Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data.

BACKGROUND: Controlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini-Hochberg (BH) and Benjamini-Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates. RESULTS: When there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures. CONCLUSION: The modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.

AIMS: Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. METHODS: We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. RESULTS: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. CONCLUSIONS: We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the 'lowest number of feature subset' with the 'maximal average AUC over the nested cross validation' and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis.

BACKGROUND: Finding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. RESULTS: Here, we developed the R package "CeTF" that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems - for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle. CONCLUSION: This preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor ( http://bioconductor.org/packages/CeTF ) and GitHub ( http://github.com/cbiagii/CeTF ).

A Novel 5-Cytokine Panel Outperforms Conventional Predictive Markers of Persistent Organ Failure in Acute Pancreatitis.

INTRODUCTION: Existing laboratory markers and clinical scoring systems have shown suboptimal accuracies for early prediction of persistent organ failure (POF) in acute pancreatitis (AP). We used information theory and machine learning to select the best-performing panel of circulating cytokines for predicting POF early in the disease course and performed verification of the cytokine panel's prognostic accuracy in an independent AP cohort. METHODS: The derivation cohort included 60 subjects with AP with early serum samples collected between 2007 and 2010. Twenty-five cytokines associated with an acute inflammatory response were ranked by computing the mutual information between their levels and the outcome of POF; 5 high-ranking cytokines were selected. These cytokines were subsequently measured in early serum samples of an independent prospective verification cohort of 133 patients (2012-2016), and the results were trained in a Random Forest classifier. Cross-validated performance metrics were compared with the predictive accuracies of conventional laboratory tests and clinical scores. RESULTS: Angiopoietin 2, hepatocyte growth factor, interleukin 8, resistin, and soluble tumor necrosis factor receptor 1A were the highest-ranking cytokines in the derivation cohort; each reflects a pathologic process relevant to POF. A Random Forest classifier trained the cytokine panel in the verification cohort and achieved a 10-fold cross-validated accuracy of 0.89 (area under the curve 0.91, positive predictive value 0.89, and negative predictive value 0.90), which outperformed individual cytokines, laboratory tests, and clinical scores (all P ≤ 0.006). DISCUSSION: We developed a 5-cytokine panel, which accurately predicts POF early in the disease process and significantly outperforms the prognostic accuracy of existing laboratory tests and clinical scores.

Biology transcends the limits of computation.

Cognition-sensing and responding to the environment-is the unifying principle behind the genetic code, origin of life, evolution, consciousness, artificial intelligence, and cancer. However, the conventional model of biology seems to mistake cause and effect. According to the reductionist view, the causal chain in biology is chemicals → code → cognition. Despite this prevailing view, there are no examples in the literature to show that the laws of physics and chemistry can produce codes, or that codes produce cognition. Chemicals are just the physical layer of any information system. In contrast, although examples of cognition generating codes and codes controlling chemicals are ubiquitous in biology and technology, cognition remains a mystery. Thus, the central question in biology is: What is the nature and origin of cognition? In order to elucidate this pivotal question, we must cultivate a deeper understanding of information flows. Through this lens, we see that biological cognition is volitional (i.e., deliberate, intentional, or knowing), and while technology is constrained by deductive logic, living things make choices and generate novel information using inductive logic. Information has been called "the hard problem of life' and cannot be fully explained by known physical principles (Walker et al., 2017). The present paper uses information theory (the mathematical foundation of our digital age) and Turing machines (computers) to highlight inaccuracies in prevailing reductionist models of biology, and proposes that the correct causation sequence is cognition → code → chemicals.

Concepts and Applications of Information Theory to Immuno-Oncology.

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

CTD: An information-theoretic algorithm to interpret sets of metabolomic and transcriptomic perturbations in the context of graphical models.

