Entropy, or Information, Unifies Ecology and Evolution and Beyond

Principles of Molecular Evolution: Concepts from Non-equilibrium Thermodynamics for the Multilevel Theory of Learning

We present a non-equilibrium thermodynamics approach to the multilevel theory of learning for the study of molecular evolution. This approach allows us to study the explicit time dependence of molecular evolutionary processes and their impact on entropy production. Interpreting the mathematical expressions, we can show that two main contributions affect entropy production of molecular evolution processes which can be identified as mutation and gene transfer effects. Accordingly, our results show that the optimal adaptation of organisms to external conditions in the context of evolutionary processes is driven by principles of minimum entropy production. Such results can also be interpreted as the basis of some previous postulates of the theory of learning. Although our macroscopic approach requires certain simplifications, it allows us to interpret molecular evolutionary processes using thermodynamic descriptions with reference to well-known biological processes.

On Non-Random Mating, Adaptive Evolution, and Information Theory

Population genetics describes evolutionary processes, focusing on the variation within and between species and the forces shaping this diversity. Evolution reflects information accumulated in genomes, enhancing organisms' adaptation to their environment. In this paper, I propose a model that begins with the distribution of mating based on mutual fitness and progresses to viable adult genotype distribution. At each stage, the changes result in different measures of information. The evolutionary dynamics at each stage of the model correspond to certain aspects of interest, such as the type of mating, the distribution of genotypes in regard to mating, and the distribution of genotypes and haplotypes in the next generation. Changes to these distributions are caused by variations in fitness and result in Jeffrey's divergence values other than zero. As an example, a model of hybrid sterility is developed of a biallelic locus, comparing the information indices associated with each stage of the evolutionary process. In conclusion, the informational perspective seems to facilitate the connection between cause and effect and allows the development of statistical tests to perform hypothesis testing against zero-information null models (random mating, no selection, etc.). The informational perspective could contribute to clarify, deepen, and expand the mathematical foundations of evolutionary theory.

Potential Benefits and Challenges of Quantifying Pseudoreplication in Genomic Data with Entropy Statistics

Generating vast arrays of genetic markers for evolutionary ecology studies has become routine and cost-effective. However, analyzing data from large numbers of loci associated with a small number of finite chromosomes introduces a challenge: loci on the same chromosome do not assort independently, leading to pseudoreplication. Previous studies have demonstrated that pseudoreplication can substantially reduce precision of genetic analyses (and make confidence intervals wider), such as FST and linkage disequilibrium (LD) measures between pairs of loci. In LD analyses, another type of dependency (overlapping pairs of the same loci) also creates pseudoreplication. Building on previous work, we explore the potential of entropy metrics to improve the status quo, particularly total correlation (TC), to assess pseudoreplication in LD studies. Our simulations, performed on a monoecious population with a range of effective population sizes (Ne) and numbers of loci, attempted to isolate the overlapping-pairs-of-loci effect by considering unlinked loci and using entropy to quantify inter-locus relationships. We hypothesized a positive correlation between TC and the number of loci (L), and a negative correlation between TC and Ne. Results from our statistical models predicting TC demonstrate a strong effect of the number of loci, and muted effects of Ne and other predictors, adding support to the use of entropy-based metrics as a tool for estimating the statistical information of complex genetic datasets. Our results also highlight a challenge regarding scalability; computational limitations arise as the number of loci grows, making our current approach limited to smaller datasets. Despite these challenges, this work further refines our understanding of entropy measures, and offers insights into the complex dynamics of genetic information in evolutionary ecology research.

