Fundamental behaviors emerge from simulations of a living minimal cell.

We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.

A hierarchy index for networks in the brain reveals a complex entangled organizational structure.

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

Dynamic context-dependent regulation of auxin feedback signaling in synthetic gene circuits.

Phytohormone auxin plays a key role in regulating plant organogenesis. However, understanding the complex feedback signaling network that involves at least 29 proteins in Arabidopsis in the dynamic context remains a significant challenge. To address this, we transplanted an auxin-responsive feedback circuit responsible for plant organogenesis into yeast. By generating dynamic microfluidic conditions controlling gene expression, protein degradation, and binding affinity of auxin response factors to DNA, we illuminate feedback signal processing principles in hormone-driven gene expression. In particular, we recorded the regulatory mode shift between stimuli counting and rapid signal integration that is context-dependent. Overall, our study offers mechanistic insights into dynamic auxin response interplay trackable by synthetic gene circuits, thereby offering instructions for engineering plant architecture.

A quantum information processing machine for computing by observables.

A quantum machine that accepts an input and processes it in parallel is described. The logic variables of the machine are not wavefunctions (qubits) but observables (i.e., operators) and its operation is described in the Heisenberg picture. The active core is a solid-state assembly of small nanosized colloidal quantum dots (QDs) or dimers of dots. The size dispersion of the QDs that causes fluctuations in their discrete electronic energies is a limiting factor. The input to the machine is provided by a train of very brief laser pulses, at least four in number. The coherent band width of each ultrashort pulse needs to span at least several and preferably all the single electron excited states of the dots. The spectrum of the QD assembly is measured as a function of the time delays between the input laser pulses. The dependence of the spectrum on the time delays can be Fourier transformed to a frequency spectrum. This spectrum of a finite range in time is made up of discrete pixels. These are the visible, raw, basic logic variables. The spectrum is analyzed to determine a possibly smaller number of principal components. A Lie-algebraic point of view is used to explore the use of the machine to emulate the dynamics of other quantum systems. An explicit example demonstrates the considerable quantum advantage of our scheme.

Decomposing Neural Circuit Function into Information Processing Primitives.

It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.

Perturbed Information Processing Complexity in Experimental Epilepsy.

Comorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we test the hypothesis that primitive processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex in experimental epilepsy in adult, male Wistar rats. We find that information storage and sharing are organized into substates across the stereotypic states of slow and theta oscillations in both epilepsy and control conditions. However, their internal composition and organization through time are disrupted in epilepsy, partially losing brain state selectivity compared with controls, and shifting toward a regimen of disorder. We propose that the alteration of information processing at this algorithmic level of computation, the theoretical intermediate level between structure and function, may be a mechanism behind the emergent and widespread comorbidities associated with epilepsy, and perhaps other disorders.SIGNIFICANCE STATEMENT Comorbidities, such as cognitive deficits, which often accompany epilepsies, constitute a basal state, while seizures are rare and transient events. This suggests that neural dynamics, in particular those supporting cognitive function, are altered in a permanent manner in epilepsy. Here, we show that basic processes of information processing at the core of cognitive function (i.e., storage and sharing of information) are altered in the hippocampus and the entorhinal cortex (two regions involved in memory processes) in experimental epilepsy. Such disruption of information processing at the algorithmic level itself could underlie the general performance impairments in epilepsy.

Key Issues Review: Useful autonomous quantum machines.

Controlled quantum machines have matured significantly. A natural next step is to increasingly grant them autonomy, freeing them from time-dependent external control. For example, autonomy could pare down the classical control wires that heat and decohere quantum computers; and an autonomous quantum refrigerator recently reset superconducting qubits to near their ground states, as is necessary before a computation. Which fundamental conditions are necessary for realizing useful autonomous quantum machines? Inspired by recent quantum thermodynamics and chemistry, we posit conditions analogous to DiVincenzo's criteria for quantum computing. Furthermore, we illustrate the criteria with multiple autonomous quantum machines (refrigerators, computers, clocks, etc.) and multiple candidate platforms (neutral atoms, molecules, superconducting qubits, etc.). Our criteria are intended to foment and guide the development of useful autonomous quantum machines.