We consider the following general family of algorithmic problems that arises in transcriptomics, metabolomics and other fields: given a weighted graph G and a subset of its nodes S, find subsets of S that show significant connectedness within G. A specific solution to this problem may be defined by devising a scoring function, the Maximum Clique problem being a classic example, where S includes all nodes in G and where the score is defined by the size of the largest subset of S fully connected within G. Major practical obstacles for the plethora of algorithms addressing this type of problem include computational efficiency and, particularly for more complex scores which take edge weights into account, the computational cost of permutation testing, a statistical procedure required to obtain a bound on the p-value for a connectedness score. To address these problems, we developed CTD, "Connect the Dots", a fast algorithm based on data compression that detects highly connected subsets within S. CTD provides information-theoretic upper bounds on p-values when S contains a small fraction of nodes in G without requiring computationally costly permutation testing. We apply the CTD algorithm to interpret multi-metabolite perturbations due to inborn errors of metabolism and multi-transcript perturbations associated with breast cancer in the context of disease-specific Gaussian Markov Random Field networks learned directly from respective molecular profiling data.

An investigation into the allosteric mechanism of GPCR A2A adenosine receptor with trajectory-based information theory and complex network model.

G protein-coupled receptors (GPCRs), a large superfamily of transmembrane (TM) proteins, allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to effector proteins in the intracellular (IC) domain, therefore forming the largest class of drug targets. The A2A adenosine receptor (A2AAR), a class-A GPCR, has been extensively studied as it offers numerous possibilities for therapeutic applications. However, the mechanism of allosteric communication between EC and IC domains is not completely clear. In this work, we utilize torsional mutual information to quantify the correlated motions of residue pairs from its molecular dynamics (MD) simulation trajectories, and further use the complex network model to obtain allosteric pipelines and hubs. The identified allosteric communication pipelines mainly transmit the signal from EC domain to the cytoplasmic ends of TM helix 5 (TM5), TM6 and TM7. The allosteric hubs, mostly located at TM5, TM6 and TM7, play an important role in mediating allosteric signal transmission to keep the receptor rigid and prevent G protein from binding to IC domain, which can explain the reason why their mutations distant from ligand-binding site do not affect the ligand binding affinity but affect the ligand efficacy. Additionally, we identify the key residues located in antagonist ZM241385 binding pocket which mediate multiple allosteric pathways and have been experimentally proven to play a critical role in affecting the ligand potency. This study is helpful for understanding the allosteric communication mechanism of A2AAR, and can provide valuable information for the structure-based drug design of GPCRs.Communicated by Ramaswamy H. Sarma.

Research synthesis of information theory measures of uncertainty: Meta-analysis of entropy and mutual information of diagnostic tests.

RATIONALE, AIMS, AND OBJECTIVES: Assessing the performance of diagnostic tests requires evaluation of the amount of diagnostic uncertainty a test reduces. Statistical measures, such as sensitivity and specificity, currently dominating the evidence-based medicine (EBM) and related fields, cannot explicitly measure this reduction in diagnostic uncertainty. Mutual information (MI), an information theory statistic, explicitly quantifies diagnostic uncertainty by measuring information gain before vs after diagnostic testing. In this paper, we propose the use of MI as a single measure to express diagnostic test performance and demonstrate how it can be used in the meta-analysis of diagnostic test studies. METHODS: We use two case studies from the literature to demonstrate the applicability of MI meta-analysis in assessing diagnostic performance. Meta-analysis of studies evaluating (a) ultrasonography (US) to detect endometrial cancer and (b) magnetic resonance angiography to detect arterial stenosis. RESULTS: The results of MI meta-analyses are comparable to those of traditional statistical measures' meta-analyses. However, the results of MI are easier to understand as it relates directly to the extent of uncertainty a diagnostic test can reduce. For example, the US test, diagnosing endometrial cancer, is 40% specific and 94% sensitive. The combination of these values is difficult to interpret and may lead to inappropriate assessment (eg, one could favour the test due to its high sensitivity, ignoring its low specificity). In terms of MI, however, a single metric shows that the test reduces diagnostic uncertainty by 10%, which many users may consider small under most circumstances. CONCLUSIONS: We have demonstrated the suitability of MI in assessing the performance of diagnostic tests, which can facilitate easier interpretation of the true utility of diagnostic tests. Similarly, to the guidance for interpretation of effect size of treatment interventions, we also propose the guidelines for interpretation of the utility of diagnostic tests based on the magnitude of reduction in diagnostic uncertainty.

