Biosystems

Flow state, characterized by optimal engagement and performance, represents a key concept in understanding human performance and cognitive resource allocation. Grounded in Csikszentmihalyis and Sherrys flow theory and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP), this study investigated physiological and neural correlates of flow state during a simulated driving task under different music conditions and difficulty levels. Using a 2 x 3 factorial design with 20 participants, this study examined self-selected versus non-self-selected music across three difficulty levels, testing the relationship between task switching, cognitive resource allocation, and flow state. Physiological measures included heart rate and EEG (alpha/theta power) using a 4-channel Muse 2 headband, alongside a self-report measure of flow experience. Hierarchical linear modeling revealed significant physiological changes during self-selected music: heart rate decreased ({beta} = -5.15, p < .001), while alpha ({beta} = 5829.77, p < .001) and theta power ({beta} = 7637.24, p < .001) increased. Task difficulty also showed significant effects, with heart rate decreasing during hard ({beta} = -6.70, p < .001) and moderate ({beta} = -3.40, p = .001) conditions. In particular, while physiological measures showed robust changes, the self-reported flow state did not reach significance. Task switching rates showed significant decreases during self-selected music ({beta} = -0.86, p < .001) and hard difficulty ({beta} = -0.61, p < .001), supporting the LC4MP frameworks predictions regarding cognitive resource allocation. These findings demonstrate how task switching and cognitive resource allocation relate to flow state induction. The results highlight the importance of multimodal measurement approaches and demonstrate that personal relevance through music selection and task difficulty significantly influence physiological and neural responses during performance. Future research should employ more comprehensive measurement approaches to better capture the complexity of flow-related neural activity and its relationship to task switching and cognitive resource allocation.

An expectation of a hypothesis that proposes cell-to-cell signalling pathways are redundant due to the redundancy of pathway terminal transcription factors (TFs) was tested by screening 35 signalling ligands (SLs) for rescue of a decapentaplegic (dpp) hypomorphic wing growth phenotype. The screen identified three examples of partial rescue: Hedgehog (HH), Semphorin 1a (SEMA1A) and Wnt ortholog 2 (WNT2). HH overexpression with dppGAL4 may increase the expression of DPP activity from the hypomorphic dpp alleles. However, SEMA1A and WNT2 did not phenocopy ectopic expression of HH or DPP and neither SEMA1A nor WNT2 were required for wing growth suggesting substitution of DPP for partial restoration of wing growth. The WNT2 rescue was dependent on the Frizzled 4 (FZ4) WNT receptor excluding the possibility that WNT2 weakly binds the DPP receptor. Although examples of phenotypic nonspecificity of SL function were identified, this is an expectation, and not direct proof, of the hypothesis of TF redundancy. Screen Report SummaryAn expectation of a hypothesis proposing that cell-to-cell signalling pathways are redundant due to the redundancy of the pathway terminal transcription factors was tested by screening for replacement of one signalling ligand (DPP; SLa) with another SLb for wing growth. Three non-DPP SLs were identified in the screen of 35SLs: HH, SEMA1A and WNT2. Genetic analysis of Sema1a and Wnt2 suggests functional complementation of dpp for wing growth suggesting that SEMA1A and WNT2 partially replace DPP for wing growth. Therefore, an expectation of the hypothesis is met.

