Degeneracy and complexity in biological systems

Beyond single targets: leveraging degeneracy in sodium channels for osteoarthritis analgesia

Commentary on: Shin SM, Itson-Zoske B, Xu H, Xiang H, Fan F, Hogan QH, Yu H. Sensory neuron-specific block of multifaceted sodium channels mitigates neuropathic pain behaviors of osteoarthritis. Pain Rep 2025. DOI: 10.1097/PR9.0000000000001288.

Dynamic switching circuit modulated by intramolecular conformation transition of DNA translator for versatile fluorescence biosensors

It might be intriguing and desirable to explore the stimuli-responsive modulation of dynamic switching circuit (DSC) for constructing versatile fluorescence biosensors via two-step sequential independent displacement reactions. Here, a switchable DNA translator (DT) encoding three functional modules is proposed to implement DSC for interpreting specific Key triggers (i.e. DNA segment, miRNA or small molecule) by activating intramolecular conformation transition. Upon presenting Key to regulate strand displacement, the Lock-blocked DT is liberated and self-folded into "active" on-state hairpin structure, so that two ended toeholds are oriented closely to execute proximal hybridization cooperatively for Key-responsive signal readout. Benefited from fast kinetics, efficient transduction, simplified operation and flexible programming, the Key-actuated DSC strategy achieved label-free assay of various target species by employing tunable Ag nanocluster as fluorescent reporter adjacent to or away from hemin/G-quadruplex complexes, which would be more potential and applicable in versatile fluorescence biosensors, identifiable cell imaging or customized tasks than typical strand displacements.

Nav1.8 and Chronic Pain: From Laboratory Animals to Clinical Patients

As a subtype of voltage-gated sodium channel and predominantly expressed in the sensory neurons located in the dorsal root ganglion (DRG), the Nav1.8 channel encoded by the SCN10A gene is found to have different variants in patients suffering chronic pain or insensitivity to pain due to the gain-of-function or loss-of-function of Nav1.8 channels. In animal models of chronic pain, Nav1.8 is also verified to be involved, suggesting that Nav1.8 may be a potential target for treatment of chronic pain. Another voltage-gated sodium channel, Nav1.7, is also proposed to be a target for chronic pain, supported by clinical findings in patients and laboratory animal models; however, there is no Nav1.7-specific drug that has passed clinical trials, although they demonstrated satisfactory effects in laboratory animals. This discrepancy between clinical and preclinical studies may be related to the differences between humans and laboratory animals or due to the degeneracy in different sodium channels governing the DRG neuronal excitability, which is thought of as the underlying machinery of chronic pain and mostly studied. This review summarizes recent findings of Nav1.8 in chronic pain from clinics and laboratories and discusses the difference, which may be helpful for future investigation of Nav1.8 in chronic pain, considering the dilemma of the Nav1.7 channel in chronic pain.

Toward Principles of Brain Network Organization and Function

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

The Theory of Constructed Emotion: More Than a Feeling

A recently published article by van Heijst et al. attempted to reconcile two research approaches in the science of emotion-basic emotion theory and the theory of constructed emotion-by suggesting that the former explains emotions as bioregulatory states of the body whereas the latter explains feelings that arise from those state changes. This bifurcation of emotion into objective physical states and subjective feelings involves three misleading simplifications that fundamentally misrepresent the theory of constructed emotion and prevent progress in the science of emotion. In this article we identify these misleading simplifications and the resulting factual errors, empirical oversights, and evolutionary oversimplifications. We then discuss why such errors will continue to arise until scientists realize that the two theories are intrinsically irreconcilable. They rest on incommensurate assumptions and require different methods of evaluation. Only by directly considering these differences will these research silos in the science of emotion finally dissolve, speeding the accumulation of trustworthy scientific knowledge about emotion that is usable in the real world.

Odorant representations indicate nonlinear processing across the olfactory system

The olfactory system comprises intricate networks of interconnected brain regions that process information across both the local and long-range circuits to extract odorant identity. Similar to pattern recognition in other sensory domains, such as the visual system, recognizing odorant identity likely depends on highly nonlinear interactions between these recurrently connected nodes. In this study, we investigate whether odorant identity can be distinguished through nonlinear interactions in the local field potentials of the olfactory bulb and telencephalic regions (the ventral nucleus of the ventral telencephalon and the dorsal posterior zone of the telencephalon) in anesthetized rainbow trout. Our results show that odorant identity modulates complex information-theoretic measures, specifically information sharing and redundancy across these brain areas, indicating nonlinear processing. In contrast, traditional linear connectivity measures, such as coherence and phase synchrony, showed little or no significant modulation by odorants. These findings suggest that nonlinear interactions encoded by olfactory oscillations carry crucial odor information across the teleost olfactory system, offering insights into the broader role of nonlinear dynamics in sensory processing.

Synergistic small worlds that drive technological sophistication

It is a well-known fact that economic growth goes hand in hand with improvements in technological sophistication. While critical to such sophistication, the nature and underlying structure of the input interactions taking place inside production processes remain opaque, at least in the study of large systems such as industries and entire economies. We develop a method to quantify the degree of input complementarity in production processes form input-output data. We propose that the information-theoretic concept of synergistic information is analog to economic complementarity and exploit this link to create a data-driven approach that does not require the ex ante assumption of production functions. In contrast to alternative empirical approaches, our method is able identify input-input interactions and to quantify their contribution to output, revealing an input-input synergistic interaction network that characterizes an industry's productive technology. We find that more sophisticated industries tend to exhibit highly modular small-world topologies; with the tertiary sector as its central connective core. Overall, countries and industries that have a well-established connective core and specialized modules exhibit higher economic complexity, higher output, and lower emissions. The proposed method provides a framework to identify key relationships in the economy that can enhance economic performance.

