The Missing Person problem through the lens of information theory

Missing person cases typically require a genetic kinship test to determine the relationship between an unidentified individual and the relatives of the missing person. When not enough genetic evidence has been collected the lack of statistical power of these tests might lead to unreliable results. This is particularly true when just a few distant relatives are available for genotyping. In this contribution, we considered a Bayesian network approach for kinship testing and proposed several information theoretic metrics in order to quantitatively evaluate the information content of pedigrees. We show how these statistics are related to the widely used likelihood ratio values and could be employed to efficiently prioritize family members in order to optimize the statistical power in missing person problems. Our methodology seamlessly integrates with Bayesian modeling approaches, like the GENis platform that we have recently developed for high-throughput missing person identification tasks. Furthermore, our approach can also be easily incorporated into Elston-Stewart forensic frameworks. To facilitate the application of our methodology, we have developed the forensIT package, freely available on CRAN repository, which implements all the methodologies described in our manuscript.

Stress-induced failure of embodied cognition: A general model

We derive the classic, ubiquitous, but enigmatic Yerkes-Dodson effect of applied stress on real-world performance in a highly natural manner from fundamental assumptions on cognition and its dynamics, as constrained by the asymptotic limit theorems of information and control theories. We greatly extend the basic approach by showing how differences in an underlying probability model can affect the dynamics of decision across a broad range of cognitive enterprise. Most particularly, however, this development may help inform our understanding of the different expressions of human psychopathology. A 'thin tailed' underlying distribution appears to characterize expression of 'ordinary' situational depression/anxiety symptoms of conditions like burnout induced by toxic stress. A 'fat tailed' underlying distribution appears to be associated with brain structure and function abnormalities leading to serious mental illness and poor decision making where symptoms are not only emerging in the setting of severe stress but may also appear in a highly punctuated manner at relatively lower levels of stress. A simple hierarchical optimization shows how environmental 'shadow price' constraints can buffer or aggravate the effects of stress and arousal. Extension of the underlying theory to other patterns of pathology, like immune disorders and premature aging, seems apt. Applications to the punctuated dynamics of institutional cognition under stress also appear possible. Ultimately, the probability models studied here can be converted to new statistical tools for the analysis of observational and experimental data.

Determination of the point resolution of high-resolution transmission electron microscope using the through-focus technique

From the viewpoint of evaluating the instrumental performance of high-resolution electron microscopy (HREM), the Scherzer condition was investigated using information theory. As a result, the optimum defocus amount Δf can be expressed based on [Formula: see text] , and the formula [Formula: see text] is obtained. Furthermore, a procedure for measuring point resolution using the through-focus technique is developed, and a new method for determining the spherical aberration coefficient using the variance of Δf is introduced in the procedure.

Probabilistic assessment of wind power plant energy potential through a copula-deep learning approach in decision trees

In the face of environmental degradation and diminished energy resources, there is an urgent need for clean, affordable, and sustainable energy solutions, which highlights the importance of wind energy. In the global transition to renewable energy sources, wind power has emerged as a key player that is in line with the Paris Agreement, the Net Zero Target by 2050, and the UN 2030 Goals, especially SDG-7. It is critical to consider the variable and intermittent nature of wind to efficiently harness wind energy and evaluate its potential. Nonetheless, since wind energy is inherently variable and intermittent, a comprehensive assessment of a prospective site's wind power generation potential is required. This analysis is crucial for stakeholders and policymakers to make well-informed decisions because it helps them assess financial risks and choose the best locations for wind power plant installations. In this study, we introduce a framework based on Copula-Deep Learning within the context of decision trees. The main objective is to enhance the assessment of the wind power potential of a site by exploiting the intricate and non-linear dependencies among meteorological variables through the fusion of copulas and deep learning techniques. An empirical study was carried out using wind power plant data from Turkey. This dataset includes hourly power output measurements as well as comprehensive meteorological data for 2021. The results show that acknowledging and addressing the non-independence of variables through innovative frameworks like the Copula-LSTM based decision tree approach can significantly improve the accuracy and reliability of wind power plant potential assessment and analysis in other real-world data scenarios. The implications of this research extend beyond wind energy to inform decision-making processes critical for a sustainable energy future.

Towards generalizable Graph Contrastive Learning: An information theory perspective

Graph Contrastive Learning (GCL) is increasingly employed in graph representation learning with the primary aim of learning node/graph representations from a predefined pretext task that can generalize to various downstream tasks. Meanwhile, the transition from a specific pretext task to diverse and unpredictable downstream tasks poses a significant challenge for GCL's generalization ability. Most existing GCL approaches maximize mutual information between two views derived from the original graph, either randomly or heuristically. However, the generalization ability of GCL and its theoretical principles are still less studied. In this paper, we introduce a novel metric GCL-GE, to quantify the generalization gap between predefined pretext and agnostic downstream tasks. Given the inherent intractability of GCL-GE, we leverage concepts from information theory to derive a mutual information upper bound that is independent of the downstream tasks, thus enabling the metric's optimization despite the variability in downstream tasks. Based on the theoretical insight, we propose InfoAdv, a GCL framework to directly enhance generalization by jointly optimizing GCL-GE and InfoMax. Extensive experiments validate the capability of InfoAdv to enhance performance across a wide variety of downstream tasks, demonstrating its effectiveness in improving the generalizability of GCL.

Preserving domain private information via mutual information maximization

Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant features could significantly improve the generalization of a model with respect to a new data domain. However, current methodologies often neglect to retain domain private information, which is the unique information inherent to the unlabeled new domain, compromising generalization. This paper presents a novel method that utilizes mutual information to protect this domain-specific information, ensuring that the latent features of the unlabeled data not only remain domain-invariant but also reflect the unique statistics of the unlabeled domain. We show that simultaneous maximization of mutual information and reduction of domain divergence can effectively preserve domain-private information. We further illustrate that a neural estimator can aptly estimate the mutual information between the unlabeled input space and its latent feature space. Both theoretical analysis and empirical results validate the significance of preserving such unique information of the unlabeled domain for cross-domain generalization. Comparative evaluations reveal our method's superiority over existing state-of-the-art techniques across multiple benchmark datasets.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

An Information-Theoretic Account of Availability Effects in Language Production

I present a computational-level model of language production in terms of a combination of information theory and control theory in which words are chosen incrementally in order to maximize communicative value subject to an information-theoretic capacity constraint. The theory generally predicts a tradeoff between ease of production and communicative accuracy. I apply the theory to two cases of apparent availability effects in language production, in which words are selected on the basis of their accessibility to a speaker who has not yet perfectly planned the rest of the utterance. Using corpus data on English relative clause complementizer dropping and experimental data on Mandarin noun classifier choice, I show that the theory reproduces the observed phenomena, providing an alternative account to Uniform Information Density and a promising general model of language production which is tightly linked to emerging theories in computational neuroscience.