This study presents a proof-of-concept cyber-physical architecture integrating a SEIR epidemiological model (Susceptible-Exposed-Infectious-Recovered), implemented in MATLAB, with a simulated Internet of Things (IoT) acquisition and transmission stage based on the ESP32 microcontroller and the ThingSpeak platform. The system generates synthetic biomedical signals of body temperature and peripheral oxygen saturation (SpO2), structured across three levels: circadian variation, scheduled pathological episodes, and Gaussian noise. These signals feed a dual parametric coupling function that dynamically updates the SEIR transmission parameter as a combined function of body temperature and oxygen saturation deviations from their clinical reference values. The proposed architecture is organized into four functional phases: measurement, communication, computational processing, and feedback. Five simulated clinical scenarios were evaluated, ranging from normal conditions (T = 36.5 {degrees}C, SpO2 = 97%) to fever with severe hypoxia (T = 38.5 {degrees}C, SpO2 = 88%), yielding basic reproduction number (R0) values between 4.20 and 5.38, and peak infected proportions between 29.9% and 35.2% of the simulated population (N = 1,000). A sensitivity analysis on the coupling coefficients, with {+/-}50% variation from nominal values, showed that the oxygen saturation coefficient is the most influential parameter on R0 (range = 0.76) compared to the thermal coefficient (range = 0.42), with monotonic and predictable behavior across the entire evaluated parametric space. The primary contribution of this work is system integration: we propose a reproducible platform connecting biomedical simulation, IoT communication, and epidemiological modeling through parametric coupling in a controlled environment. All data used are entirely synthetic; a retrospective calibration with real Colombian data from the first epidemic wave of 2020 confirmed the epidemiological consistency of the model, with a calibrated R0 of 1.85 and a Pearson correlation of 0.930. Results should be interpreted as evidence of architectural feasibility, not as clinical or epidemiological validation. Author SummaryThe COVID-19 pandemic made it clear that epidemiological surveillance systems need tools that combine accessible technology with mathematical models capable of anticipating disease spread. In this work, we built a proof-of-concept platform connecting three elements: a low-cost electronic sensor based on the ESP32 microcontroller, a cloud communication platform (ThingSpeak), and a mathematical model that simulates how an epidemic spreads through a population. The sensor generates synthetic data on body temperature and oxygen saturation that, through a mathematical formula we designed, dynamically modify the rate of contagion in the model. We evaluated five clinical scenarios, ranging from normal conditions to fever with severe hypoxia, and analyzed how sensitive the results are to changes in the system parameters. We found that oxygen saturation has a greater influence on the estimated contagion potential than body temperature. Although all data are synthetic, this platform demonstrates that it is possible to integrate low-cost sensors with epidemiological models in real time, opening a viable pathway for early warning systems in resource-limited settings.

Whole genome duplication is a common mutation in eukaryotes with far-reaching phenotypic effects. The resulting morphological, physiological, and fitness consequences and how they affect the survival probability of newly polyploid lineages are intensively studied, but very little is known about the effect of genome doubling on the short-term evolvability of populations. Understanding the effect of polyploidization on the adaptive potential of populations is of crucial importance to predict the future of polyploid populations. In this paper, I investigate the immediate consequences of genome doubling on the genetic variance of populations. To do so, I performed numerical iterations and simulations of how the genetic variance of a quantitative trait changes after polyploidization, under different genetic architectures (additivity, dominance, and epistasis). I found that genetic variance generally decreases after genome doubling. Non-additive gene actions can make autotetraploid populations genetically more diverse than their diploid progenitors in rare cases, notably with overdominance and directional epistasis. By collecting estimates from the agronomic literature, I found that both dominance and epistatic variance contribute to the genetic variance of polyploid populations. These results bring new insights into the adaptive potential of newly formed tetraploid populations, and call for further experimental investigations of how polyploidization is associated with a short-term decrease in evolvability.

Fluorescent reporters cover a wide range of applications in both basic and applied research. Whether a study involves microscopic imaging to study (co)-localization of proteins, FRET, biosensing, or quantifying gene expression, fluorophores are attractive reporter candidates due to their relatively straightforward in vivo readout. For microbiological applications, a wide variety of fluorescent proteins with varying excitation and emission wavelengths, brightness levels, and maturation times are available. Careful consideration is required when selecting from this large suite of proteins, especially when choosing multiple fluorophores. This is further complicated in phototrophic organisms, which exhibit strong autofluorescence, especially towards the red part of the spectrum, effectively eliminating common candidates such as mCherry. In this study, the specific properties and performance of a selection of fluorescent proteins are systematically evaluated against the background of photosynthetic pigment-derived autofluorescence in the cyanobacterium Synechocystis sp. PCC 6803. Specific readouts of different combinations of fluorescent proteins are also analyzed using high-throughput methods, namely plate reader fluorescent scans and single-cell flow cytometry to quantify fluorescence. The ultimate goal is to assess each fluorescent protein with regard to: 1.) Its ability to be discerned from cyanobacterial autofluorescence. 2.) Its compatibility with other fluorophores in this context. 3.) Its overall suitability in cyanobacterial research. Several highly suitable fluorescent proteins for use in cyanobacteria are identified, including mTagBFP2, mNeonGreen and mScarlet-I and suitable combinations, covering nearly the whole spectrum of visible light. This study expands the knowledge and toolset for current and future researchers and uncovers a whole spectrum of possibilities for fluorescent protein selection in cyanobacterial cell biology.