Evolution of communication signals and information during species radiation.

Communicating species identity is a key component of many animal signals. However, whether selection for species recognition systematically increases signal diversity during clade radiation remains debated. Here we show that in woodpecker drumming, a rhythmic signal used during mating and territorial defense, the amount of species identity information encoded remained stable during woodpeckers' radiation. Acoustic analyses and evolutionary reconstructions show interchange among six main drumming types despite strong phylogenetic contingencies, suggesting evolutionary tinkering of drumming structure within a constrained acoustic space. Playback experiments and quantification of species discriminability demonstrate sufficient signal differentiation to support species recognition in local communities. Finally, we only find character displacement in the rare cases where sympatric species are also closely related. Overall, our results illustrate how historical contingencies and ecological interactions can promote conservatism in signals during a clade radiation without impairing the effectiveness of information transfer relevant to inter-specific discrimination.

Multiplexing information flow through dynamic signalling systems.

We consider how a signalling system can act as an information hub by multiplexing information arising from multiple signals. We formally define multiplexing, mathematically characterise which systems can multiplex and how well they can do it. While the results of this paper are theoretical, to motivate the idea of multiplexing, we provide experimental evidence that tentatively suggests that the NF-κB transcription factor can multiplex information about changes in multiple signals. We believe that our theoretical results may resolve the apparent paradox of how a system like NF-κB that regulates cell fate and inflammatory signalling in response to diverse stimuli can appear to have the low information carrying capacity suggested by recent studies on scalar signals. In carrying out our study, we introduce new methods for the analysis of large, nonlinear stochastic dynamic models, and develop computational algorithms that facilitate the calculation of fundamental constructs of information theory such as Kullback-Leibler divergences and sensitivity matrices, and link these methods to a new theory about multiplexing information. We show that many current models such as those of the NF-κB system cannot multiplex effectively and provide models that overcome this limitation using post-transcriptional modifications.

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.

Collective Pulsing in Xeniid Corals: Part I-Using Computer Vision and Information Theory to Search for Coordination.

Xeniid corals (Cnidaria: Alcyonacea), a family of soft corals, include species displaying a characteristic pulsing behavior. This behavior has been shown to increase oxygen diffusion away from the coral tissue, resulting in higher photosynthetic rates from mutualistic symbionts. Maintaining such a pulsing behavior comes at a high energetic cost, and it has been proposed that coordinating the pulse of individual polyps within a colony might enhance the efficiency of fluid transport. In this paper, we test whether patterns of collective pulsing emerge in coral colonies and investigate possible interactions between polyps within a colony. We video recorded different colonies of Heteroxenia sp. in a laboratory environment. Our methodology is based on the systematic integration of a computer vision algorithm (ISOMAP) and an information-theoretic approach (transfer entropy), offering a vantage point to assess coordination in collective pulsing. Perhaps surprisingly, we did not detect any form of collective pulsing behavior in the colonies. Using artificial data sets, however, we do demonstrate that our methodology is capable of detecting even weak information transfer. The lack of a coordination is consistent with previous work on many cnidarians where coordination between actively pulsing polyps and medusa has not been observed. In our companion paper, we show that there is no fluid dynamic benefit of coordinated pulsing, supporting this result. The lack of coordination coupled with no obvious fluid dynamic benefit to grouping suggests that there may be non-fluid mechanical advantages to forming colonies, such as predator avoidance and defense.

Information Theory: New Look at Oncogenic Signaling Pathways.