Information-Theoretic Analysis of a Model of CAR-4-1BB-Mediated NFκB Activation

Systems biology utilizes computational approaches to examine an array of biological processes, such as cell signaling, metabolomics and pharmacology. This includes mathematical modeling of CAR T cells, a modality of cancer therapy by which genetically engineered immune cells recognize and combat a cancerous target. While successful against hematologic malignancies, CAR T cells have shown limited success against other cancer types. Thus, more research is needed to understand their mechanisms of action and leverage their full potential. In our work, we set out to apply information theory on a mathematical model of NFκB signaling initiated by the CAR following antigen encounter. First, we estimated channel capacity for CAR-4-1BB-mediated NFκB signal transduction. Next, we evaluated the pathway's ability to distinguish contrasting "low" and "high" antigen concentration levels, depending on the amount of variability in protein concentrations. Finally, we assessed the fidelity by which NFκB activation reflects the encountered antigen concentration, depending on the prevalence of antigen-positive targets in tumor population. We found that in most scenarios, fold change in the nuclear concentration of NFκB carries a higher channel capacity for the pathway than NFκB's absolute response. Additionally, we found that most errors in transducing the antigen signal through the pathway skew towards underestimating the concentration of encountered antigen. Finally, we found that disabling IKKβ deactivation could increase signaling fidelity against targets with antigen-negative cells. Our information-theoretic analysis of signal transduction can provide novel perspectives on biological signaling, as well as enable a more informed path to cell engineering.Kindly check and confirm whether the corresponding affiliation is correctly identified.this is correct.

BERTs of a feather: Studying inter- and intra-group communication via information theory and language models

When communicating, individuals alter their language to fulfill a myriad of social functions. In particular, linguistic convergence and divergence are fundamental in establishing and maintaining group identity. Quantitatively characterizing linguistic convergence is important when testing hypotheses surrounding language, including interpersonal and group communication. We provide a quantitative interpretation of linguistic convergence grounded in information theory. We then construct a computational model, built on top of a neural network model of language, that can be deployed to measure and test hypotheses about linguistic convergence in "big data." We demonstrate the utility of our convergence measurement in two case studies: (1) showing that our measurement is indeed sensitive to linguistic convergence across turns in dyadic conversation, and (2) showing that our convergence measurement is sensitive to social factors that mediate convergence in Internet-based communities (specifically, r/MensRights and r/MensLib). Our measurement also captures differences in which social factors influence web-based communities. We conclude by discussing methodological and theoretical implications of this semantic convergence analysis.

An information-theoretic approach to the typology of spatial demonstratives

We explore systems of spatial deictic words (such as 'here' and 'there') from the perspective of communicative efficiency using typological data from over 200 languages Nintemann et al. (2020). We argue from an information-theoretic perspective that spatial deictic systems balance informativity and complexity in the sense of the Information Bottleneck (Zaslavsky et al., (2018). We find that under an appropriate choice of cost function and need probability over meanings, among all the 21,146 theoretically possible spatial deictic systems, those adopted by real languages lie near an efficient frontier of informativity and complexity. Moreover, we find that the conditions that the need probability and the cost function need to satisfy for this result are consistent with the cognitive science literature on spatial cognition, especially regarding the source-goal asymmetry. We further show that the typological data are better explained by introducing a notion of consistency into the Information Bottleneck framework, which is jointly optimized along with informativity and complexity.

Information and the Umwelt: A theoretical framework for the evolution of play

Play is phylogenetically widespread, and there are many proposed theories and fitness benefits of play. However, we still need a theoretical framework that unifies our understanding of the benefits that facilitated the evolution of play in so many diverse species. Starting with von Uexküll's theory of the Umwelt (i.e., the sensory-motor worlds of animals), together with the behavior systems approach, we propose that the Umwelt is an information processing system that serves basic biological functions. During development, the Umwelt undergoes a rapid expansion in the sensory and motor stimuli it processes. We argue that play is a process that converts surplus resources into information. By increasing the information content of the developing Umwelt, play confers fitness benefits. To demonstrate that play could evolve based on its information benefits, we present a model and simulation results of the evolution of a social play learning process that provides fitness-enhancing information in adult cooperative and competitive situations. Finally, we discuss this information-theoretic framework in relation to proposed hypotheses and fitness benefits of play.

Information theory-guided heuristic progressive multi-view coding

Multi-view representation learning aims to capture comprehensive information from multiple views of a shared context. Recent works intuitively apply contrastive learning to different views in a pairwise manner, which is still scalable: view-specific noise is not filtered in learning view-shared representations; the fake negative pairs, where the negative terms are actually within the same class as the positive, and the real negative pairs are coequally treated; evenly measuring the similarities between terms might interfere with optimization. Importantly, few works study the theoretical framework of generalized self-supervised multi-view learning, especially for more than two views. To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning. Guided by it, we build a multi-view coding method with a three-tier progressive architecture, namely Information theory-guided heuristic Progressive Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the distribution between views to reduce view-specific noise. In the set-tier, IPMC constructs self-adjusted contrasting pools, which are adaptively modified by a view filter. Lastly, in the instance-tier, we adopt a designed unified loss to learn representations and reduce the gradient interference. Theoretically and empirically, we demonstrate the superiority of IPMC over state-of-the-art methods.

Information theoretic perspective on sample complexity

The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset. The learning system is then required to output a prediction rule learned from the training dataset's input-output pairs. In this work, we investigate the relationship between the sample complexity, the empirical risk and the generalization error based on the asymptotic equipartition property (AEP) (Shannon, 1948). We provide theoretical guarantees for reliable learning under the information-theoretic AEP, with respect to the generalization error and the sample size in different settings.