Neuroscience in pictures: Functional neurological disorder

Functional neurological disorder (FND) is a biopsychosocially-complex, prevalent, and potentially disabling neuropsychiatric condition. In this pictorial review, we explore the complexity of FND, from its diagnosis and conceptualization to current mechanistic understandings. We highlight advances in neuroimaging research that have revealed structural and functional brain alterations in FND and discuss a variety of factors that may serve as predisposing vulnerabilities for the development of this condition and/or perpetuate symptoms. This overview is designed as an initial teaching resource to educate trainees, clinicians, and researchers, highlighting core concepts in the literature on FND. Given that mechanistic research in FND is at a relatively early stage and is rapidly evolving, the interested reader should aim to continue updating their mechanistic understanding of FND as research further advances in this area.

SYSTEM XC- AS A MOLECULAR MECHANISM FOR EVOLUTIONARY NEW FORMS OF ADVANCED COGNITION

Human cognitive abilities are deeply rooted in evolutionary building blocks that maximize computation while maintaining efficiency. These abilities are not without evolutionary signatures; conserved processes like vision have undergone continual phylogenetic adjustments to better serve ecological niches. Conversely, more sophisticated forms of cognition may have required evolutionary innovations to transform existing neuronal processing to expand computational abilities. One such innovation is system xc- (Sxc), a cystine-glutamate antiporter predominantly localized to astrocytes that emerged in deuterostomes (e.g., vertebrates) after their divergence from protostomes over 550 million years ago. Previous evidence suggests that genetically modified rats that lack functional Sxc (MSxc) exhibit enhanced cocaine-seeking behavior. In this study, we deconstructed drug-seeking into its component behaviors, categorizing them as reliant on evolutionary conserved or newly evolved cognitive processes. Our results reveal that Sxc function is dispensable for conserved processes like visual, emotional, and hedonic processing, but critical for advanced, evolutionary new cognitive functions, particularly impulse control and decision making. Notably, we demonstrate a temporally specific reliance on Sxc during the learning phase of optimal decision-making, but not in maintaining established strategies. This is an important addition to our current understanding of astrocytes in non-homeostatic functions, indicating their critical role in computationally demanding phases of learning and memory. Unraveling evolutionary innovations like Sxc not only deepens our understanding of cognitive evolution but also paves the way for revolutionary, precision- targeted therapies in neuropsychiatric disorders, potentially transforming treatment paradigms and patient outcomes.

Using deep generative models for simultaneous representational and predictive modeling of brain and behavior: A graded unsupervised-to-supervised modeling framework

This paper uses a generative neural network architecture combining unsupervised (generative) and supervised (discriminative) models with a model comparison strategy to evaluate assumptions about the mappings between brain states and behavior. Most modeling in cognitive neuroscience publications assume a one-to-one brain-behavior relationship that is linear, but never test these assumptions or the consequences of violating them. We systematically varied these assumptions using simulations of four ground-truth brain-behavior mappings that involve progressively more complex relationships, ranging from one-to-one linear mappings to many-to-one nonlinear mappings. We then applied our Variational AutoEncoder-Classifier framework to the simulations to show how it accurately captured diverse brain-behavior mappings,provided evidence regarding which assumptions are supported by the data, and illustrated the problems that arise when assumptions are violated. This integrated approach offers a reliable foundation for cognitive neuroscience to effectively model complex neural and behavioral processes, allowing more justified conclusions about the nature of brain-behavior mappings.

Toward a functional future for the cognitive neuroscience of human aging

The cognitive neuroscience of human aging seeks to identify neural mechanisms behind the commonalities and individual differences in age-related behavioral changes. This goal has been pursued predominantly through structural or "task-free" resting-state functional neuroimaging. The former has elucidated the material foundations of behavioral decline, and the latter has provided key insight into how functional brain networks change with age. Crucially, however, neither is able to capture brain activity representing specific cognitive processes as they occur. In contrast, task-based functional imaging allows a direct probe into how aging affects real-time brain-behavior associations in any cognitive domain, from perception to higher-order cognition. Here, we outline why task-based functional neuroimaging must move center stage to better understand the neural bases of cognitive aging. In turn, we sketch a multi-modal, behavior-first research framework that is built upon cognitive experimentation and emphasizes the importance of theory and longitudinal design.

An ecological approach for skill development and performance in soccer goalkeeper training: Empirical evidence and coaching applications

Ecological approaches in sport consider that athletes adapt to properties of the task and the surrounding environment. Thus, task and environment are key constraints of performance. Yet, the influence of task and environmental constraints on athletes' performance needs empirical examination, especially in sport-specific contexts such as soccer goalkeeping. This study aimed to examine if and how task and environmental constraints influenced goalkeepers (GKs') performances. We monitored performance coefficients of two professional female GKs across 13 training tasks that varied based on 9 constraints, referring to both interactions among athletes and properties of the surrounding landscape. Results showed that constraints explain ~ 47% of the observed variability in GKs' performances. Numerical complexity (i.e., the potential interactions between athletes) showed a major influence on performance, which indicates that number of interactions among athletes may constrain GKs' perceived opportunities for action. Field dimensions and landscape representativity (including elements such as penalty area(s), target goal(s) and constraints for shooting) showed positive relationships with performance, supporting that training designs retaining closer proximity to the game may benefit GKs' performances. Overall, results supported that athlete-environment couplings could be understood as a multifactorial model and hence, a combination of task constraints are necessary for designing effective learning environments.

Computational complexity as a potential limitation on brain-behaviour mapping

Within the reductionist framework, researchers in the special sciences formulate key terms and concepts and try to explain them with lower-level science terms and concepts. For example, behavioural vision scientists describe contrast perception with a psychometric function, in which the perceived brightness increases logarithmically with the physical contrast of a light patch (the Weber-Fechner law). Visual neuroscientists describe the output of neural circuits with neurometric functions. Intuitively, the key terms from two adjacent scientific domains should map onto each other; for instance, psychometric and neurometric functions may map onto each other. Identifying such mappings has been the very goal of neuroscience for nearly two centuries. Yet mapping behaviour to brain measures has turned out to be difficult. Here, we provide various arguments as to why the conspicuous lack of robust brain-behaviour mappings is rather a rule than an exception. First, we provide an overview of methodological and conceptual issues that may stand in the way of successful brain-behaviour mapping. Second, extending previous theoretical work (Herzog, Doerig and Sachse, 2023), we show that brain-behaviour mapping may be limited by complexity barriers. In this case, reduction may be impossible.

