Bernoulli source for a given word length. Our results show that in the case of correlated signals the loss of information is relatively small, and thus temporal codes, which are more energetically efficient, can replace rate codes effectively. These results were confirmed by experiments.

Digitization of healthcare organizations: The digital health landscape and information theory.

BACKGROUND: This paper builds on an integrated framework of digital opportunities, based on information theory that was initially proposed by Claude Shannon: 1) providers of infrastructure for transmitting information; 2) platforms, devices, and software to bundle, store and deliver others' content; 3) original content creator); 4) platforms enabling peer-to-peer exchange; and 5) connected intelligence embed in devices. We aimed to clarify the meaning of digitization of organisations in the healthcare landscape and answer 'how can healthcare organisations compete in a world that is rapidly digitizing?'. METHODS: To gain more insight into the views of different stakeholders, we invited 12 participants with a wide range of backgrounds to a workshop. Stakeholders included people representing the views from patients, carers, local hospitals, pharmacy retailers, health insurers, health services researchers, engineers, and technology and pharmaceutical companies. We organised the workshop at the IMD business school in Lausanne and used Switzerland as a case study. We asked participants to take part in group work to identify organisations with digital health products and their key success factors. We evaluated organisations' aims and competencies, and through which pathway they competed. MAIN FINDINGS: There was overlap in some key success factors, such as security, interoperability, low cost, and enough people using it, between different types of digital opportunities of the framework. No organisations that were identified during the workshop were currently operating in different opportunities. We found a range of different ways through which digital health organisations partnered with other organisations. The relatively smaller organisations identified in our workshop operated through one pathway. PRINCIPAL CONCLUSIONS: This work has implications for organisations in understanding the digital health landscape and their competitors' digital initiatives. Firstly, organisations can identify where they operate and map strengths and limitations of their digital initiatives. Secondly, they can learn from different competition pathways: 1) develop new capabilities faster; 2) collaborate with technology organisations; 3) create collaborations with traditional competitors; and 4) create a new business model. This knowledge needs to be the basis for the development of digital solutions, which will be crucial for the survival of many organisations in the healthcare landscape.

An information-theoretic perspective on the costs of cognition.

In statistics and machine learning, model accuracy is traded off with complexity, which can be viewed as the amount of information extracted from the data. Here, we discuss how cognitive costs can be expressed in terms of similar information costs, i.e. as a function of the amount of information required to update a person's prior knowledge (or internal model) to effectively solve a task. We then examine the theoretical consequences that ensue from this assumption. This framework naturally explains why some tasks - for example, unfamiliar or dual tasks - are costly and permits to quantify these costs using information-theoretic measures. Finally, we discuss brain implementation of this principle and show that subjective cognitive costs can originate either from local or global capacity limitations on information processing or from increased rate of metabolic alterations. These views shed light on the potential adaptive value of cost-avoidance mechanisms.

A new approach for sample size calculation in cost-effectiveness studies based on value of information.

BACKGROUND: Value of information is now recognized as a reference method in the decision process underpinning cost-effectiveness evaluation. The expected value of perfect information (EVPI) is the expected value from completely reducing the uncertainty surrounding the cost-effectiveness of an innovative intervention. Among sample size calculation methods used in cost-effectiveness studies, only one is coherent with this decision framework. It uses a Bayesian approach and requires data of a pre-existing cost-effectiveness study to derive a valid prior EVPI. When evaluating the cost-effectiveness of innovations, no observed prior EVPI is usually available to calculate the sample size. We here propose a sample size calculation method for cost-effectiveness studies, that follows the value of information theory, and, being frequentist, can be based on assumptions if no observed prior EVPI is available. METHODS: The general principle of our method is to define the sampling distribution of the incremental net monetary benefit (ΔB), or the distribution of ΔB that would be observed in a planned cost-effectiveness study of size n. Based on this sampling distribution, the EVPI that would remain at the end of the trial (EVPIn) is estimated. The optimal sample size of the planned cost-effectiveness study is the n for which the cost of including an additional participant becomes equal or higher than the value of the information gathered through this inclusion. RESULTS: Our method is illustrated through four examples. The first one is used to present the method in depth and describe how the sample size may vary according to the parameters' value. The three other examples are used to illustrate in different situations how the sample size may vary according to the ceiling cost-effectiveness ratio, and how it compares with a test statistic-based method. We developed an R package (EBASS) to run these calculations. CONCLUSIONS: Our sample size calculation method follows the value of information theory that is now recommended for analyzing and interpreting cost-effectiveness data, and sets the size of a study that balances its cost and the value of its information.

PyPhi: A toolbox for integrated information theory.