The QBIT theory of consciousness: Entropy and qualia

The QBIT theory is a recently introduced multi-disciplinary approach to the problem of consciousness. One of the main axioms of the theory is that when information-theoretic certainty of an observer about a stimulus goes beyond a certain threshold, the observer becomes conscious of that stimulus. This axiom could provide an explanation for how the brain generates consciousness.In short, the QBIT theory suggests that the brain generates consciousness by reducing the entropy of its internal representations below a critical threshold. This paper explains how the brain gradually minimizes the entropy of its internal representations and consequently generate minimum-entropy representations (also known as conscious representations or qualia). The paper also explores the consequences of this entropy-minimization process in the context of quantum information theory.

Phenomenological description of the acidity of the citric acid and its deprotonated species: informational-theoretical study

CONTEXT

In spite of the fact that molecular acidity is a fundamental physicochemical property of molecular systems, the vast majority of theoretical studies have focused attention on monoprotic acids and on the prediction of pKa's. Polyprotic acids, represent a challenge for electronic structure calculations since the multiple acidic sites result in a vast group of species with different conformations and reactivities. In this work, Information-theoretic (IT) concepts of localizability, order and uniformity are applied to the Citric Acid and its deprotonated species through the one-electron density functionals: Shannon entropy (S), Fisher information (I) and Disequilibrium (D), respectively. We pursue the goal of characterizing the acidity of the aforementioned species with the aim to associate the IT concepts to chemical features such as the polarizability of the protonated/deprotonated species, the liability of the acidic sites, atomic electrostatic potentials, covalent bonding. IT analyses looks very promising for future studies on the acidity of specific deprotonation-sites of polyprotic acids.

METHODS

Density functional theory (DFT) calculations were performed with Gaussian 09 program. A sensitivity analysis of the IT-measures was performed for the citric acid and the citrate using B3LYP, B3PW91, BPW91, M05-2X, M06-2X and PBEPBE functionals with the 6-311++g(3df,2p), 6-311++g(d,p), 6.311+g(d,p) and aug-cc-pVDZ basis sets. The rest of the analysis was performed with the M05-2X/6-311+G(d,p) level of theory. Additionally, aqueous media was considered by use of the SMD solvent model. The IT-measures were calculated using a suite of programs developed in our laboratory jointly with the DGRID software package.

Optimal sensor placement for the routine monitoring of urban drainage systems: A re-clustering method

The construction of an efficient monitoring network is critical for the effective and safe management of urban drainage systems. This study developed a re-clustering methodology that incorporates additional perspectives beyond node similarity to improve the traditional clustering process for optimal sensor placement. Instead of targeting event-specific water quality or hydraulic monitoring, the method integrates the water hydraulic and quality characteristics of nodes in response to the demand for routine monitoring. The implementation of this method first applies model simulation to generate the attribute datasets required for clustering analysis, and then re-clusters the initial clustering result according to the constructed re-clustering potential indices. And the information theory-based evaluation metrics were introduced to quantitatively assess the sensor deployment scheme obtained by amalgamating the two clustering results. Two networks with different drainage systems and sizes were chosen as case studies to illustrate the application of the framework. The results demonstrate that the clustering process enables to expand the information contained in the monitoring network, and that the re-clustering strategy can generate more comprehensive and practical solutions upon this basis.

The Relationship Between Word Length and Average Information Content in Japanese

Piantadosi, Tily, and Gibson analyzed a large-scale web-scraping corpus (the Google 1T dataset) and reported that word length is independently predicted from average information content (surprisal) calculated by a 2- to 4-gram model (hereafter, longer-span surprisal) across 11 Indo-European languages, namely, Czech, Dutch, English, French, German, Italian, Polish, Spanish, Portuguese, Romanian, and Swedish. However, a recent article by Meylan and Griffiths suggested the importance of preprocessing for studies with large-scale corpora and reanalyzed the same databases. After their preprocessing, the results in Piantadosi et al. were not replicated in Czech, Romanian, and Swedish. Additionally, a German-specific study by Koplenig, Kupietz, and Wolfer showed that the strict analysis did not replicate the result in Piantadosi et al. for that language with the preprocessing suggested by Meylan and Griffiths in a large-scale but less noisy database. These three studies provide evidence from 11 Indo-European languages and one Afro-Asiatic language, Hebrew, as relevant in this debate. However, we do not have evidence from other linguistic groups. This study provides evidence about Japanese based on a strict preprocessing of Google's web-scraping database. The results show that Japanese word length can be predicted independently by 2- to 4-gram surprisal.

Mechanisms of adjustments to different types of uncertainty in the reward environment across mice and monkeys

Despite being unpredictable and uncertain, reward environments often exhibit certain regularities, and animals navigating these environments try to detect and utilize such regularities to adapt their behavior. However, successful learning requires that animals also adjust to uncertainty associated with those regularities. Here, we analyzed choice data from two comparable dynamic foraging tasks in mice and monkeys to investigate mechanisms underlying adjustments to different types of uncertainty. In these tasks, animals selected between two choice options that delivered reward probabilistically, while baseline reward probabilities changed after a variable number (block) of trials without any cues to the animals. To measure adjustments in behavior, we applied multiple metrics based on information theory that quantify consistency in behavior, and fit choice data using reinforcement learning models. We found that in both species, learning and choice were affected by uncertainty about reward outcomes (in terms of determining the better option) and by expectation about when the environment may change. However, these effects were mediated through different mechanisms. First, more uncertainty about the better option resulted in slower learning and forgetting in mice, whereas it had no significant effect in monkeys. Second, expectation of block switches accompanied slower learning, faster forgetting, and increased stochasticity in choice in mice, whereas it only reduced learning rates in monkeys. Overall, while demonstrating the usefulness of metrics based on information theory in examining adaptive behavior, our study provides evidence for multiple types of adjustments in learning and choice behavior according to uncertainty in the reward environment.

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 (COD_Mn), 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.