Sustained pro-proliferative signaling is one of the hallmarks of cancer. Although it is generally understood that the oncogenic signaling pathways are overactivated, or at least abnormally activated, in cancer cells, important mechanistic details of such abnormal activation remain unresolved. Among these details are such aspects of signaling as robustness, redundancy, signal amplification, and others, which touch upon the domain of information theory - the field of mathematics and engineering dealing with properties of information storage, encoding, and transmission. Information theory only recently has started to be applied to intracellular signaling. Here, we overview the recent advances provided by the information theory, focusing on the nuclear factor (NF)-κB, extracellular signal-regulated kinase (ERK), and G-protein-coupled receptor (GPCR) pathways, which are frequently hijacked in cancer. Furthermore, we show how viewing previously untouched mechanics of oncogenic signaling through information theory applications may evolve into novel ways of anticancer drug discovery.

Information-theoretic analysis of multivariate single-cell signaling responses.

Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI-statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single-cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.

Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation.

BACKGROUND: Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation. RESULTS: We undertake an information theoretic analysis of two stochastic models concerning glioma differentiation therapy, an alternative cancer treatment modality whose underlying intracellular mechanisms remain poorly understood. Discernible changes in response dynamics, as captured by summary measures, were observed at low noise levels. Mitigating certain feedback mechanisms present in the signaling network improved information transmission overall, as did targeted subsampling and clustering of response dynamics. CONCLUSION: Computing the channel capacity of noisy signaling pathways present great probative value in uncovering the prevalent trends in noise-induced dynamics. Areas of high dynamical variation can provide concise snapshots of informative system behavior that may otherwise be overlooked. Through this approach, we can examine the delicate interplay between noise and information, from signal to response, through the observed behavior of relevant system components.

Communication Channels Used by Women to Contact a Population-Based Breast Cancer Screening Program in Catalonia, Spain.

Communication is a corner stone of population-based breast cancer screening programs that need to invite all the women from their target population and provide them with balanced information on screening to guaranty informed participation. Invited women also need to be able to contact screening programs to get further information on screening procedures and/or cancel and reschedule appointments. This study describes the communication channels used by women invited for breast cancer screening to contact the program. The study population consisted of 141,684 women, aged 50-69 years, who were invited during 2015-2016 for screening by the Catalan Breast Cancer Screening Program (Spain). Multiple logistic regression models were performed to assess the association between age, screening history, socioeconomic status and reasons for contacting the program and the outcome variables (contact with the program; contact through information and communication technology (ICT) channels). Among the 141,684 women invited for BC screening, 22.5% contacted the screening office mainly to reschedule (42.2%) and cancel (29.2%) appointments. While the communication channel mostly used was the telephone, 24.8% of the women used ICT. ICT was more frequently used by women who had never been screened. Women who wanted to change their appointment were 65% (OR 1.65, 95%CI 1.54-1.76) more likely to use ICT than women who wanted to cancel it. This study showed the need to reinforce communication between women and breast cancer screening programs and the importance of offering communication channels suiting all women's needs to facilitate appointments' rescheduling and cancelling and therefore improve screening programs' efficiency.

Stimulus dynamics-dependent information transfer of olfactory and vomeronasal sensory neurons in mice.

The parallel processing of chemical signals by the main olfactory system and the vomeronasal system has been known to control animal behavior. The physiological significance of peripheral parallel pathways consisting of olfactory sensory neurons and vomeronasal sensory neurons is not well understood. Here, we show complementary characteristics of the information transfer of the olfactory sensory neurons and vomeronasal sensory neurons. A difference in excitability between the sensory neurons was revealed by patch-clamp experiments. The olfactory and vomeronasal sensory neurons showed phasic and tonic firing, respectively. Intrinsic channel kinetics determining firing patterns was demonstrated by a Hodgkin-Huxley-style computation. Our estimation of the information carried by action potentials during one cycle of sinusoidal stimulation with variable durations revealed distinct characteristics of information transfer between the sensory neurons. Phasic firing of the olfactory sensory neurons was suitable to carry information about rapid changes in a shorter cycle (<200 ms). In contrast, tonic firing of the vomeronasal sensory neurons was able to convey information about smaller stimuli changing slowly with longer cycles (>500 ms). Thus, the parallel pathways of the two types of sensory neurons can convey information about a wide range of dynamic stimuli. A combination of complementary characteristics of olfactory information transfer may enhance the synergy of the interaction between the main olfactory system and the vomeronasal system.