Integration of partially observed multimodal and multiscale neural signals for estimating a neural circuit using dynamic causal modeling

Integrating multiscale, multimodal neuroimaging data is essential for a comprehensive understanding of neural circuits. However, this is challenging due to the inherent trade-offs between spatial coverage and resolution in each modality, necessitating a computational strategy that combines modality-specific information effectively. This study introduces a dynamic causal modeling (DCM) framework designed to address the challenge of combining partially observed, multiscale signals across a larger-scale neural circuit by employing a shared neural state model with modality-specific observation models. The proposed method achieves robust circuit inference by iteratively integrating parameter estimates from local microscale and global meso- or macroscale circuits, derived from signals across various scales and modalities. Parameters estimated from high-resolution data within specific regions inform global circuit estimation by constraining neural properties in unobserved regions, while large-scale circuit data help elucidate detailed local circuitry. Using a virtual ground truth system, we validated the method across diverse experimental settings, combining calcium imaging (CaI), voltage-sensitive dye imaging (VSDI), and blood-oxygen-level-dependent (BOLD) signals-each with distinct coverage and resolution. Our reciprocal and iterative parameter estimation approach markedly improves the accuracy of neural property and connectivity estimates compared to traditional one-step estimation methods. This iterative integration of local and global parameters presents a reliable approach to inferring extensive, complex neural circuits from partially observed, multimodal, and multiscale data, showcasing how information from different scales reciprocally enhances entire circuit parameter estimation.

Is artificial consciousness achievable? Lessons from the human brain

We here analyse the question of developing artificial consciousness from an evolutionary perspective, taking the evolution of the human brain and its relation with consciousness as a reference model or as a benchmark. This kind of analysis reveals several structural and functional features of the human brain that appear to be key for reaching human-like complex conscious experience and that current research on Artificial Intelligence (AI) should take into account in its attempt to develop systems capable of human-like conscious processing. We argue that, even if AI is limited in its ability to emulate human consciousness for both intrinsic (i.e., structural and architectural) and extrinsic (i.e., related to the current stage of scientific and technological knowledge) reasons, taking inspiration from those characteristics of the brain that make human-like conscious processing possible and/or modulate it, is a potentially promising strategy towards developing conscious AI. Also, it cannot be theoretically excluded that AI research can develop partial or potentially alternative forms of consciousness that are qualitatively different from the human form, and that may be either more or less sophisticated depending on the perspectives. Therefore, we recommend neuroscience-inspired caution in talking about artificial consciousness: since the use of the same word "consciousness" for humans and AI becomes ambiguous and potentially misleading, we propose to clearly specify which level and/or type of consciousness AI research aims to develop, as well as what would be common versus differ in AI conscious processing compared to human conscious experience.

Neural Predictors of Fear Depend on the Situation

The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

Multicellular adaptation to electrophysiological perturbations analyzed by deterministic and stochastic bioelectrical models

Cells can compensate a disruptive change in one ion channel by compensatory changes in other channels. We have simulated the adaptation of a multicellular aggregate of non-excitable cells to the electrophysiological perturbation produced by the external blocking of a cation channel. In the biophysical model employed, we consider that this blocking provokes a cell depolarization that opens a voltage-gated calcium channel, thus allowing toxic Ca2+ levels. The cell adaptation to this externally-induced perturbation is ascribed to the multiplicity of channels available to keep the cell membrane potential within a physiological window. We propose that the cell depolarization provokes the upregulated expression of a compensatory channel protein that resets the cell potential to the correct polarized value, which prevents the calcium entry. To this end, we use two different simulation algorithms based on deterministic and stochastic methods. The simulations suggest that because of the local correlations coupling the cell potential to transcription, short-term bioelectrical perturbations can trigger long-term biochemical adaptations to novel stressors in multicellular aggregates. Previous experimental data on planarian flatworms' adaptation to a barium-containing environment is also discussed.

Comparative Physiology and Morphology of BLA-Projecting NBM/SI Cholinergic Neurons in Mouse and Macaque

Cholinergic projection neurons of the nucleus basalis and substantia innominata (NBM/SI) densely innervate the basolateral amygdala (BLA) and have been shown to contribute to the encoding of fundamental and life-threatening experiences. Given the vital importance of these circuits in the acquisition and retention of memories that are essential for survival in a changing environment, it is not surprising that the basic anatomical organization of the NBM/SI is well conserved across animal classes as diverse as teleost and mammal. What is not known is the extent to which the physiology and morphology of NBM/SI neurons have also been conserved. To address this issue, we made patch-clamp recordings from NBM/SI neurons in ex vivo slices of two widely divergent mammalian species, mouse and rhesus macaque, focusing our efforts on cholinergic neurons that project to the BLA. We then reconstructed most of these recorded neurons post hoc to characterize neuronal morphology. We found that rhesus macaque BLA-projecting cholinergic neurons were both more intrinsically excitable and less morphologically compact than their mouse homologs. Combining measurements of 18 physiological features and 13 morphological features, we illustrate the extent of the separation. Although macaque and mouse neurons both exhibited considerable within-group diversity and overlapped with each other on multiple individual metrics, a combined morphoelectric analysis demonstrates that they form two distinct neuronal classes. Given the shared purpose of the circuits in which these neurons participate, this finding raises questions about (and offers constraints on) how these distinct classes result in similar behavior.