Whole-brain network transitions within the framework of ignition and transfer entropy following VIM-MRgFUS in essential tremor patients

Magnetic resonance-guided focused ultrasound (MRgFUS) lesioning of the ventralis intermedius nucleus (VIM) has shown promise in treating drug-refractory essential tremor (ET). It remains unknown whether focal VIM lesions by MRgFUS have broader restorative effects on information flow within the whole-brain network of ET patients. We applied an information-theoretical approach based on intrinsic ignition and the concept of transfer entropy (TE) to assess the spatiotemporal dynamics after VIM-MRgFUS. Eighteen ET patients (mean age 71.44 years) underwent repeated 3T resting-state functional magnetic resonance imaging combined with Clinical Rating Scale for Tremor (CRST) assessments one day before (T0) and one month (T1) and six months (T2) post-MRgFUS, respectively. We observed increased whole brain ignition-driven mean integration (IDMI) at T1 (p < 0.05), along with trend increases at T2. Further, constraining to motor network nodes, we identified significant increases in information-broadcasting (bilateral supplementary motor area (SMA) and left cerebellar lobule III) and information-receiving (right precentral gyrus) at T1. Remarkably, increased information-broadcasting in bilateral SMA was correlated with relative improvement of the CRST in the treated hand. In addition, causal TE-based effective connectivity (EC) at T1 showed an increase from right SMA to left cerebellar lobule crus II and from left cerebellar lobule III to right thalamus. In conclusion, results suggest a change in information transmission capacity in ET after MRgFUS and a shift towards a more integrated functional state with increased levels of global and directional information flow.

Ageing as a software design flaw

Ageing is inherent to all human beings, yet why we age remains a hotly contested topic. Most mechanistic explanations of ageing posit that ageing is caused by the accumulation of one or more forms of molecular damage. Here, I propose that we age not because of inevitable damage to the hardware but rather because of intrinsic design flaws in the software, defined as the DNA code that orchestrates how a single cell develops into an adult organism. As the developmental software runs, its sequence of events is reflected in shifting cellular epigenetic states. Overall, I suggest that to understand ageing we need to decode our software and the flow of epigenetic information throughout the life course.

An information-oriented paradigm in evaluating accuracy and agreement in radiology

The goal of any radiological diagnostic process is to gain information about the patient's status. However, the mathematical notion of information is usually not adopted to measure the performance of a diagnostic test or the agreement among readers in providing a certain diagnosis. Indeed, commonly used metrics for assessing diagnostic accuracy (e.g., sensitivity and specificity) or inter-reader agreement (Cohen [Formula: see text] statistics) use confusion matrices containing the number of true- and false positives/negatives results of a test, or the number of concordant/discordant categorizations, respectively, thus lacking proper information content. We present a methodological paradigm, based on Shannon's information theory, aiming to measure both accuracy and agreement in diagnostic radiology. This approach models the information flow as a "diagnostic channel" connecting the state of the patient's disease and the radiologist or, in the case of agreement analysis, as an "agreement channel" linking two or more radiologists evaluating the same set of images. For both cases, we proposed some measures, derived from Shannon's mutual information, which can represent an alternative way to express diagnostic accuracy and agreement in radiology.Key points• Diagnostic processes can be modeled with information theory (IT).• IT metrics of diagnostic accuracy are independent from disease prevalence.• IT metrics of inter-reader agreements can overcome Cohen κ pitfalls.

A primer on entropy in neuroscience

Entropy is not just a property of a system - it is a property of a system and an observer. Specifically, entropy is a measure of the amount of hidden information in a system that arises due to an observer's limitations. Here we provide an account of entropy from first principles in statistical mechanics with the aid of toy models of neural systems. Specifically, we describe the distinction between micro and macrostates in the context of simplified binary-state neurons and the characteristics of entropy required to capture an associated measure of hidden information. We discuss the origin of the mathematical form of entropy via the indistinguishable re-arrangements of discrete-state neurons and show the way in which the arguments are extended into a phase space description for continuous large-scale neural systems. Finally, we show the ways in which limitations in neuroimaging resolution, as represented by coarse graining operations in phase space, lead to an increase in entropy in time as per the second law of thermodynamics. It is our hope that this primer will support the increasing number of studies that use entropy as a way of characterising neuroimaging timeseries and of making inferences about brain states.

Formal perspectives on shared interbrain activity in social communication: Insights from information and control theories

The mechanisms underlying a reorientation of neuroscience from a single-brain to a multi-brain frame of reference have long been with us. These revolve around the evolutionary exaptation of the inevitable second-law 'leakage' of crosstalk between co-resident cognitive phenomena. Crosstalk characterizes such processes as immune response, wound-healing, gene expression, as so on, up through and including far more rapid neural processes. It is not a great leap-of-faith to infer that similar phenomena affect/afflict social interactions between individuals within and across populations.

Computational methods to study information processing in neural circuits

The brain is an information processing machine and thus naturally lends itself to be studied using computational tools based on the principles of information theory. For this reason, computational methods based on or inspired by information theory have been a cornerstone of practical and conceptual progress in neuroscience. In this Review, we address how concepts and computational tools related to information theory are spurring the development of principled theories of information processing in neural circuits and the development of influential mathematical methods for the analyses of neural population recordings. We review how these computational approaches reveal mechanisms of essential functions performed by neural circuits. These functions include efficiently encoding sensory information and facilitating the transmission of information to downstream brain areas to inform and guide behavior. Finally, we discuss how further progress and insights can be achieved, in particular by studying how competing requirements of neural encoding and readout may be optimally traded off to optimize neural information processing.

Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care

BACKGROUND

Life-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where further functional decline might be detrimental for the self-perceived quality of life. However, there still is substantial uncertainty involved in decisions about LST. We used the framework of information theory to assess that uncertainty by measuring information processed during decision-making.

METHODS

Datasets from two multicentre studies (VIP1, VIP2) with a total of 7488 ICU patients aged 80 years or older were analysed concerning the contribution of information about the acute illness, age, gender, frailty and other geriatric characteristics to decisions about LST. The role of these characteristics in the decision-making process was quantified by the entropy of likelihood distributions and the Kullback-Leibler divergence with regard to withholding or withdrawing decisions.