Unifying quantification methods for sexual selection and assortative mating using information theory

Sexual selection plays a crucial role in modern evolutionary theory, offering valuable insight into evolutionary patterns and species diversity. Recently, a comprehensive definition of sexual selection has been proposed, defining it as any selection that arises from fitness differences associated with nonrandom success in the competition for access to gametes for fertilization. Previous research on discrete traits demonstrated that non-random mating can be effectively quantified using Jeffreys (or symmetrized Kullback-Leibler) divergence, capturing information acquired through mating influenced by mutual mating propensities instead of random occurrences. This novel theoretical framework allows for detecting and assessing the strength of sexual selection and assortative mating. In this study, we aim to achieve two primary objectives. Firstly, we demonstrate the seamless alignment of the previous theoretical development, rooted in information theory and mutual mating propensity, with the aforementioned definition of sexual selection. Secondly, we extend the theory to encompass quantitative traits. Our findings reveal that sexual selection and assortative mating can be quantified effectively for quantitative traits by measuring the information gain relative to the random mating pattern. The connection of the information indices of sexual selection with the classical measures of sexual selection is established. Additionally, if mating traits are normally distributed, the measure capturing the underlying information of assortative mating is a function of the square of the correlation coefficient, taking values within the non-negative real number set [0, +∞). It is worth noting that the same divergence measure captures information acquired through mating for both discrete and quantitative traits. This is interesting as it provides a common context and can help simplify the study of sexual selection patterns.

Pan-Evo: The Evolution of Information and Biology's Part in This

Many people wonder whether biology, including humans, will benefit or experience harm from new developments in information such as artificial intelligence (AI). Here, it is proposed that biological and non-biological information might be components of a unified process, 'Panevolution' or 'Pan-Evo', based on four basic operations-innovation, transmission, adaptation, and movement. Pan-Evo contains many types of variable objects, from molecules to ecosystems. Biological innovation includes mutations and behavioural changes; non-biological innovation includes naturally occurring physical innovations and innovation in software. Replication is commonplace in and outside biology, including autocatalytic chemicals and autonomous software replication. Adaptation includes biological selection, autocatalytic chemicals, and 'evolutionary programming', which is used in AI. The extension of biological speciation to non-biological information creates a concept called 'Panspeciation'. Panevolution might benefit or harm biology, but the harm might be minimal if AI and humans behave intelligently because humans and the machines in which an AI resides might split into vastly different environments that suit them. That is a possible example of Panspeciation and would be the first speciation event involving humans for thousands of years. This event will not be particularly hostile to humans if humans learn to evaluate information and cooperate better to minimise both human stupidity and artificial simulated stupidity (ASS-a failure of AI).

Predicting the spread of SARS-CoV-2 variants: An artificial intelligence enabled early detection

During more than 3 years since its emergence, SARS-CoV-2 has shown great ability to mutate rapidly into diverse variants, some of which turned out to be very infectious and have spread throughout the world causing waves of infections. At this point, many countries have already experienced up to six waves of infections. Extensive academic work has focused on the development of models to predict the pandemic trajectory based on epidemiological data, but none has focused on predicting variant-specific spread. Moreover, important scientific literature analyzes the genetic evolution of SARS-CoV-2 variants and how it might functionally affect their infectivity. However, genetic attributes have not yet been incorporated into existing epidemiological modeling that aims to capture infection trajectory. Thus, this study leverages variant-specific genetic characteristics together with epidemiological information to systematically predict the future spread trajectory of newly detected variants. The study describes the analysis of 9.0 million SARS-CoV-2 genetic sequences in 30 countries and identifies temporal characteristic patterns of SARS-CoV-2 variants that caused significant infection waves. Using this descriptive analysis, a machine-learning-enabled risk assessment model has been developed to predict, as early as 1 week after their first detection, which variants are likely to constitute the new wave of infections in the following 3 months. The model's out-of-sample area under the curve (AUC) is 86.3% for predictions after 1 week and 90.8% for predictions after 2 weeks. The methodology described in this paper could contribute more broadly to the development of improved predictive models for variants of other infectious viruses.

Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, it was suggested that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories-fluctuations, rapid environmental responses, and long-term changes-converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

The safety assessment of tampons: illustration of a comprehensive approach for four different products

Introduction

We illustrate a comprehensive tampon safety assessment approach that assures products can be used safely. Material biocompatibility, vaginal mucosa assessment, vaginal microbiome evaluation, and in vitro assessment of potential risk of staphylococcal toxic shock syndrome expressed through growth of Staphylococcus aureus (S. aureus) and production of TSST-1 are the four essential portions of the approach. Post-marketing surveillance informs of possible health effects that warrant follow up. The approach meets or exceeds US and international regulatory guidance and is described through the example of four tampon products.