Genetically identical cells grown in the same environment show variation in gene expression known as expression noise. Expression noise can be heritable and impact fitness, making it subject to natural selection. Increasing expression noise for the Saccharomyces cerevisiae TDH3 gene was shown to be beneficial in glucose-based media when mean TDH3 expression was far from the fitness optimum but deleterious when it was close to this optimum. Here, we show that growth on different carbon sources alters the effects of new mutations on TDH3 expression noise and examine the fitness effects of changing expression noise. In galactose-based media, we observed the same relationship between expression noise and fitness seen in glucose-based media, but in glycerol- and ethanol-based media, we observed the opposite relationship or no significant relationship, respectively. Using simulations of single-cell organisms, we found that these differences were most likely explained by environment-specific relationships between gene expression and fitness. We also found that, far from the optimum, the fitness effects of noise were greatest when expression was highly heritable between mother and daughter cells. The empirical observations and simulations reported in this study show how environments influence both the production of expression noise and its impacts on fitness.

When three cyanobacterial proteins--KaiA, KaiB, and KaiC--are incubated with ATP in vitro, the phosphorylation level of KaiC exhibits stable circadian oscillations. Biochemical and structural analyses have shown that KaiCs ATPase activity is crucial for these oscillations, leading to the hypothesis that ATP-consuming dynamics function as a molecular clock, determining the oscillation period of individual molecules. Moreover, these molecular clocks synchronize with one another, resulting in collective oscillations at the ensemble level. In this study, we develop a theoretical model to test this molecular clockwork hypothesis. Our model clarifies the relationship between the oscillation period and ATPase activity, explaining the significant changes in the period induced by amino-acid substitutions near the CI-CII domain boundary of the KaiC hexamer. Furthermore, the model addresses the physical basis for temperature compensation concerning both the oscillation period and ATPase activity. Thus, the molecular clockwork perspective provides a framework for understanding the atomic design behind collective oscillations.

Cell size in a proliferating cell population generally varies over a limited range ([~]2-4-fold). Within such populations, organelle content increases with cell size maintaining a relatively constant organelle density (amount per cell volume). However, cells of different types can differ greatly in cell size as well as in organelle composition. In such cases, it is often unclear to what degree, if any, the differences in organelle composition are due to the difference in cell size. In principle, this issue could be resolved by examining situations where a proliferating population of cells of the same cell type exhibit much greater size variation. Here we characterize how organelle content scales with cell volume in the polymorphic fungus, A. pullulans, whose proliferating cells span a [~]100-fold size range. We find that mitochondria and ER content increases in proportion to cell volume, while this is not the case for vacuoles and peroxisomes. Thus, organelle composition is affected by cell size in this system.

The prevalent transcriptional repressor NrdR binds to highly conserved prokaryotic sequences in the promoter regions of operons encoding the essential enzyme ribonucleotide reductase. The NrdR binding sites consist of two partially palindromic 16 bp sequences (NrdR boxes) separated by a 15-16 bp linker sequence. We have assessed the requirement of both boxes for binding, the propensity of different NrdRs to bind to heterologous binding sites, and that the linker sequence is only limited to length and not sequence conservation. As we have observed several deviations from the conserved sequences of the NrdR boxes, we here test the conservation requirements of individual basepairs in the NrdR boxes using a synthetic DNA fragment (Synt DNA) to which the NrdR proteins from the actinomycete Streptomyces coelicolor and the gammaproteobacterium Escherichia coli bind equally well as to their homologous binding sites. By introducing isolated mutations to Synt DNA and testing the binding capacity of NrdR from S. coelicolor and E. coli we expand our understanding of what criteria are needed to build a functional binding site for the NrdR repressor.