RESULTS

Decisions to withhold or withdraw LST were made in 2186 and 1110 patients, respectively. Both in VIP1 and VIP2, information about the acute illness had the lowest entropy and largest Kullback-Leibler divergence with respect to decisions about withdrawing LST. Age, gender and geriatric characteristics contributed to that decision only to a smaller degree.

CONCLUSIONS

Information about the severity of the acute illness and, thereby, short-term prognosis dominated decisions about LST in old ICU patients. The smaller contribution of geriatric features suggests persistent uncertainty about the importance of functional outcome. There still remains a gap to fully explain decision-making about LST and further research involving contextual information is required.

TRIAL REGISTRATION

VIP1 study: NCT03134807 (1 May 2017), VIP2 study: NCT03370692 (12 December 2017).

Order of statistical learning depends on perceptive uncertainty

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (e _n+1 ) based on the preceding events (e _n ) that have a length of "n". It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Flexible control as surrogate reward or dynamic reward maximization

The utility of a given experience, like interacting with a particular friend or tasting a particular food, fluctuates continually according to homeostatic and hedonic principles. Consequently, to maximize reward, an individual must be able to escape or attain outcomes as preferences change, by switching between actions. Recent work on human and artificial intelligence has defined such flexible instrumental control in information theoretic terms and postulated that it may serve as a reward surrogate. Another possibility, however, is that the adaptability afforded by flexible control is tacitly implemented by planning for dynamic changes in outcome values. In the current study, an expected utility model that computes decision values over a range of possible monetary gains and losses associated with sensory outcomes provided the best fit to behavioral choice data and performed best in terms of earned rewards. Moreover, consistent with previous work on perceived control and personality, individual differences in dimensional schizotypy were correlated with behavioral choice preferences in conditions with the greatest and lowest levels of flexible control. These results contribute to a growing literature on the role of instrumental control in goal-directed choice.

The streamwater microbiome encodes hydrologic data across scales

Many fundamental questions in hydrology remain unanswered due to the limited information that can be extracted from existing data sources. Microbial communities constitute a novel type of environmental data, as they are comprised of many thousands of taxonomically and functionally diverse groups known to respond to both biotic and abiotic environmental factors. As such, these microscale communities reflect a range of macroscale conditions and characteristics, some of which also drive hydrologic regimes. Here, we assess the extent to which streamwater microbial communities (as characterized by 16S gene amplicon sequence abundance) encode information about catchment hydrology across scales. We analyzed 64 summer streamwater DNA samples collected from subcatchments within the Willamette, Deschutes, and John Day river basins in Oregon, USA, which range 0.03-29,000 km² in area and 343-2334 mm/year of precipitation. We applied information theory to quantify the breadth and depth of information about common hydrologic metrics encoded within microbial taxa. Of the 256 microbial taxa that spanned all three watersheds, we found 9.6 % (24.5/256) of taxa, on average, shared information with a given hydrologic metric, with a median 15.6 % (range = 12.4-49.2 %) reduction in uncertainty of that metric based on knowledge of the microbial biogeography. All of the hydrologic metrics we assessed, including daily discharge at different time lags, mean monthly discharge, and seasonal high and low flow durations were encoded within the microbial community. Summer microbial taxa shared the most information with winter mean flows. Our study demonstrates quantifiable relationships between streamwater microbial taxa and hydrologic metrics at different scales, likely resulting from the integration of multiple overlapping drivers of each. Streamwater microbial communities are rich sources of information that may contribute fresh insight to unresolved hydrologic questions.

Assessing Combinatorial Diversity of Aureochrome Basic Leucine Zippers through Genome-Wide Screening

Aureochromes are unique blue light-responsive light-oxygen-voltage (LOV) photoreceptors cum basic leucine zipper (bZIP) transcription factors (TFs), present exclusively in photosynthetic marine stramenopiles. Considering the availability of the complete genome sequence, this study focuses on aureochromes from Ectocarpus siliculosus. Aureochromes mediate light-regulated developmental responses in these brown photosynthetic algae. Both the LOV sensor and the bZIP effector show overall sequence-structure conservation. The structurally similar LOV + bZIP modules of aureochrome homologs/paralogs prefer a dimeric state. Besides a heterogeneous linker connecting the sensor-effector and a flexible N-terminal region, the sequence composition of both domains is vital. Aureochromes execute diverse cellular responses in different photosynthetic stramenopiles - though their activities can vary even within a given algal species. Therefore, it is important to understand whether aureochromes select dimerization partners from the same family or interact with other bZIPs as well. To regulate multifarious biological activities, it is possible that aureochromes activate the global TF interaction network. Following homo/heterodimer modeling, we address the compatibility of dimerization partners by screening through heptad repeats. We evaluate the dimer interface area in terms of gain in solvation energy and the number of hydrogen bonds/salt bridge interactions. We further explore the relative stability of these structures from a graph-theoretic perspective through well-studied measures such as the energy of the graph, average participation coefficient, and betweenness centrality. Furthermore, we also conduct an information-theoretic analysis using hitherto understudied measures such as network information centrality and Kullback-Leibler divergence. We find that all our investigations into the relative stability of the dimers using diverse methods from bioinformatics, network science, and information theory are in harmonious agreement. Coupling preferences of monomers in aureochromes can be further translated to design novel optogenetic tools useful for understanding human development and disease.

Mental control of uncertainty

Can you reduce uncertainty by thinking? Intuition suggests that this happens through the elusive process of attention: if we expend mental effort, we can increase the reliability of our sensory data. Models based on "rational inattention" formalize this idea in terms of a trade-off between the costs and benefits of attention. This paper surveys the origin of these models in economics, their connection to rate-distortion theory, and some of their recent applications to psychology and neuroscience. We also report new data from a numerosity judgment task in which we manipulate performance incentives. Consistent with rational inattention, people are able to improve performance on this task when incentivized, in part by increasing the reliability of their sensory data.