Integrated information theory provides a mathematical framework to fully characterize the cause-effect structure of a physical system. Here, we introduce PyPhi, a Python software package that implements this framework for causal analysis and unfolds the full cause-effect structure of discrete dynamical systems of binary elements. The software allows users to easily study these structures, serves as an up-to-date reference implementation of the formalisms of integrated information theory, and has been applied in research on complexity, emergence, and certain biological questions. We first provide an overview of the main algorithm and demonstrate PyPhi's functionality in the course of analyzing an example system, and then describe details of the algorithm's design and implementation. PyPhi can be installed with Python's package manager via the command 'pip install pyphi' on Linux and macOS systems equipped with Python 3.4 or higher. PyPhi is open-source and licensed under the GPLv3; the source code is hosted on GitHub at https://github.com/wmayner/pyphi. Comprehensive and continually-updated documentation is available at https://pyphi.readthedocs.io. The pyphi-users mailing list can be joined at https://groups.google.com/forum/#!forum/pyphi-users. A web-based graphical interface to the software is available at http://integratedinformationtheory.org/calculate.html.

Diversity begets diversity: A global perspective on gender equality in scientific society leadership.

Research shows that gender inequality is still a major issue in academic science, yet academic societies may serve as underappreciated and effective avenues for promoting female leadership. That is, society membership is often self-selective, and board positions are elected (with a high turnover compared to institutions)-these characteristics, among others, may thus create an environment conducive to gender equality. We therefore investigate this potential using an information-theoretic approach to quantify gender equality (male:female ratios) in zoology society boards around the world. We compare alternative models to analyze how society characteristics might predict or correlate with the proportion of female leaders, and find that a cultural model, including society age, size of board and whether or not a society had an outward commitment or statement of equality, was the most informative predictor for the gender ratio of society boards and leadership positions. This model was more informative than alternatives that considered, for instance, geographic location, discipline of study or taxonomic focus. While women were more highly represented in society leadership than in institutional academic leadership, this representation was still far short of equal (~30%): we thus also provide a checklist and recommendations for societies to contribute to global gender equality in science.

MACS - a new SPM toolbox for model assessment, comparison and selection.

BACKGROUND: In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for statistical model inference. NEW METHOD: We introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data. MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection and model averaging in fMRI data analysis. RESULTS: The toolbox - which is freely available from GitHub - directly builds on the Statistical Parametric Mapping (SPM) software package and is easy-to-use, general-purpose, modular, readable and extendable. We validate the toolbox by reproducing model selection and model averaging results from earlier publications. COMPARISON WITH EXISTING METHODS: A previous toolbox for model diagnosis in fMRI has been discontinued and other approaches to model comparison between GLMs have not been translated into reusable computational resources in the past. CONCLUSIONS: Increased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI studies.

Infoxicación en salud. La sobrecarga de información sobre salud en la web y el riesgo de que lo importante se haga invisible

Objetivo. Este estudio tiene como objetivos 1) crear conciencia del volumen de información en salud existente en la web de calidad, 2) explorar la percepción de profesionales de la información con relación al uso de fuentes cualificadas en la toma de decisiones en salud, y 3) presentar recomendaciones que permitan fortalecer las capacidades de los trabajadores de la salud y las competencias institucionales relacionadas con la alfabetización digital. Métodos. Se realizó un estudio no experimental descriptivo transversal con una muestra no probabilística de 32 profesionales de la información de nueve países. Se recopiló información de internet sobre el volumen de contenidos existentes en herramientas web, redes sociales y fuentes de información en salud. Se realizaron búsquedas en inglés y en español utilizando las palabras clave Ebola, Zika, Dengue, Chikungunya, Safe food, Health equity, Safe sex, y Obesit. Por último, se obtuvo información sobre la oferta de formación formal en temas de alfabetización digital, gestión de información y otros relacionados. Resultados. Seleccionando sólo cuatro enfermedades de alto impacto en salud pública en mayo de 2016 y promediando un tiempo de revisión mínimo de cada producto de información, se tardaría más de 50 años seguidos sin dormir para consultar todo lo publicado en línea sobre Dengue, Zika, Ebola y Chikungunja. Conclusión. Se concluye que la salud pública se beneficiaría con instituciones de salud que implementaran estrategias formales de gestión del conocimiento, con instituciones académicas de ciencias de la salud que incorporaran programas formales de alfabetización digital y con trabajadores de la salud cuyo desarrollo profesional sea responsable y funcional en la sociedad de la información.