Methods/Results

Each product is comprised mostly of large molecular weight components (cotton, rayon, polymers) that cannot pass the vaginal mucosa, are widely used across the industry, and replete with a vast body of safety data and a long history of safe use in the category. Quantitative risk assessment of all small molecular weight components assured a sufficient margin of safety supporting their use. Vaginal mucosa assessment confirmed that pressure points, rough edges and/or sharp contact points were absent. A randomized cross-over clinical trial (ClinicalTrials.gov Identifier: NCT03478371) revealed favorable comfort ratings, and few complaints of irritation, burning, stinging, or discomfort upon insertion, wear, and removal. Adverse events were few, mild in severity, self-limited and resolved without treatment. Vaginal microbiota assessment in vitro presented no adverse effect on microbial growth. Culture-independent microbiome analyses from vaginal swab samples obtained during the clinical trial showed no differences attributable to tampon usage, but instead due to statistically significant subject-to-subject variability. Growth of S. aureus and TSST-1 toxin production in the presence of any of the four products in vitro were statistically significantly reduced when compared to medium control alone.

Discussion

The data from the four elements of the comprehensive safety assessment approach illustrated herein confirm that tampons evaluated using this system can be used safely for menstrual protection. A post-marketing surveillance system that monitors and responds to in-market experiences indicated in-use tolerability of the product among consumers, thus confirming the conclusions of the pre-marketing safety assessment.

Safety assessment scheme for menstrual cups and application for the evaluation of a menstrual cup comprised of medical grade silicone

BACKGROUND

Ensuring menstrual cup safety is paramount, yet a menstrual cup safety assessment scheme is lacking. This paper presents a quadripartite scheme, showing how it can be applied.

METHODS

The Tampax Menstrual Cup was evaluated in the safety assessment scheme: (1) Biocompatibility and chemical safety of cup constituents. Extractables were obtained under different use condition; exposure-based risk assessments (EBRA) were conducted for extractables exceeding thresholds of toxicological concern. (2) Physical impact to vaginal mucosa. After physical evaluations, the Tampax Cup and another cup were assessed in a randomised double-blinded, two-product, two-period cross-over clinical trial (65 women, mean age 34.2 years). (3) Impact to vaginal microbiota (in vitro mixed microflora assay and evaluation of vaginal swabs). (4) In vitro growth of Staphylococcus aureus and toxic shock syndrome toxin-1 (TSST-1) production.

FINDINGS

Biocompatibility assessments and EBRA of cup constituents showed no safety concerns. In the randomised clinical trial, all potentially product-related adverse effects were mild, vaginal exams were unremarkable, no clinically relevant pH changes occurred, post-void residual urine volume with and without cup were similar, and self-reported measures of comfort along with reports of burning, itching and stinging between cups were comparable. Cup use had no effect on microbial growth in vitro or in the 62 subjects who completed the trial or on in vitro TSST-1 production.

INTERPRETATION

The quadripartite safety assessment scheme allows evaluation of menstrual cup safety. The Tampax Cup is safe and well-tolerated upon intended use. As with all feminine hygiene products, post-market safety surveillance confirmed this conclusion.

FUNDING

By Procter & Gamble.

Bray-Curtis (AFD) differentiation in molecular ecology: Forecasting, an adjustment ( A A), and comparative performance in selection detection

Geographic genetic differentiation measures are used for purposes such as assessing genetic diversity and connectivity, and searching for signals of selection. Confirmation by unrelated measures can minimize false positives. A popular differentiation measure, Bray-Curtis, has been used increasingly in molecular ecology, renamed AFD (hereafter called BCAFD). Critically, BCAFD is expected to be partially independent of the commonly used Hill "Q-profile" measures. BCAFD needs scrutiny for potential biases, by examining limits on its value, and comparing simulations against expectations. BCAFD has two dependencies on within-population (alpha) variation, undesirable for a between-population (beta) measure. The first dependency is derived from similarity toG ST andF ST . The second dependency is that BCAFD cannot be larger than the highest allele proportion in either location (alpha variation), which can be overcome by data-filtering or by a modified statistic A A or "Adjusted AFD". The first dependency does not forestall applications such as assessing connectivity or selection, if we know the measure's null behavior under selective neutrality with specified conditions-which is shown in this article for A A, for equilibrium, and nonequilibrium, for the commonly used data type of single-nucleotide-polymorphisms (SNPs) in two locations. Thus, A A can be used in tandem with mathematically contrasting differentiation measures, with the aim of reducing false inferences. For detecting adaptive loci, the relative performance of A A and other measures was evaluated, showing that it is best to use two mathematically different measures simultaneously, and that A A is in one of the best such pairwise criteria. For any application, using A A, rather than BCAFD, avoids the counterintuitive limitation by maximum allele proportion within localities.