Molecular nanomagnets: a viable path toward quantum information processing?

Molecular nanomagnets (MNMs), molecules containing interacting spins, have been a playground for quantum mechanics. They are characterized by many accessible low-energy levels that can be exploited to store and process quantum information. This naturally opens the possibility of using them as qudits, thus enlarging the tools of quantum logic with respect to qubit-based architectures. These additional degrees of freedom recently prompted the proposal for encoding qubits with embedded quantum error correction (QEC) in single molecules. QEC is the holy grail of quantum computing and this qudit approach could circumvent the large overhead of physical qubits typical of standard multi-qubit codes. Another important strength of the molecular approach is the extremely high degree of control achieved in preparing complex supramolecular structures where individual qudits are linked preserving their individual properties and coherence. This is particularly relevant for building quantum simulators, controllable systems able to mimic the dynamics of other quantum objects. The use of MNMs for quantum information processing is a rapidly evolving field which still requires to be fully experimentally explored. The key issues to be settled are related to scaling up the number of qudits/qubits and their individual addressing. Several promising possibilities are being intensively explored, ranging from the use of single-molecule transistors or superconducting devices to optical readout techniques. Moreover, new tools from chemistry could be also at hand, like the chiral-induced spin selectivity. In this paper, we will review the present status of this interdisciplinary research field, discuss the open challenges and envisioned solution paths which could finally unleash the very large potential of molecular spins for quantum technologies.

Metric error monitoring as a component of metacognitive processing.

Metacognitive processing constitutes one of the contemporary target domains in consciousness research. Error monitoring (the ability to correctly report one's own errors without feedback) is considered one of the functional outcomes of metacognitive processing. Error monitoring is traditionally investigated as part of categorical decisions where choice accuracy is a binary construct (choice is either correct or incorrect). However, recent studies revealed that this ability is characterized by metric features (i.e., direction and magnitude) in temporal, spatial, and numerical domains. Here, we discuss methodological approaches to investigating metric error monitoring in both humans and non-human animals and review their findings. The potential neural substrates of metric error monitoring measures are also discussed. This new scope of metacognitive processing can help improve our current understanding of conscious processing from a new perspective. Thus, by summarizing and discussing the perspectives, findings, and common applications in the metric error monitoring literature, this paper aims to provide a guideline for future research.

Coactivation of Autonomic and Central Nervous Systems During Processing of Socially Relevant Information in Autism Spectrum Disorder: A Systematic Review.

Body-brain interaction provides a novel approach to understand neurodevelopmental conditions such as autism spectrum disorder (ASD). In this systematic review, we analyse the empirical evidence regarding coexisting differences in autonomic (ANS) and central nervous system (CNS) responses to social stimuli between individuals with ASD and typically developing individuals. Moreover, we review evidence of deviations in body-brain interaction during processing of socially relevant information in ASD. We conducted systematic literature searches in PubMed, Medline, PsychInfo, PsychArticles, and Cinahl databases (until 12.1.2022). Studies were included if individuals with ASD were compared with typically developing individuals, study design included processing of social information, and ANS and CNS activity were measured simultaneously. Out of 1892 studies identified based on the titles and abstracts, only six fulfilled the eligibility criteria to be included in synthesis. The quality of these studies was assessed using a quality assessment checklist. The results indicated that individuals with ASD demonstrate atypicalities in ANS and CNS signalling which, however, are context dependent. There were also indications for altered contribution of ANS-CNS interaction in processing of social information in ASD. However, the findings must be considered in the context of several limitations, such as small sample sizes and high variability in (neuro)physiological measures. Indeed, the methodological choices varied considerably, calling for a need for unified guidelines to improve the interpretability of results. We summarize the current experimentally supported understanding of the role of socially relevant body-brain interaction in ASD. Furthermore, we propose developments for future studies to improve incremental knowledge building across studies of ANS-CNS interaction involving individuals with ASD.

The (Un)ideal Physicist: How Humans Rely on Object Interaction for Friction Estimates.