Psychedelics and schizophrenia: Distinct alterations to Bayesian inference

Schizophrenia and states induced by certain psychotomimetic drugs may share some physiological and phenomenological properties, but they differ in fundamental ways: one is a crippling chronic mental disease, while the others are temporary, pharmacologically-induced states presently being explored as treatments for mental illnesses. Building towards a deeper understanding of these different alterations of normal consciousness, here we compare the changes in neural dynamics induced by LSD and ketamine (in healthy volunteers) against those associated with schizophrenia, as observed in resting-state M/EEG recordings. While both conditions exhibit increased neural signal diversity, our findings reveal that this is accompanied by an increased transfer entropy from the front to the back of the brain in schizophrenia, versus an overall reduction under the two drugs. Furthermore, we show that these effects can be reproduced via different alterations of standard Bayesian inference applied on a computational model based on the predictive processing framework. In particular, the effects observed under the drugs are modelled as a reduction of the precision of the priors, while the effects of schizophrenia correspond to an increased precision of sensory information. These findings shed new light on the similarities and differences between schizophrenia and two psychotomimetic drug states, and have potential implications for the study of consciousness and future mental health treatments.

Computational analysis of 4-1BB-induced NFκB signaling suggests improvements to CAR cell design

Background

Chimeric antigen receptor (CAR)-expressing cells are a powerful modality of adoptive cell therapy against cancer. The potency of signaling events initiated upon antigen binding depends on the costimulatory domain within the structure of the CAR. One such costimulatory domain is 4-1BB, which affects cellular response via the NFκB pathway. However, the quantitative aspects of 4-1BB-induced NFκB signaling are not fully understood.

Methods

We developed an ordinary differential equation-based mathematical model representing canonical NFκB signaling activated by CD19scFv-4-1BB. After a global sensitivity analysis on model parameters, we ran Monte Carlo simulations of cell population-wide variability in NFκB signaling and quantified the mutual information between the extracellular signal and different levels of the NFκB signal transduction pathway.

Results

In response to a wide range of antigen concentrations, the magnitude of the transient peak in NFκB nuclear concentration varies significantly, while the timing of this peak is relatively consistent. Global sensitivity analysis showed that the model is robust to variations in parameters, and thus, its quantitative predictions would remain applicable to a broad range of parameter values. The model predicts that overexpressing NEMO and disabling IKKβ deactivation can increase the mutual information between antigen levels and NFκB activation.

Conclusions

Our modeling predictions provide actionable insights to guide CAR development. Particularly, we propose specific manipulations to the NFκB signal transduction pathway that can fine-tune the response of CD19scFv-4-1BB cells to the antigen concentrations they are likely to encounter.

Enhanced effective codon numbers to understand codon usage bias

Codon usage bias is a well recognized phenomenon but the relative influence of its major causes: G+C content, mutational biases, and selection, are often difficult to disentangle. This paper presents methods to calculate modified effective codon numbers that allow the investigation of the sources of codon bias and how genes or organisms have their codon biases shaped. In particular, it demonstrates that variation in codon usage bias across organisms is likely driven more by likely mutational forces while the variation in codon usage bias within genomes is likely driven by codon selectional forces.

On the current failure-but bright future-of topology-driven biological network alignment

Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when the optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10^-300. We predict that topological network alignment has a bright future as edge densities increase toward the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way toward high-throughput functional prediction based on topology-driven network alignment.

Analysis of phenotype-genotype associations using genomic informational field theory (GIFT)

We show how field- and information theory can be used to quantify the relationship between genotype and phenotype in cases where phenotype is a continuous variable. Given a sample population of phenotype measurements, from various known genotypes, we show how the ordering of phenotype data can lead to quantification of the effect of genotype. This method does not assume that the data has a Gaussian distribution, it is particularly effective at extracting weak and unusual dependencies of genotype on phenotype. However, in cases where data has a special form, (eg Gaussian), we observe that the effective phenotype field has a special form. We use asymptotic analysis to solve both the forward and reverse formulations of the problem. We show how p-values can be calculated so that the significance of correlation between phenotype and genotype can be quantified. This provides a significant generalisation of the traditional methods used in genome-wide association studies GWAS. We derive a field-strength which can be used to deduce how the correlations between genotype and phenotype, and their impact on the distribution of phenotypes.

Network subgraph-based approach for analyzing and comparing molecular networks

Molecular networks are built up from genetic elements that exhibit feedback interactions. Here, we studied the problem of measuring the similarity of directed networks by proposing a novel alignment-free approach: the network subgraph-based approach. Our approach does not make use of randomized networks to determine modular patterns embedded in a network, and this method differs from the network motif and graphlet methods. Network similarity was quantified by gauging the difference between the subgraph frequency distributions of two networks using Jensen-Shannon entropy. We applied the subgraph approach to study three types of molecular networks, i.e., cancer networks, signal transduction networks, and cellular process networks, which exhibit diverse molecular functions. We compared the performance of our subgraph detection algorithm with other algorithms, and the results were consistent, but other algorithms could not address the issue of subgraphs/motifs embedded within a subgraph/motif. To evaluate the effectiveness of the subgraph-based method, we applied the method along with the Jensen-Shannon entropy to classify six network models, and it achieves a 100% accuracy of classification. The proposed information-theoretic approach allows us to determine the structural similarity of two networks regardless of node identity and network size. We demonstrated the effectiveness of the subgraph approach to cluster molecular networks that exhibit similar regulatory interaction topologies. As an illustration, our method can identify (i) common subgraph-mediated signal transduction and/or cellular processes in AML and pancreatic cancer, and (ii) scaffold proteins in gastric cancer and hepatocellular carcinoma; thus, the results suggested that there are common regulation modules for cancer formation. We also found that the underlying substructures of the molecular networks are dominated by irreducible subgraphs; this feature is valid for the three classes of molecular networks we studied. The subgraph-based approach provides a systematic scenario for analyzing, compare and classifying molecular networks with diverse functionalities.

Temporal signaling, population control, and information processing through chromatin-mediated gene regulation

Chromatin regulation is a key pathway cells use to regulate gene expression in response to temporal stimuli, and is becoming widely used as a platform for synthetic biology applications. Here, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations. Chromatin regulators can silence genes in an all-or-none fashion at the single-cell level, with individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We integrate this mode of regulation with classical gene regulatory motifs, such as autoregulatory and incoherent feedforward loops, to determine the types of responses achievable with duration-dependent signaling. We demonstrate that repressive regulators without long-term epigenetic memory can filter out high frequency noise, and as part of an autoregulatory loop can precisely tune the fraction of cells in a population that expresses a gene of interest. Additionally, we find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced and, when included in a feed forward loop, enable perfect adaptation. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Altogether, we show that chromatin-mediated gene control enables a repertoire of complex cell population responses to temporal signals and can transmit higher information levels than previously measured in gene regulation.