The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)

Scientific writings, as one essential part of human culture, have evolved over centuries into their current form. Knowing how scientific writings evolved is particularly helpful in understanding how trends in scientific culture developed. It also allows us to better understand how scientific culture was interwoven with human culture generally. The availability of massive digitized texts and the progress in computational technologies today provide us with a convenient and credible way to discern the evolutionary patterns in scientific writings by examining the diachronic linguistic changes. The linguistic changes in scientific writings reflect the genre shifts that took place with historical changes in science and scientific writings. This study investigates a general evolutionary linguistic pattern in scientific writings. It does so by merging two credible computational methods: relative entropy; word-embedding concreteness and imageability. It thus creates a novel quantitative methodology and applies this to the examination of diachronic changes in the Philosophical Transactions of Royal Society (PTRS, 1665–1869). The data from two computational approaches can be well mapped to support the argument that this journal followed the evolutionary trend of increasing professionalization and specialization. But it also shows that language use in this journal was greatly influenced by historical events and other socio-cultural factors. This study, as a “culturomic” approach, demonstrates that the linguistic evolutionary patterns in scientific discourse have been interrupted by external factors even though this scientific discourse would likely have cumulatively developed into a professional and specialized genre. The approaches proposed by this study can make a great contribution to full-text analysis in scientometrics.

Shannon diversity index: a call to replace the original Shannon's formula with unbiased estimator in the population genetics studies

BACKGROUND

The Shannon diversity index has been widely used in population genetics studies. Recently, it was proposed as a unifying measure of diversity at different levels-from genes and populations to whole species and ecosystems. The index, however, was proven to be negatively biased at small sample sizes. Modifications to the original Shannon's formula have been proposed to obtain an unbiased estimator.

METHODS

In this study, the performance of four different estimators of Shannon index-the original Shannon's formula and those of Zahl, Chao and Shen and Chao et al.-was tested on simulated microsatellite data. Both the simulation and analysis of the results were performed in the R language environment. A new R function was created for the calculation of all four indices from the genind data format.

RESULTS

Sample size dependence was detected in all the estimators analysed; however, the deviation from parametric values was substantially smaller in the derived measures than in the original Shannon's formula. Error rate was negatively associated with population heterozygosity. Comparisons among loci showed that fast-mutating loci were less affected by the error, except for the original Shannon's estimator which, in the smallest sample, was more strongly affected by loci with a higher number of alleles. The Zahl and Chao et al. estimators performed notably better than the original Shannon's formula.

CONCLUSION

The results of this study show that the original Shannon index should no longer be used as a measure of genetic diversity and should be replaced by Zahl's unbiased estimator.

The Introduction of Entropy and Information Methods to Ecology by Ramon Margalef

In ecology and evolution, entropic methods are now used widely and increasingly frequently. Their use can be traced back to Ramon Margalef's first attempt 70 years ago to use log-series to quantify ecological diversity, including searching for ecologically meaningful groupings within a large assemblage, which we now call the gamma level. The same year, Shannon and Weaver published a generally accessible form of Shannon's work on information theory, including the measure that we now call Shannon-Wiener entropy. Margalef seized on that measure and soon proposed that ecologists should use the Shannon-Weiner index to evaluate diversity, including assessing local (alpha) diversity and differentiation between localities (beta). He also discussed relating this measure to environmental variables and ecosystem processes such as succession. Over the subsequent decades, he enthusiastically expanded upon his initial suggestions. Finally, 2019 also would have been Margalef's 100th birthday.