To estimate object properties such as mass or friction, our brain relies on visual information to efficiently compute approximations. The role of sensorimotor feedback, however, is not well understood. Here we tested healthy adults (N = 79) in an inclined-plane problem, that is, how much a plane can be tilted before an object starts to slide, and contrasted the interaction group with observation groups who accessed involved forces by watching objects being manipulated. We created objects of different masses and levels of friction and asked participants to estimate the critical tilt angle after pushing an object, lifting it, or both. Estimates correlated with applied forces and were biased toward object mass, with higher estimates for heavier objects. Our findings highlight that inferences about physical object properties are tightly linked to the human sensorimotor system and that humans integrate sensorimotor information even at the risk of nonveridical perceptual estimates.

Engineering Modular DNA Reaction Networks for Signal Processing.

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs. This strategy is based on a universal core unit with signal selection capability, and a time-adjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal cross-catalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for large-scale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Advancing Chinese biomedical text mining with community challenges.

OBJECTIVE: This study aims to review the recent advances in community challenges for biomedical text mining in China. METHODS: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. RESULTS: We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. CONCLUSION: Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.

Complex decongestive therapy improves finger tapping score in patients with breast cancer-related lymphedema.

PURPOSE: Breast cancer-related lymphedema (BCRL) impairs upper limb function and cognitive performance. This study aimed to evaluate the effects of fifteen sessions of complex decongestive therapy (CDT) on fine motor performance and information processing speed in women with BCRL. METHODS: Thirty-eight women with BCRL (54.97 ± 10.78 years) were recruited in the study. Participants either received five times weekly CDT consisting of manual lymphatic drainage, skin care, compression bandaging, and remedial exercises (n = 19) or served as a wait-list control group (n = 19). We used the Finger Tapping Task to assess fine motor performance and the Digit Symbol Substitution Test to assess information processing speed. ANCOVA was performed to analyze the effect of CDT on the dependent variables, adjusting for covariates and baseline values. RESULTS: CDT significantly improved finger tapping score (p < 0.001) compared to the wait-list to the control group, whereas information processing speed did not significantly change (p = 0.673). CONCLUSION: The findings suggest that CDT is an effective conservative therapeutic approach to improve upper extremity fine motor function in women with BCRL. Future studies are needed to investigate the effect of CDT on different cognitive domains.

A qualitative study of career decision making among African and Asian international medical students in China: process, challenges, and strategies.

China hosts around 68,000 international medical students (IMSs) primarily from lower income countries in Africa and Asia, who have the potential to contribute to international medical services. Understanding how these IMSs make career decisions can help better address the issue of global medical workforce shortage. However, such research is limited. Our study aims to explore the career decision-making process of China-educated IMSs, the challenges they experienced and the strategies they employed.In this exploratory qualitative study, we conducted semi-structured interviews with IMSs educated in China in 2022 using purposeful sampling. Twenty virtual one-on-one interviews were conducted, and data were analysed through directed qualitative content analysis. Cognitive Information Processing (CIP) theory was applied as the guiding framework for organising and analysing the data.The career decision-making process of the participants generally followed the stages of decision-making cycle in CIP theory, with a combination of urgent migration decisions and specialisation considerations adding layers of complexity to their career trajectories. Identified challenges encompassed lack of knowledge about oneself and career options, lack of decision-making skills, concerns of contextual complexities that limited the career decision-making process, low motivation and negative thoughts. Specific challenges due to their role as IMSs arose, which were related to career information access, self-capability evaluation, degree accreditation, employment competitiveness and mental states. Participants' proposed strategies were categorised into personal and institutional aspects, providing insights into addressing these challenges.This study substantiates and expands the application of the CIP theory within the sphere of the particular cultural and educational context of IMSs educated in China. It highlights the significance of integrating migration decision-making into career guidance for IMSs, and contributes to the literature by proposing an evidence-based tiered career intervention programme for IMSs.

The impact of public leadership on collaborative administration and public health delivery.