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75-90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

Enhanced Extraction of Activation Time and Contractility From Myocardial Strain Data Using Parameter Space Features and Computational Simulations

A computational model enables the extraction of two critical myocardial tissue properties: activation time (AT) and contractility (Con) from recorded cardiac strains. However, interference between these parameters reduces the precision and accuracy of the extraction process. This study investigates whether leveraging features in the parameter space can enhance parameter extraction. We utilized a computational model to simulate sarcomere mechanics, creating a parameter space grid of 41 × 41 AT and Con pairs. Each pair generated a simulated strain pattern, and by scanning the grid, we identified cohorts of similar strain patterns for each simulation. These cohorts were represented as binary images-synthetic fingerprints-where the position and shape of each blob indicated extraction uniqueness. We also generated a measurement fingerprint for a strain pattern from a patient with left bundle branch block and compared it to the synthetic fingerprints to calculate a proximity map based on their similarity. This approach allowed us to extract AT and Con using both the measurement fingerprint and the proximity map, corresponding to simple optimization and enhanced parameter extraction methods, respectively. Each synthetic fingerprint consisted of a single connected blob whose size and shape varied characteristically within the parameter space. The AT values extracted from the measurement fingerprint and the proximity map ranged from -59 to 19 ms and from -16 to 14 ms, respectively, while Con values ranged from 48% to 110% and from 85% to 110%, respectively. This study demonstrates that similarity in simulations leads to an asymmetric distribution of parameter values in the parameter space. By using a proximity map, this distortion is considered, significantly improving the accuracy of parameter extraction.

The Brain's Best Kept Secret Is Its Degenerate Structure

Degeneracy is defined as multiple sets of solutions that can produce very similar system performance. Degeneracy is seen across phylogenetic scales, in all kinds of organisms. In neuroscience, degeneracy can be seen in the constellation of biophysical properties that produce a neuron's characteristic intrinsic properties and/or the constellation of mechanisms that determine circuit outputs or behavior. Here, we present examples of degeneracy at multiple levels of organization, from single-cell behavior, small circuits, large circuits, and, in cognition, drawing conclusions from work ranging from bacteria to human cognition. Degeneracy allows the individual-to-individual variability within a population that creates potential for evolution.

Safe(r) Landing by Older People: A Matter of Complexity

Maintaining balance is a complex motor problem that requires coordinated contributions from multiple biological systems. Aging inevitably lessens the fidelity of biological systems, which can result in an increased risk of falling and associated injuries. It is advantageous to land safely, but falls manifest in diverse ways, so different motor solutions are required to land safely. However, without considerable practice, it is difficult to recall the appropriate motor solution for a fall and then apply it effectively in the brief duration before hitting the ground. A complex systems perspective provides a lens through which to view the problem of safe(r) landing. It may be possible to use motor analogies to promote degeneracy within the perceptual motor system so that, regardless of the direction in which an older person falls, their body self-organizes to land with less likelihood of injury.

Reminiscences on the honeybee genome project and the rise of epigenetic concepts in insect science

The sequencing of the honeybee genome in 2006 was an important technological and logistic achievement experience. But what benefits have flown from the honeybee genome project? What does the annotated genomic assembly mean for the study of behavioural complexity and organismal function in honeybees? Here, I discuss several lines of research that have arisen from this project and highlight the rapidly expanding studies on insect epigenomics, emergent properties of royal jelly, the mechanism of nutritional control of development and the contribution of epigenomic regulation to the evolution of sociality. I also argue that the term 'insect epigenetics' needs to be carefully redefined to reflect the diversity of epigenomic toolkits in insects and the impact of lineage-specific innovations on organismal outcomes. The honeybee genome project helped pioneer advances in social insect molecular biology, and fuelled breakthrough research into the role of flexible epigenomic control systems in linking genotype to phenotype.

Psychopathy and medial frontal cortex: A systematic review reveals predominantly null relationships

Theories have posited that psychopathy is caused by dysfunction in the medial frontal cortex, including ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and dorsomedial prefrontal cortex (dmPFC). Recent reviews have questioned the reproducibility of neuroimaging findings within this field. We conducted a systematic review to describe the consistency of magnetic resonance imaging (MRI) findings according to anatomical subregion (vmPFC, ACC, dmPFC), experimental task, psychopathy assessment, study power, and peak coordinates of significant effects. Searches of PsycInfo and MEDLINE databases produced 77 functional and 24 structural MRI studies that analyzed the medial frontal cortex in relation to psychopathy in adult samples. Findings were predominantly null (85.4 % of 1573 tests across the three medial frontal regions). Studies with higher power observed null effects at marginally lower rates. Finally, peak coordinates of significant effects were widely dispersed. The evidence failed to support theories positing the medial frontal cortex as a consistent neural correlate of psychopathy. Theory and methods in the field should be revised to account for predominantly null neuroimaging findings.

Two New Bio-Inspired Particle Swarm Optimisation Algorithms for Single-Objective Continuous Variable Problems Based on Eavesdropping and Altruistic Animal Behaviours

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants, namely biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour, which allows for the formation of lending-borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity, which contributes to the prevention of premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC'13, CEC'14 and CEC'17 test suites and various constrained real-world optimisation problems, as well as against 13 well-known PSO variants, the CEC competition winner, differential evolution algorithm L-SHADE and the recent bio-inspired I-CPA metaheuristic. The experimental results show that both the BEPSO and AHPSO algorithms provide very competitive performance on the unconstrained test suites and the constrained real-world problems. On the CEC13 test suite, across all dimensions, both BEPSO and AHPSO performed statistically significantly better than 10 of the 15 comparator algorithms, while none of the remaining 5 algorithms performed significantly better than either BEPSO or AHPSO. On the CEC17 test suite, on the 50D and 100D problems, both BEPSO and AHPSO performed statistically significantly better than 11 of the 15 comparator algorithms, while none of the remaining 4 algorithms performed significantly better than either BEPSO or AHPSO. On the constrained problem set, in terms of mean rank across 30 runs on all problems, BEPSO was first, and AHPSO was third.