Cells communicate via extracellular ligands, such as hormones, which bind to plasma membrane receptors and trigger intracellular signaling cascades. G Protein-Coupled Receptors (GPCRs) exemplify this mechanism by initiating signaling both at the cell surface and, from intracellular compartments such as endosomes. The kinetics and spatial localization of these signals are critical determinants of cellular responses, yet receptor trafficking-including internalization, endosomal sorting, and recycling-remains a pivotal but often overlooked component of theoretical GPCR models. In this study, we present a mathematical framework that integrates receptor trafficking and signaling compartmentalization into generic GPCR dynamic models. Using a compartmentalized approach based on systems of ordinary differential equations (Chemical Reaction Networks), we analyze how receptor internalization and recycling modulate ligand-induced responses. Our results show that the balance between plasma membrane and endosomal signaling can significantly enhance or diminish ligand efficacy. Calibrated with high-throughput kinetic data, our model offers a refined tool for ligand pharmacological characterization and advances the understanding of GPCR signaling spatial organization.

Ribosome biogenesis is a highly coordinated pathway that involves the assembly of ribosomal RNAs (rRNAs) with ribosomal proteins (r-proteins) to generate functional ribosomal subunits (r-subunits). The Saccharomyces cerevisiae (yeast) large 60S r-subunit consists of three rRNA molecules and 46 r-proteins. The contributions of nearly all r-proteins of the yeast large r-subunit have been characterized; however, a few non-essential proteins remain poorly understood. Although non-essential, human eL22 has been identified as a key player in p53 regulation during ribosomal stress and as a highly mutated target in cancers. Despite this function, the role of eL22 in ribosome maturation is still ill-defined. In this study, we characterized yeast eL22 r-protein. Our results show that eL22 assembles into intermediate nucleolar pre-60S ribosomal particles. Loss of eL22 impairs cell growth and reduces 60S r-subunit accumulation, phenotypes that are exacerbated at low temperatures. Analysis of pre-rRNA processing by pulse-chase labeling, northern blot hybridization, and primer extension reveals a defect in 27S pre-rRNA maturation, specifically at the level of 27SB pre-rRNA processing. Consequently, nuclear export of eL22-deficient pre-60S particles is mildly impaired. Furthermore, we identify genetic interactions between eL22 and neighboring r-proteins, eL38 and eL31. We conclude that eL22 assembly is required for optimal pre-60S maturation during middle nucleolar stages, particularly at low temperatures, a function likely supported by the cooperative action of other r-proteins associated with common elements of 25S rRNA. HighlightsO_LIWe have studied the role of r-protein eL22 in yeast ribosome assembly. C_LIO_LIeL22 is required for 60S ribosomal subunit production. C_LIO_LIThe absence of eL22 is critical at low temperatures. C_LIO_LIeL22 is important for 27SB pre-rRNA processing and nuclear export of pre-ribosomes. C_LIO_LIeL22 functionally interacts with r-proteins eL38 and eL31 in domain III of 25S rRNA. C_LI

Heat tolerance determines the vitality of tree species under climate change independently of drought tolerance, but has been much less studied than tree water relations. We studied species-specific differences and the capacity for seasonal heat acclimation in Central Europes naturally most important tree species, Fagus sylvatica, in comparison with two exotic tree species (Fagus orientalis, Pseudotsuga menziesii) that are considered for silvicultural climate change adaptation in managed forests. Foliage of mature trees was incubated at temperatures from 35-50 {degrees}C for up to 4 h to simulate daily heat maxima during heat waves. The maximum quantum yield (Fv/Fm) of photosystem II (PS II) of dark-adapted leaves was measured, because the PS II is particularly sensitive to heat and its functionality can decide on plant survival under heat. Fagus sylvatica was much more tolerant to heat than Pseudotsuga menziesii, but weakly (albeit significantly) less tolerant than Fagus orientalis. Within its limits, Pseudotsuga menziesii showed high seasonal heat acclimation with constantly increasing tolerance during the growing season. Fagus orientalis, but practically not Fagus sylvatica, also acclimated to heat. This makes Fagus orientalis slightly superior over Fagus sylvatica in terms of heat tolerance, whereas the suitability of Pseudotsuga menziesii for silvicultural climate change adaptation is questionable. Strong heat acclimation, but also overall low heat tolerance, in Pseudotsuga menziesii might be the result of evergreenness, which requires the generation of both cold and heat tolerance during the year.