BACKGROUND: This research depicts the linkage of public leadership on public health delivery (PHD) and collaborative administration. The research is also focused to examine the effect of public leadership on public health delivery through the intervening variable of collaborative administration by using both social information processing theory and collaboration theory. METHODS: This research is based on quantitative method. Data was collected from 464 public hospital administration in the context of Pakistan. This study evaluated data using SPSS, AMOS, and PROCESS Macro. RESULTS: Public leadership has a positive profound effect on public health delivery and collaborative administration, and that collaborative administration significantly promotes public health delivery. The outcomes also exposed that public leadership has substantial influence on public health delivery through intervening collaborative administration. CONCLUSIONS: Whilst public leadership demonstrated positive outcomes on public health delivery and collaborative administration, there is a need for more rigor studies on collaborative governance leadership, collaborative ethics and collaborative norms in the public health service.

Sustainability risk management: Exploring the role of artificial intelligence capabilities through an information-processing lens.

The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.

Hostile and threatening interpretation biases in adolescent inpatients are specific to callous-unemotional traits and social anxiety.

Psychopathological syndromes, such as disruptive behavior and anxiety disorders in adolescence, are characterized by distorted cognitions and problematic behavior. Biased interpretations of ambiguous social situations can elicit both aggressive and avoidance behavior. Yet, it is not well understood whether different interpretation biases are specific to different syndromes, or whether they can co-occur. We assessed both hostile and threatening interpretation biases in identical social situations, and proposed that they are uniquely related to callous-unemotional (CU) traits and social anxiety, respectively. We also explored the role of gender and age herein. The sample consisted of 390 inpatients between 10 and 18 years of age with a variety of psychiatric disorders. Hostile and threatening interpretations were assessed with the Ambiguous Social Scenario Task (ASST) consisting of 10 written vignettes. Both CU-traits and social anxiety were assessed with self-report questionnaires. Results showed that, overall, CU-traits were related to more hostile interpretations, whereas social anxiety was related to more threatening interpretations. In addition, in boys, hostile and threatening interpretations correlated significantly positive with each other. Age was not related to interpretation biases. Together, these results generally support the content-specificity of interpretation biases in concepts relevant to disruptive behavior disorders and anxiety disorders, and indicate that different interpretation biases can co-occur specifically in boys.

Aggressiveness, inhibitory control, and emotional states: A provocation paradigm.

The current study examines the effects of trait aggressiveness, inhibitory control and emotional states on aggressive behavior in a laboratory paradigm. One hundred and fifty-one adult participants took part (73 men, 71 women, and 7 nondisclosed). Event Related Potentials (ERPs) during a Go/No-Go task were utilized to capture the extent of inhibitory processing, with a laboratory provocation paradigm used to assess aggression. Contrary to the expectations, negative affective responses to provocation were negatively associated only with short-lived aggression and only among those with high past aggressiveness. Furthermore, past aggressiveness was related to a continuous increase in laboratory aggressive behavior regardless of the level of inhibitory control (P3 difference amplitude). However, feeling hostile was associated with short-lived aggressive behavior, only in those with lower levels of inhibitory control. These findings demonstrate the effect of distinct mechanisms on different patterns of aggressive behavior.

Negative emotional cues improve free recall of positive and neutral words in unmedicated patients with major depressive disorder.

Individuals with major depressive disorder (MDD) exhibit attentional biases toward negative, mood-congruent stimuli while filtering out positive and neutral stimuli, resulting in memory biases to negative content. While attentional and memory biases in MDD have been extensively studied, the underlying mechanisms of these biases remain unclear. The current study investigates a novel model proposing that exposure to negative emotional cues triggers a transient "attentional window" in individuals with MDD, leading to heightened and deeper cognitive processing of any subsequent information, irrespective of its content. Forty-two unmedicated patients with MDD and no comorbid disorder and 41 healthy controls, completed six blocks of the emotional memory task, in which they were asked to watch a short video (negative, neutral, or positive valence) followed by a memory test on a list of neutral or positive valance words. Results indicated that participants with MDD, but not healthy controls, had better recall performance after a negative video compared to after neutral or positive videos, and that this effect occurred for both neutral and positive word-lists. These findings provide evidence that participants with MDD engage in deeper information processing following exposure to negative emotional stimuli. Potential clinical implications are discussed.