Comparative analysis of codon usage bias in the chloroplast genomes of eighteen Ampelopsideae species (Vitaceae)

BACKGROUND

The tribe Ampelopsideae plants are important garden plants with both medicinal and ornamental values. The study of codon usage bias (CUB) facilitates a deeper comprehension of the molecular genetic evolution of species and their adaptive strategies. The joint analysis of CUB in chloroplast genomes (cpDNA) offers valuable insights for in-depth research on molecular genetic evolution, biological resource conservation, and elite breeding within this plant family.

RESULTS

The base composition and codon usage preferences of the eighteen chloroplast genomes were highly similar, with the GC content of bases at all positions of their codons being less than 50%. This indicates that they preferred A/T bases. Their effective codon numbers were all in the range of 35-61, which indicates that the codon preferences of the chloroplast genomes of the 18 Ampelopsideae plants were relatively weak. A series of analyses indicated that the codon preference of the chloroplast genomes of the 18 Ampelopsideae plants was influenced by a combination of multiple factors, with natural selection being the primary influence. The clustering tree generated based on the relative usage of synonymous codons is consistent with some of the results obtained from the phylogenetic tree of chloroplast genomes, which indicates that the clustering tree based on the relative usage of synonymous codons can be an important supplement to the results of the sequence-based phylogenetic analysis. Eventually, 10 shared best codons were screened on the basis of the chloroplast genomes of 18 species.

CONCLUSION

The codon preferences of the chloroplast genome in Ampelopsideae plants are relatively weak and are primarily influenced by natural selection. The codon composition of the chloroplast genomes of the eighteen Ampelopsideae plants and their usage preferences were sufficiently similar to demonstrate that the chloroplast genomes of Ampelopsideae plants are highly conserved. This study provides a scientific basis for the genetic evolution of chloroplast genes in Ampelopsideae species and their suitable strategies.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

The effect of experience on the perception of affordances for aperture crossing in cycling

To ride successfully and safely, cyclists must perceive and act on the affordances that are available in a given situation. This study investigated whether experience in perceiving and acting with respect to a person-plus-object system would influence whether and how a person choses to cross an aperture of different widths, especially in relation to their maximal action capabilities. We also explore whether the distribution of action modes reflects this effect. We examined the performance (i.e., the probability of successfully crossing the aperture) and the decision (i.e., the probability of attempting to cross the aperture) of 8 experienced cyclists and 16 occasional cyclists in an aperture crossing task. In term of performance, experienced cyclists demonstrated greater ability to cross narrower apertures than occasional cyclists, but there were no such differences when aperture width was scaled to maximal action capabilities. In term of decision, both experienced and occasional cyclists tended to over-estimate their abilities, but the experienced cyclists did so to a greater extent. Our findings indicate that experience improves the ability to perform more complex tasks due to utilizing a wider repertoire of actions, but not necessarily the ability to perceive and actualize (action-scaled) affordances.

Publication guidelines for human heart rate and heart rate variability studies in psychophysiology-Part 1: Physiological underpinnings and foundations of measurement

This Committee Report provides methodological, interpretive, and reporting guidance for researchers who use measures of heart rate (HR) and heart rate variability (HRV) in psychophysiological research. We provide brief summaries of best practices in measuring HR and HRV via electrocardiographic and photoplethysmographic signals in laboratory, field (ambulatory), and brain-imaging contexts to address research questions incorporating measures of HR and HRV. The Report emphasizes evidence for the strengths and weaknesses of different recording and derivation methods for measures of HR and HRV. Along with this guidance, the Report reviews what is known about the origin of the heartbeat and its neural control, including factors that produce and influence HRV metrics. The Report concludes with checklists to guide authors in study design and analysis considerations, as well as guidance on the reporting of key methodological details and characteristics of the samples under study. It is expected that rigorous and transparent recording and reporting of HR and HRV measures will strengthen inferences across the many applications of these metrics in psychophysiology. The prior Committee Reports on HR and HRV are several decades old. Since their appearance, technologies for human cardiac and vascular monitoring in laboratory and daily life (i.e., ambulatory) contexts have greatly expanded. This Committee Report was prepared for the Society for Psychophysiological Research to provide updated methodological and interpretive guidance, as well as to summarize best practices for reporting HR and HRV studies in humans.

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Complex systems are neither fully determined nor completely random. Biological complex systems, including single neurons, manifest intermediate regimes of randomness that recruit integration of specific combinations of functionally specialized subsystems. Such emergence of biological function provides the substrate for the expression of degeneracy, the ability of disparate combinations of subsystems to yield similar function. Here, we present evidence for the expression of degeneracy in morphologically realistic models of dentate gyrus granule cells (GCs) through functional integration of disparate ion-channel combinations. We performed a 45-parameter randomized search spanning 16 active and passive ion channels, each biophysically constrained by their gating kinetics and localization profiles, to search for valid GC models. Valid models were those that satisfied 17 sub- and suprathreshold cellular-scale electrophysiological measurements from rat GCs. A vast majority (>99%) of the 15,000 random models were not electrophysiologically valid, demonstrating that arbitrarily random ion-channel combinations would not yield GC functions. The 141 valid models (0.94% of 15,000) manifested heterogeneities in and cross-dependencies across local and propagating electrophysiological measurements, which matched with their respective biological counterparts. Importantly, these valid models were widespread throughout the parametric space and manifested weak cross-dependencies across different parameters. These observations together showed that GC physiology could neither be obtained by entirely random ion-channel combinations nor is there an entirely determined single parametric combination that satisfied all constraints. The complexity, the heterogeneities in measurement and parametric spaces, and degeneracy associated with GC physiology should be rigorously accounted for while assessing GCs and their robustness under physiological and pathological conditions.NEW & NOTEWORTHY A recent study from our laboratory had demonstrated pronounced heterogeneities in a set of 17 electrophysiological measurements obtained from a large population of rat hippocampal granule cells. Here, we demonstrate the manifestation of ion-channel degeneracy in a heterogeneous population of morphologically realistic conductance-based granule cell models that were validated against these measurements and their cross-dependencies. Our analyses show that single neurons are complex entities whose functions emerge through intricate interactions among several functionally specialized subsystems.