In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the cycle of theory development and refinement, assisting researchers in formulating the precise definitions that models require, and making accurate predictions. This manuscript introduces a computational implementation of Pavlovian learning models in a Python environment, termed Pavlovian Associative Learning Models Simulation (PALMS). In addition to the canonical Rescorla-Wagner model, attentional approaches are implemented, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a novel extension of the Rescorla-Wagner model featuring a unified variable learning rate that synthesises Mackintoshs and Pearce and Halls opposing conceptualisations. To our knowledge, only the first attentional model has been previously specified computationally in a general design tool. PALMS integrates a graphical interface that permits the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. It uniquely enables the simulation of experiments involving hundreds of stimuli, such as those used with human participants, and the computation of configural cues and configural-cue compounds across all models, thereby substantially broadening their predictive capabilities. A comprehensive description of the models implementation and the environment functionalities is provided in the paper; these include efficient and accurate operation and instant visualisation of predicted results across different models within a single architecture and environment. We evaluate PALMS by simulating five published experiments in the associative learning literature that assessed the predictive scope of existing models, and we show that this implementation provides neuroscientists with a useful tool for identifying critical variables, refining experimental designs, making precise predictions, comparing model fitness, and formulating new theoretical approaches. PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The environment source code and the latest multiplatform release build are accessible as a GitHub repository at https://github.com/cal-r/PALMS-Simulator. Author summaryResearch on associative learning is multidisciplinary, encompassing disciplines such as neuroscience, AI, psychology, psychiatry, behavioural sciences, planning, and marketing. Unlike static formalisms, precise computational definitions specify how a model operates, enabling model simulation, swift and error-free prediction calculations, which are essential for testing theories, comparing predictions, holding models accountable, and providing a common language across fields. We introduce Pavlovian Associative Learning Models Simulation (PALMS), a user-friendly, open-source Python environment for simulating classical conditioning and studying the role of attention in learning. PALMS implements the prescriptive Rescorla-Wagner and attentional models: Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a new hybrid model with a unified variable learning rate that blends Mackintosh and Pearce-Halls conflicting views. Its graphical interface makes it easy for neuroscientists to enter experiments. Our computational implementation supports simulations with hundreds of stimuli, configural cues, and compounds, broadening the models predictive power. Designed for efficiency, it offers instant visual results and useful features. We evaluate PALMS by simulating five published experiments, highlighting its value for model comparison and refinement, and, more generally, as a tool to assist research.

Predicting protein-protein interactions is a fundamental problem in molecular biology. Experimental approaches for identifying protein-protein interactions are time-consuming and labor-intensive, motivating the development of efficient computational alternatives, including machine learning-based methods. However, conventional machine learning methods often rely on manually engineered features that require substantial domain expertise. In this study, we propose a two-stage framework to address these limitations. In the first stage, a one-dimensional convolutional neural network autoencoder is used to automatically learn latent representations from protein sequences. The quality of these features is evaluated through reconstruction error, reflecting how accurately the model reconstructs the original sequence. In the second stage, these learned features are combined with amino acid frequency-based features to form a hybrid feature set for predicting protein-protein interactions. A systematic comparison is performed between models trained on frequency features alone and those using a hybrid representation. The comparison showed that incorporating one-dimensional convolutional neural network-derived latent features improved the models performance of predicting protein-protein interactions. The dataset was split into training, validation, and test sets. Nested cross-validation was employed, with inner loops for hyperparameter tuning and outer loops for model selection. The random forest classifier achieved the best performance, with a mean receiver operating characteristic-area under curve of 0.91 and a test F1-score of 0.87. These results highlight the effectiveness of integrating deep feature learning with ensemble methods for predicting protein-protein interactions and build upon previous work focused on this fundamental problem. Author SummaryProtein-protein interactions are fundamental in all biological processes. However, predicting these interactions is a key problem in molecular biology. Computational approaches have been tested to address this problem. We applied a mix of machine learning and deep learning to gain insight into the qualities of proteins that engage in interaction. First, we trained a deep learning model, which automatically learned the primary sequence and characters related thereto, reducing bias in the actual prediction process. We combined these features, or latent representations, with amino acid frequency features of protein sequences, and called the two together "hybrid features." Then we performed a systematic comparison of amino acid frequency features-only with hybrid features, among four different machine learning classifiers. Our results suggest that the random forest classifier performed best among all four classifiers at predicting interactions between proteins. We propose that this approach could be used to improve efficiency in testing protein-protein interactions at the bench and may have applications to other biologically relevant molecular interactions.