A network information theoretic framework to characterise muscle synergies in space and time

Objective Current approaches to muscle synergy extraction rely on linear dimensionality reduction algorithms that make specific assumptions on the underlying signals. However, to capture nonlinear time varying, large-scale but also muscle-specific interactions, a more generalised approach is required. Approach Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. We first quantify informational dynamics between muscles, time-samples or muscle-time pairings using a novel mutual information formulation. We then model these pairwise interactions as multiplex networks and identify modules representing the network architecture. We employ this modularity criterion as the input parameter for dimensionality reduction, which verifiably extracts the identified modules, and also to characterise salient structures within each module. Main results This novel framework captures spatial, temporal and spatiotemporal interactions across two benchmark datasets of reaching movements, producing distinct spatial groupings and both tonic and phasic temporal patterns. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified, demonstrating significant task dependence, ability to capture trial-to-trial fluctuations and concordance across participants. Furthermore, our framework identifies submodular structures that represent the distributed networks of co-occurring signal interactions across scales. Significance The capabilities of this framework are illustrated through the concomitant continuity with previous research and novelty of the insights gained. Several previous limitations are circumvented including the extraction of functionally meaningful and multiplexed pairwise muscle couplings under relaxed model assumptions. The extracted synergies provide a holistic view of the movement while important details of task performance are readily interpretable. The identified muscle groupings transcend biomechanical constraints and the temporal patterns reveal characteristics of fundamental motor control mechanisms. We conclude that this framework opens new opportunities for muscle synergy research and can constitute a bridge between existing models and recent network-theoretic endeavours.

The learnability consequences of Zipfian distributions in language

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language.

Harnessing the information theory and chaos game representation for pattern searching among essential and non-essential genes in Bacteria

Proteins encoded by genes are engaged in most of the processes within a cell. Typing a minimal set of genes required for survival is still a challenging task. Essential genes seem to be more conservative and are usually responsible for basic functions, for instance, genetic information flow or energy production. Despite persistent advances in experimental methods, computer predictions may constitute an important part of this investigation. Firstly, they may embrace a huge amount of data and provide some characteristic patterns. Furthermore, they enable scientists to build models for predicting essential genes which are not yet verified experimentally. Some papers indicate interesting dependencies within essential genes sequences using different computer models. In this paper, an author took a three-step analysis for a deeper understanding of the fundamentals of essential and non-essential genes. Beginning from a simple nucleotide composition and finishing at long-range correlations, presents some characteristic patterns that are expected to be developed in future studies.

Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, these aggregate signals lack cellular resolution and thus are not easy to be interpreted directly in terms of parameters of neural microcircuits. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important for both neuroscientific and clinical applications. Over the years, we have developed tools based on neural network modeling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. This review article gives an overview of the main computational tools that we have developed and employed to invert LFPs and EEGs in terms of circuit-level neural phenomena, and outlines future challenges and directions for future research.

Archerfish respond to a hunting robotic conspecific

While the unique hunting behavior of archerfish has received considerable scientific attention, the specific social cues that govern behaviors like intraspecific kleptoparasitism in the species are less understood. This paper asks whether the use of a robotic facsimile representing an archerfish can elicit a social response if it approximates an archerfish's appearance, along with key features of its hunting behavior. We found that the fish respond to the robot when it hunted, as indicated by decreasing distances between the robot and fish (and among the fish) during the robot's hunting behavior sequence, as well as higher net transfer entropy when the robot was hunting. These effects were present even when the robot's "hunt" was unproductive and did not result in food. The temporal pattern of fish approach to the robot and each other indicated that the segment of robot hunting behavior proximal to the robotic facsimile shot elicited fish behavior initially. However, earlier cues in the robot's hunting sequence became important following more experience with a food contingency. This indicates that further studies could use a robotic facsimile to conduct a detailed stimulus analysis, changing aspects of the robot's appearance and behavior to uncover the basic mechanisms of information transfer among individuals in a social hunting scenario.

Characterization of multiscale logic operations in the neural circuits

Background: Ever since the seminal work by McCulloch and Pitts, the theory of neural computation and its philosophical foundation known as 'computationalism' have been central to brain-inspired artificial intelligence (AI) technologies. The present study describes neural dynamics and neural coding approaches to understand the mechanisms of neural computation. The primary focus is to characterize the multiscale nature of logic computations in the brain, which might occur at a single neuron level, between neighboring neurons via synaptic transmission, and at the neural circuit level. Results: For this, we begin the analysis with simple neuron models to account for basic Boolean logic operations at a single neuron level and then move on to the phenomenological neuron models to explain the neural computation from the viewpoints of neural dynamics and neural coding. The roles of synaptic transmission in neural computation are investigated using biologically realistic multi-compartment neuron models: two representative computational entities, CA1 pyramidal neuron in the hippocampus and Purkinje fiber in the cerebellum, are analyzed in the information-theoretic framework. We then construct two-dimensional mutual information maps, which demonstrate that the synaptic transmission can process not only basic AND/OR Boolean logic operations but also the linearly non-separable XOR function. Finally, we provide an overview of the evolutionary algorithm and discuss its benefits in automated neural circuit design for logic operations. Conclusions: This study provides a comprehensive perspective on the multiscale logic operations in the brain from both neural dynamics and neural coding viewpoints. It should thus be beneficial for understanding computational principles of the brain and may help design biologically plausible neuron models for AI devices.

Beamforming Using Exact Evaluation of Leakage and Ergodic Capacity of MU-MIMO System

Closed-form evaluation of key performance indicators (KPIs) of telecommunication networks help perform mathematical analysis under several network configurations. This paper deals with a recent mathematical approach of indefinite quadratic forms to propose simple albeit exact closed-form expressions of the expectation of two significant logarithmic functions. These functions formulate KPIs which include the ergodic capacity and leakage rate of multi-user multiple-input multiple-output (MU-MIMO) systems in Rayleigh fading channels. Our closed-form expressions are generic in nature and they characterize several network configurations under statistical channel state information availability. As a demonstrative example of the proposed characterization, the derived expressions are used in the statistical transmit beamformer design in a broadcast MU-MIMO system to portray promising diversity gains using standalone or joint maximization techniques of the ergodic capacity and leakage rate. The results presented are validated by Monte Carlo simulations.