START: A Versatile Platform for Bacterial Ligand Sensing with Programmable Performances

Recognition of signaling molecules for coordinated regulation of target genes is a fundamental process for biological systems. Cells often rely on transcription factors to accomplish these intricate tasks, yet the subtle conformational changes of protein structures, coupled with the complexity of intertwined protein interaction networks, pose challenges for repurposing these for bioengineering applications. This study introduces a novel platform for ligand-responsive gene regulation, termed START (Synthetic Trans-Acting Riboswitch with Triggering RNA). Inspired by the bacterial ligand sensing system, riboswitch, and the synthetic gene regulator, toehold switch, the START platform enables the implementation of synthetic biosensors for various ligands. Rational sequence design with targeted domain optimization yields high-performance STARTs with a dynamic range up to 67.29-fold and a tunable ligand sensitivity, providing a simple and intuitive strategy for sensor engineering. The START platform also exhibits modularity and composability to allow flexible genetic circuit construction, enabling seamless implementation of OR, AND, and NOT Boolean logic gates for multiple ligand inputs. The START design principle is capable of broadening the suite of synthetic biosensors for diverse chemical and protein ligands, providing a novel riboregulator chassis for synthetic biology and bioengineering applications.

Designing intelligent bioorthogonal nanozymes: Recent advances of stimuli-responsive catalytic systems for biomedical applications

Bioorthogonal nanozymes have emerged as a potent tool in biomedicine due to their unique ability to perform enzymatic reactions that do not interfere with native biochemical processes. The integration of stimuli-responsive mechanisms into these nanozymes has further expanded their potential, allowing for controlled activation and targeted delivery. As such, intelligent bioorthogonal nanozymes have received more and more attention in developing therapeutic approaches. This review provides a comprehensive overview of the recent advances in the development and application of stimuli-responsive bioorthogonal nanozymes. By summarizing the design outlines for anchoring bioorthogonal nanozymes with stimuli-responsive capability, this review seeks to offer valuable insights and guidance for the rational design of these remarkable materials. This review highlights the significant progress made in this exciting field with different types of stimuli and the various applications. Additionally, it also examines the current challenges and limitations in the design, synthesis, and application of these systems, and proposes potential solutions and research directions. This review aims to stimulate further research toward the development of more efficient and versatile stimuli-responsive bioorthogonal nanozymes for biomedical applications.

Comparative physiology and morphology of BLA-projecting NBM/SI cholinergic neurons in mouse and macaque

Cholinergic projection neurons of the nucleus basalis and substantia innominata (NBM/SI) densely innervate the basolateral amygdala (BLA) and have been shown to contribute to the encoding of fundamental and life-threatening experiences. Given the vital importance of these circuits in the acquisition and retention of memories that are essential for survival in a changing environment, it is not surprising that the basic anatomical organization of the NBM/SI is well conserved across animal classes as diverse as teleost and mammal. What is not known is the extent to which the physiology and morphology of NBM/SI neurons have also been conserved. To address this issue, we made patch-clamp recordings from NBM/SI neurons in ex vivo slices of two widely divergent mammalian species, mouse and rhesus macaque, focusing our efforts on cholinergic neurons that project to the BLA. We then reconstructed most of these recorded neurons post hoc to characterize neuronal morphology. We found that rhesus macaque BLA-projecting cholinergic neurons were both more intrinsically excitable and less morphologically compact than their mouse homologs. Combining measurements of 18 physiological features and 13 morphological features, we illustrate the extent of the separation. Although macaque and mouse neurons both exhibited considerable within-group diversity and overlapped with each other on multiple individual metrics, a combined morpho-electric analysis demonstrates that they form two distinct neuronal classes. Given the shared purpose of the circuits in which these neurons participate, this finding raises questions about (and offers constraints on) how these distinct classes result in similar behavior.

Levels of Airborne Sound And Substrate-borne Vibration Calling Are Negatively Related Across Neotropical False-leaf Katydids

Animals often signal in multiple sensory modalities to attract mates, but the level of signaling investment in each modality can differ dramatically between individuals and across species. When functionally overlapping signals are produced in different modalities, their relative use can be influenced by many factors, including differences in signal active space, energetic costs, and predation risk. Characterizing differences in total signal investment across time can shed light on these factors, but requires long focal recordings of signal production. Neotropical pseudophylline katydids produce mate advertisement signals as airborne sound and substrate-borne vibration. Airborne calls, produced via stridulation, are extremely short, high-frequency, and longer-range signals. Conversely, substrate-borne calls produced via abdominal tremulation are longer, low-frequency, relatively more energetically costly, and shorter-range signals. To examine patterns of stridulation and tremulation across species and test hypotheses about the drivers of signal use in each modality, we recorded multimodal signaling activity over 24 hours for males from 10 pseudophylline species from a single Panamanian community. We also collected data on demographic and morphological species characteristics, and acoustic features of airborne calls, such as bandwidth, peak frequency, and duration. Finally, we generated a molecular phylogeny for these species and used phylogenetic generalized least squares models to test for relationships between variables while controlling for evolutionary relationships. We found a negative relationship between sound and vibration calling, indicating that substrate-borne vibrational signaling may compensate for reduced airborne signaling in these species. Sound call bandwidth and the proportion of males collected at lights, a proxy for the amount of male movement, also explained a significant amount of variation in sound calling across species, indicating that the overall relationship between the two types of calling signals may be mediated by the specific characteristics of the signals as well as other species traits.