Perceptual systems in humans and many other animals are able to segment scenes into regions that are likely to be physically meaningful. This ability depends on having low-level mechanisms that can accurately categorize whether local image patches are samples from the same or different kinds of texture. We find that using spatial proximity as a proxy for same-different ground truth makes it possible to train accurate decision variables and bounds directly from arbitrary natural images with no feedback. We also find that performance can be further improved by using proximity as a ground truth for adjusting the final decision variables and bounds for the current image/scene. These surprising findings result from the simple fact that under a wide range of conditions proximity discrimination (near vs. far) and texture discrimination (same vs. different) have mathematically identical decision bounds if the same image features are used for both tasks. We used the decision variables and bounds trained on natural images as the initial steps in a hierarchical Bayesian observer (HBO) model of texture discrimination [9]. Given the relative simplicity of this HBO model, it did an excellent job of segmenting images having randomly shaped regions containing arbitrary natural textures. We suggest that the proximity proxy is something that natural selection could discover and exploit for any same-different task where the task-relevant stimulus features also vary systematically with distance in space and/or time. For example, natural selection could have created developmental learning/plasticity mechanisms that exploit the proximity proxy.

The standard set of 20 genetically-encoded amino acids (C20) exhibits a statistically non-random distribution in primarily two structurally-relevant physicochemical properties: hydrophobicity and molecular volume, and to a lesser extent charge. It remains an open question, however, whether evolutionary pressures similarly optimized the same alphabet for the distribution of functionally-relevant properties, such as reactivity. In this study, we used semi-empirical quantum chemistry simulations to calculate the highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO-LUMO) gaps for 84 xeno amino acids and constructed 10 million random 20-mer amino acid alphabets to determine where C20 fit amongst this background. The HOMO-LUMO gap measurements demonstrated that C20, similar to hydrophobicity and volume, also exhibits a non-random distribution. However, unlike hydrophobicity and volume, this distribution is non-random across an unevenly broad range. The results expand upon previous theory and suggest HOMO-LUMO gap energies as one synthetic biologists may consider when developing novel protein design tools or designing functional xeno amino acid alphabets. HighlightsO_LILifes amino acid alphabet is non-randomly distributed within an expanded computationally-generated chemistry space generated from large-scale quantum chemistry simulations. C_LIO_LIAmino acid alphabet coverage theory applies beyond structurally-relevant physicochemical descriptors to include functionally-relevant properties like reactivity as measured by frontier molecular orbitals C_LIO_LIFindings here provide a theoretical framework to guide the design of novel proteins and development of synthetic amino acid alphabets. C_LI

Metabolic networks are typically viewed as homeostatic systems that stabilize flux, energy charge, redox balance, and metabolite availability under perturbation. However, it remains unclear whether the same feedback architectures that support metabolic robustness can also generate learning-like, experience-dependent adaptation. Here, we develop a coarse-grained dynamical model of mammalian energy metabolism to test whether prior perturbation can improve future metabolic responses. The model represents core glucose, glutamine, fatty acid, and oxidative phosphorylation pathways as coupled ordinary differential equations with Michaelis-Menten-type fluxes, product-inhibition feedback, adaptive enzyme-capacity regulation, and explicit ATP costs for enzyme adjustment. Rather than aiming to reproduce quantitative fluxes for a specific cell type, the framework is designed to expose how metabolic feedback, regulatory cost, repeated perturbation, and environmental variability interact. We use this model to ask whether adaptive enzyme regulation enables improved recovery after repeated challenges, whether such effects depend on energetic control costs, and whether environmental variability broadens or constrains the set of reachable adaptive states. This approach provides a tractable way to investigate how homeostatic metabolic regulation may give rise to experience-dependent metabolic plasticity.