The tyrosine phosphatase SHP2 increases robustness and information transfer within IL-6-induced JAK/STAT signalling

Background

Cell-to-cell heterogeneity is an inherent feature of multicellular organisms and is central in all physiological and pathophysiological processes including cellular signal transduction. The cytokine IL-6 is an essential mediator of pro- and anti-inflammatory processes. Dysregulated IL-6-induced intracellular JAK/STAT signalling is associated with severe inflammatory and proliferative diseases. Under physiological conditions JAK/STAT signalling is rigorously controlled and timely orchestrated by regulatory mechanisms such as expression of the feedback-inhibitor SOCS3 and activation of the protein-tyrosine phosphatase SHP2 (PTPN11). Interestingly, the function of negative regulators seems not to be restricted to controlling the strength and timely orchestration of IL-6-induced STAT3 activation. Exemplarily, SOCS3 increases robustness of late IL-6-induced STAT3 activation against heterogenous STAT3 expression and reduces the amount of information transferred through JAK/STAT signalling.

Methods

Here we use multiplexed single-cell analyses and information theoretic approaches to clarify whether also SHP2 contributes to robustness of STAT3 activation and whether SHP2 affects the amount of information transferred through IL-6-induced JAK/STAT signalling.

Results

SHP2 increases robustness of both basal, cytokine-independent STAT3 activation and early IL-6-induced STAT3 activation against differential STAT3 expression. However, SHP2 does not affect robustness of late IL-6-induced STAT3 activation. In contrast to SOCS3, SHP2 increases the amount of information transferred through IL-6-induced JAK/STAT signalling, probably by reducing cytokine-independent STAT3 activation and thereby increasing sensitivity of the cells. These effects are independent of SHP2-dependent MAPK activation.

Conclusion

In summary, the results of this study extend our knowledge of the functions of SHP2 in IL-6-induced JAK/STAT signalling. SHP2 is not only a repressor of basal and cytokine-induced STAT3 activity, but also ensures robustness and transmission of information.

Plain English summary Cells within a multicellular organism communicate with each other to exchange information about the environment. Communication between cells is facilitated by soluble molecules that transmit information from one cell to the other. Cytokines such as interleukin-6 are important soluble mediators that are secreted when an organism is faced with infections or inflammation. Secreted cytokines bind to receptors within the membrane of their target cells. This binding induces activation of an intracellular cascade of reactions called signal transduction, which leads to cellular responses. An important example of intracellular signal transduction is JAK/STAT signalling. In healthy organisms signalling is controlled and timed by regulatory mechanisms, whose activation results in a controlled shutdown of signalling pathways. Interestingly, not all cells within an organism are identical. They differ in the amount of proteins involved in signal transduction, such as STAT3. These differences shape cellular communication and responses to intracellular signalling. Here, we show that an important negative regulatory protein called SHP2 (or PTPN11) is not only responsible for shutting down signalling, but also for steering signalling in heterogeneous cell populations. SHP2 increases robustness of STAT3 activation against variable STAT3 amounts in individual cells. Additionally, it increases the amount of information transferred through JAK/STAT signalling by increasing the dynamic range of pathway activation in heterogeneous cell populations. This is an amazing new function of negative regulatory proteins that contributes to communication in heterogeneous multicellular organisms in health and disease.

Video Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12964-021-00770-7.

Toward an information theoretical description of communication in brain networks

Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different communication processes are taking place on top of a fixed structural topology: path processing score (PPS) estimates how much the brain signal has changed or has been transformed between any two brain regions (source and target); path broadcasting strength (PBS) estimates the propagation of the signal through edges adjacent to the path being assessed. We use PPS and PBS to explore communication dynamics in large-scale brain networks. We show that brain communication dynamics can be divided into three main "communication regimes" of information transfer: absent communication (no communication happening); relay communication (information is being transferred almost intact); and transducted communication (the information is being transformed). We use PBS to categorize brain regions based on the way they broadcast information. Subcortical regions are mainly direct broadcasters to multiple receivers; Temporal and frontal nodes mainly operate as broadcast relay brain stations; visual and somatomotor cortices act as multichannel transducted broadcasters. This work paves the way toward the field of brain network information theory by providing a principled methodology to explore communication dynamics in large-scale brain networks.

Perceptual uncertainty modulates auditory statistical learning: A magnetoencephalography study

Statistical learning allows comprehension of structured information, such as that in language and music. The brain computes a sequence's transition probability and predicts future states to minimise sensory reaction and derive entropy (uncertainty) from sequential information. Neurophysiological studies have revealed that early event-related neural responses (P1 and N1) reflect statistical learning - when the brain encodes transition probability in stimulus sequences, it predicts an upcoming stimulus with a high transition probability and suppresses the early event-related responses to a stimulus with a high transition probability. This amplitude difference between high and low transition probabilities reflects statistical learning effects. However, how a sequence's transition probability ratio affects neural responses contributing to statistical learning effects remains unknown. This study investigated how transition-probability ratios or conditional entropy (uncertainty) in auditory sequences modulate the early event-related neuromagnetic responses of P1m and N1m. Sequence uncertainties were manipulated using three different transition-probability ratios: 90:10%, 80:20%, and 67:33% (conditional entropy: 0.47, 0.72, and 0.92 bits, respectively). Neuromagnetic responses were recorded when participants listened to sequential sounds with these three transition probabilities. Amplitude differences between lower and higher probabilities were larger in sequences with transition-probability ratios of 90:10% and smaller in sequences with those of 67:33%, compared to sequences with those of 80:20%. This suggests that the transition-probability ratio finely tunes P1m and N1m. Our study also showed larger amplitude differences between frequent- and rare-transition stimuli in P1m than in N1m. This indicates that information about transition-probability differences may be calculated in earlier cognitive processes.