Bipedalism or bipedalisms: The os coxae of StW 573

There has been a long debate about the possibility of multiple contemporaneous species of Australopithecus in both eastern and southern Africa, potentially exhibiting different forms of bipedal locomotion. Here, we describe the previously unreported morphology of the os coxae in the 3.67 Ma Australopithecus prometheus StW 573 from Sterkfontein Member 2, comparing it with variation in ossa coxae in living humans and apes as well as other Plio-Pleistocene hominins. Statistical comparisons indicate that StW 573 and 431 resemble humans in their anteroposteriorly great iliac crest breadth compared with many other early australopiths, whereas Homo ergaster KNM WT 15000 surprisingly also has a relatively anterioposteriorly short iliac crest. StW 573 and StW 431 appear to resemble humans in having a long ischium compared with Sts 14 and KNM WT 15000. A Quadratic Discriminant Function Analysis of morphology compared with other Plio-Pleistocene hominins and a dataset of modern humans and hominoids shows that, while Lovejoy's heuristic model of the Ardipithecus ramidus os coxae falls with Pongo or in an indeterminate group, StW 573 and StW 431 from Sterkfontein Member 4 are consistently classified together with modern humans. Although clearly exhibiting the classic "basin shaped" bipedal pelvis, Sts 14 (also from Sterkfontein), AL 288-1 Australopithecus afarensis, MH2 Australopithecus sediba and KNM-WT 15000 occupy a position more peripheral to modern humans, and in some analyses are assigned to an indeterminate outlying group. Our findings strongly support the existence of two species of Australopithecus at Sterkfontein and the variation we observe in os coxae morphology in early hominins is also likely to reflect multiple forms of bipedality.

Exploring causal mechanisms of psychosis risk

Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors, and developing preventive interventions targeting risk and protective factors.

Network motifs in cellular neurophysiology

Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.

Mechanism of an Intrinsic Oscillation in Rat Geniculate Interneurons

Depolarizing current injections produced a rhythmic bursting of action potentials - a bursting oscillation - in a set of local interneurons in the lateral geniculate nucleus (LGN) of rats. The current dynamics underlying this firing pattern have not been determined, though this cell type constitutes an important cellular component of thalamocortical circuitry, and contributes to both pathologic and non-pathologic brain states. We thus investigated the source of the bursting oscillation using pharmacological manipulations in LGN slices in vitro and in silico. 1. Selective blockade of calcium channel subtypes revealed that high-threshold calcium currentsI L andI P contributed strongly to the oscillation. 2. Increased extracellular K+ concentration (decreased K+currents) eliminated the oscillation. 3. Selective blockade of K+ channel subtypes demonstrated that the calcium-sensitive potassium current (I A H P ) was of primary importance. A morphologically simplified, multicompartment model of the thalamic interneuron characterized the oscillation as follows: 1. The low-threshold calcium currentI T provided the strong initial burst characteristic of the oscillation. 2. Alternating fluxes through high-threshold calcium channels andI A H P then provided the continuing oscillation's burst and interburst periods respectively. This interplay betweenI L andI A H P contrasts with the current dynamics underlying oscillations in thalamocortical and reticularis neurons, which primarily involveI T andI H , orI T andI A H P respectively. These findings thus point to a novel electrophysiological mechanism for generating intrinsic oscillations in a major thalamic cell type. Because local interneurons can sculpt the behavior of thalamocortical circuits, these results suggest new targets for the manipulation of ascending thalamocortical network activity.

Review: Environmental enrichment builds functional capacity and improves resilience as an aspect of positive welfare in production animals

The success of the animal in coping with challenges, and in harnessing opportunities to thrive, is central to its welfare. Functional capacity describes the capacity of molecules, cells, organs, body systems, the whole animal, and its community to buffer against the impacts of environmental perturbations. This buffering capacity determines the ability of the animal to maintain or regain functions in the face of environmental perturbations, which is recognised as resilience. The accuracy of physiological regulation and the maintenance of homeostatic balance underwrite the dynamic stability of outcomes such as biorhythms, feed intake, growth, milk yield, and egg production justifying their assessment as indicators of resilience. This narrative review examines the influence of environmental enrichments, especially during developmental stages in young animals, in building functional capacity and in its subsequent expression as resilience. Experience of enriched environments can build skills and competencies across multiple functional domains including but not limited to behaviour, immunity, and metabolism thereby increasing functional capacity and facilitating resilience within the context of challenges such as husbandry practices, social change, and infection. A quantitative method for measuring the distributed property of functional capacity may improve its assessment. Methods for analysing embedded energy (emergy) in ecosystems may have utility for this goal. We suggest functional capacity provides the common thread that links environmental enrichments with an ability to express resilience and may provide a novel and useful framework for measuring and reporting resilience. We conclude that the development of functional capacity and its subsequent expression as resilience is an aspect of positive animal welfare. The emergence of resilience from system dynamics highlights a need to shift from the study of physical and mental states to the study of physical and mental dynamics to describe the positive dimension of animal welfare.

Virtual brain twins: from basic neuroscience to clinical use

Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.

Similar excitability through different sodium channels and implications for the analgesic efficacy of selective drugs

Nociceptive sensory neurons convey pain-related signals to the CNS using action potentials. Loss-of-function mutations in the voltage-gated sodium channel NaV1.7 cause insensitivity to pain (presumably by reducing nociceptor excitability) but clinical trials seeking to treat pain by inhibiting NaV1.7 pharmacologically have struggled. This may reflect the variable contribution of NaV1.7 to nociceptor excitability. Contrary to claims that NaV1.7 is necessary for nociceptors to initiate action potentials, we show that nociceptors can achieve similar excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8. Selectively blocking one of those NaV subtypes reduces nociceptor excitability only if the other subtypes are weakly expressed. For example, excitability relies on NaV1.8 in acutely dissociated nociceptors but responsibility shifts to NaV1.7 and NaV1.3 by the fourth day in culture. A similar shift in NaV dependence occurs in vivo after inflammation, impacting ability of the NaV1.7-selective inhibitor PF-05089771 to reduce pain in behavioral tests. Flexible use of different NaV subtypes exemplifies degeneracy - achieving similar function using different components - and compromises reliable modulation of nociceptor excitability by subtype-selective inhibitors. Identifying the dominant NaV subtype to predict drug efficacy is not trivial. Degeneracy at the cellular level must be considered when choosing drug targets at the molecular level.