Nitrogen is essential for all life forms, and microorganisms prefer ammonium as a nitrogen source. Due to the low affinity of glutamine synthetase (GS) for ammonium, E. coli must maintain high intracellular ammonium (NH4+) concentrations to sustain its rapid growth. Under ammonium limitation, E. coli imports ammonium through the transporter AmtB and incorporates it into glutamine by using GS. On the basis of structural and mutagenesis information, mechanisms have been proposed for the transport of ammonia (NH3) and protons by AmtB through spatially (partly) separate routes. These mechanisms do not explain the required coupling between proton and ammonia transports. How does the membrane potential push the ammonia inward so as to attain high concentrations near GS? We here compare six candidate kinetic models of E. coli ammonium transport and assimilation in terms of how they reproduce experimental data from the literature: three variants of the electro-binding model in which the membrane potential affects AmtB-NH4+ binding, and three variants of the electro-flipping model in which it influences the conformational flip of the transporter. The computer simulations decide that the electro-binding models are 28 times more plausible than the electro-flipping models and suggest that the transmembrane electric potential affects AmtB-NH4+ binding from the cytoplasmic side. The addition of kinetic and thermodynamic features to existing structural information plus our requirement of an explanation of the coupling, suggest a new spatiotemporal mechanism of coupling of ammonia and proton flows in AmtB. Further simulations show that GS and AmtB regulation is coordinated via both the uridylyltransferase/uridylyl-removing enzyme (UTase) and 2-oxoglutarate binding, allowing the cell to minimize futile cycling while maintaining rapid growth. The free energy cost of transport-related futile cycling exceeded that of the GS reaction itself. Moreover, AmtB enabled robust growth under varying ammonium concentrations and pH levels, albeit at a cost of futile cycling that became substantial at low ammonium. These findings highlight the crucial roles of GS and AmtB in E. colis adaptations and provide new insights into the trade-off mechanism between nutrient acquisition and energy efficiency.

We study how confidence in perceptual decisions depends on whether it is communicated verbally (e.g., "very likely") or numerically (e.g., "80% certainty"). We find that verbal expressions more reliably distinguish correct from incorrect choices than numerical reports, challenging the common assumption that numerical probabilities provide more precise representations of uncertainty. Additionally, in a dyadic decision-making task in which participants can revise their initial reports based on a partners choice and expressed confidence, verbal and numerical reports are equally effective in supporting accurate revisions of initial judgments. Together, these results underscore the effectiveness of verbal expressions as a means of conveying decision confidence.

Understanding how anthropogenic disturbance and vegetation structure influence bird abundance is important for biodiversity conservation in rapidly changing tropical landscapes. This study evaluated the effects of anthropogenic and vegetation-related variables on the abundance of the Red-cheeked Cordon-bleu (Uraeginthus bengalus) in human settlements and surrounding farmlands in Laminga Village, Jos-East Local Government Area, Plateau State, Nigeria. Bird surveys were conducted using line transects and quadrat-based vegetation assessments during November 2024. Poisson Generalized Linear Models (GLMs) were used to examine the influence of anthropogenic and vegetation predictors on abundance. Among anthropogenic variables, building density significantly reduced abundance ({beta} = -0.141, SE = 0.060, z = -2.333, p = 0.020), whereas human presence ({beta} = -0.073, p = 0.141) and noise level ({beta} = 0.009, p = 0.592) did not significantly influence abundance. Average grass height showed a marginal positive relationship with abundance ({beta} = 2.008, SE = 1.051, z = 1.910, p = 0.056), while hedgerow presence, hedgerow height, grass cover, and bare ground cover were not significant predictors. The vegetation model produced the lowest residual deviance (91.19) and AIC value (297.66), indicating comparatively stronger explanatory performance. The results suggest that structural habitat characteristics and building density may play more important roles in shaping Red-cheeked Cordon-bleu abundance than human activity or noise levels alone. These findings provide insight into species responses to environmental disturbance in human-modified savanna ecosystems.