Objective. The objectives of this study were to: 1) raise awareness of the volume of quality health information on the Internet; 2) explore perceptions of information professionals with regard to the use of qualified sources for health decision-making; and 3) make recommendations that facilitate strengthening health worker capacities and institutional competencies related to digital literacy. Methods. A non-experimental, descriptive cross-sectional study was conducted with a non-probability sample of 32 information professionals from nine countries. Internet information was compiled on the volume of content in Internet tools, social networks, and health information sources. Searches in English and Spanish were carried out using the keywords Ebola, Zika, dengue, chikungunya, safe food, health equity, safe sex, and obesity. Finally, information was obtained on opportunities for formal education on the subjects of digital literacy, information management, and other related topics. Results. Selecting only four diseases with a high impact on public health in May 2016 and averaging minimum review time for each information product, it would take more than 50 years without sleeping to consult everything that is published online about dengue, Zika, Ebola, and chikungunya. Conclusion. We conclude that public health would benefit from: health institutions implementing formal knowledge management strategies; academic health sciences institutions incorporating formal digital literacy programs; and having health workers who are professionally responsible and functional in the information society.

Objetivos. 1) Conscientizar sobre o volume de informação em saúde de qualidade existente na internet, 2) explorar a percepção dos profissionais da informação sobre o uso de fontes qualificadas na tomada de decisão em saúde e 3) fazer recomendações para reforçar o preparo dos profissionais de saúde e as competências institucionais quanto à alfabetização digital. Métodos. Foi realizado estudo transversal descritivo não experimental em uma amostra não probabilística com 32 profissionais da informação de nove países. Foi coletada informação da internet sobre o volume de conteúdos existentes sobre ferramentas de internet, redes sociais e fontes de informação em saúde. Foram realizadas buscas em inglês e espanhol utilizando as palavras-chave Ebola, Zika, Dengue, Chikungunya, Safe food, Health equity, Safe sex, e Obesity. E foi obtida informação sobre a oferta de cursos de formação em alfabetização digital, gestão da informação e outros tópicos relacionados. Resultados. Com a seleção de apenas quatro doenças de alto impacto em saúde pública em maio de 2016, com tempo de revisão médio mínimo para cada produto de informação, seriam necessários mais de 50 anos contínuos, sem intervalo para dormir, para consultar todo o conteúdo publicado online sobre dengue, zika, Ebola e chikungunya. Conclusão. Conclui-se que existiria benefício para a saúde pública se as instituições de saúde implementassem estratégias formais de gestão do conhecimento, as instituições acadêmicas de ciências da saúde incorporassem programas formais de alfabetização digital e os profissionais de saúde investissem em desenvolvimento profissional responsável e prático na sociedade da informação.

Seeking Beta: Experimental Considerations and Theoretical Implications Regarding the Detection of Curvature in Dose-Response Relationships for Chromosome Aberrations.

The concept of curvature in dose-response relationships figures prominently in radiation biology, encompassing a wide range of interests including radiation protection, radiotherapy and fundamental models of radiation action. In this context, the ability to detect even small amounts of curvature becomes important. Standard (ST) statistical approaches used for this purpose typically involve least-squares regression, followed by a test on sums of squares. Because we have found that these methods are not particularly robust, we investigated an alternative information theoretic (IT) approach, which involves Poisson regression followed by information-theoretic model selection. Our first objective was to compare the performances of the ST and IT methods by using them to analyze mFISH data on gamma-ray-induced simple interchanges in human lymphocytes, and on Monte Carlo simulated data. Real and simulated data sets that contained small-to-moderate curvature were deliberately selected for this exercise. The IT method tended to detect curvature with higher confidence than the ST method. The finding of curvature in the dose response for true simple interchanges is discussed in the context of fundamental models of radiation action. Our second objective was to optimize the design of experiments aimed specifically at detecting curvature. We used Monte Carlo simulation to investigate the following parameters. Constrained by available resources (i.e., the total number of cells to be scored) these include: the optimal number of dose points to use; the best way to apportion the total number of cells among these dose points; and the spacing of dose intervals. Counterintuitively, our simulation results suggest that 4-5 radiation doses were typically optimal, whereas adding more dose points may actually prove detrimental. Superior results were also obtained by implementing unequal dose spacing and unequal distributions in the number of cells scored at each dose.

Cortical Entropy, Mutual Information and Scale-Free Dynamics in Waking Mice.

Some neural circuits operate with simple dynamics characterized by one or a few well-defined spatiotemporal scales (e.g. central pattern generators). In contrast, cortical neuronal networks often exhibit richer activity patterns in which all spatiotemporal scales are represented. Such "scale-free" cortical dynamics manifest as cascades of activity with cascade sizes that are distributed according to a power-law. Theory and in vitro experiments suggest that information transmission among cortical circuits is optimized by scale-free dynamics. In vivo tests of this hypothesis have been limited by experimental techniques with insufficient spatial coverage and resolution, i.e., restricted access to a wide range of scales. We overcame these limitations by using genetically encoded voltage imaging to track neural activity in layer 2/3 pyramidal cells across the cortex in mice. As mice recovered from anesthesia, we observed three changes: (a) cortical information capacity increased, (b) information transmission among cortical regions increased and (c) neural activity became scale-free. Our results demonstrate that both information capacity and information transmission are maximized in the awake state in cortical regions with scale-free network dynamics.