Evaluation of Type 2 SLE symptoms in patients with a range of lupus nephritis activity

BACKGROUND

Type 2 systemic lupus erythematosus (SLE) symptoms, including fatigue, fibromyalgia, and brain fog, contribute to poor health-related quality of life (HRQoL) in patients with lupus. To test the hypothesis that Type 1 (classical inflammatory lupus) activity is associated with Type 2 SLE activity, we characterized the features of Type 2 SLE in patients with a range of lupus nephritis (LN) activity.

METHODS

This was a cross-sectional study of SLE patients [American College of Rheumatology (ACR) 1997 or Systemic Lupus International Collaborating Clinics (SLICC) 2012 classification criteria] from June 2018 to March 2020. Patients completed the Systemic Lupus Activity Questionnaire (SLAQ) and the Polysymptomatic Distress Scale. Patients were divided into groups based on their renal status. Active nephritis was defined using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) lupus nephritis parameter. Differences across groups were analyzed by Fisher's exact test and ANOVA.

RESULTS

In this cohort of 244 patients (93% female, mean age 43 years, 58% Black), 10% had active nephritis, 35% had historical nephritis, and 55% never had nephritis (non-nephritis). Active nephritis and non-nephritis patients had a similar burden of Type 2 SLE symptoms, despite a difference in Type 1 SLE activity. Patients with active nephritis had higher Type 2 PGA (Physician Global Assessment) scores and reported more Type 2 SLE symptoms than inactive nephritis patients. Patients with inactive nephritis had the lowest Type 2 SLE activity.

CONCLUSIONS

While Type 2 SLE symptoms are common in SLE, our findings suggest that patients with active nephritis experience significant Type 2 SLE symptoms that may be ameliorated as nephritis improves. We also observed that non-nephritis patients had a similar burden of Type 2 SLE symptoms as patients with active nephritis, despite having on average lower Type 1 SLE activity. Therefore, the etiology of Type 2 SLE symptoms is likely multifactorial and may be driven by inflammatory and non-inflammatory biopsychosocial factors. Key Points • Patients with active nephritis experienced significant Type 2 symptoms that may be ameliorated as nephritis improves. • Non-nephritis patients had a similar burden of Type 2 SLE symptoms as patients with active nephritis, despite having on average lower Type 1 SLE activity. • Because etiology of Type 2 SLE symptoms is likely multifactorial and may be driven by inflammatory and non-inflammatory biopsychosocial factors.

Disentangling neuroplasticity mechanisms in post-stroke language recovery

A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Clinical severity in Parkinson's disease is determined by decline in cortical compensation

Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.

Current and Future Approaches to Pediatric Anxiety Disorder Treatment

This overview critically appraises the literature on the treatment of pediatric anxiety disorders. The two established treatments for these conditions comprise cognitive-behavioral therapy (CBT) and antidepressant medications. Many youths receiving these treatments fail to achieve remission, which creates a need for new treatments. After summarizing the literature on CBT and currently available medications, the authors describe research that lays a foundation for improvements in the treatment of pediatric anxiety disorders. This foundation leverages neuroscientific investigations, also described in the overview, which provide insights on mechanisms of successful treatment.

The tonoplast localized protein PtNPF1 participates in the regulation of nitrogen response in diatoms

Diatoms are a highly successful group of phytoplankton, well adapted also to oligotrophic environments and capable of handling nutrient fluctuations in the ocean, particularly nitrate. The presence of a large vacuole is an important trait contributing to their adaptive features. It confers diatoms the ability to accumulate and store nutrients, such as nitrate, when they are abundant outside and then to reallocate them into the cytosol to meet deficiencies, in a process called luxury uptake. The molecular mechanisms that regulate these nitrate fluxes are still not known in diatoms. In this work, we provide new insights into the function of Phaeodactylum tricornutum NPF1, a putative low-affinity nitrate transporter. To accomplish this, we generated overexpressing strains and CRISPR/Cas9 loss-of-function mutants. Microscopy observations confirmed predictions that PtNPF1 is localized on the vacuole membrane. Furthermore, functional characterizations performed on knock-out mutants revealed a transient growth delay phenotype linked to altered nitrate uptake. Together, these results allowed us to hypothesize that PtNPF1 is presumably involved in modulating intracellular nitrogen fluxes, managing intracellular nutrient availability. This ability might allow diatoms to fine-tune the assimilation, storage and reallocation of nitrate, conferring them a strong advantage in oligotrophic environments.

Chemical Reactions in Living Systems

The term "in vivo ("in the living") chemistry" refers to chemical reactions that take place in a complex living system such as cells, tissue, body liquids, or even in an entire organism. In contrast, reactions that occur generally outside living organisms in an artificial environment (e.g., in a test tube) are referred to as in vitro. Over the past decades, significant contributions have been made in this rapidly growing field of in vivo chemistry, but it is still not fully understood, which transformations proceed efficiently without the formation of by-products or how product formation in such complex environments can be characterized. Potential applications can be imagined that synthesize drug molecules directly within the cell or confer new cellular functions through controlled chemical transformations that will improve the understanding of living systems and develop new therapeutic strategies. The guiding principles of this contribution are twofold: 1) Which chemical reactions can be translated from the laboratory to the living system? 2) Which characterization methods are suitable for studying reactions and structure formation in complex living environments?

A generative model of electrophysiological brain responses to stimulation

Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.