Control Processing Research Articles

Mechanisms underlying modulation of human GlyRα3 receptors by Zn2+ and pH.

Glycine receptors (GlyRs) regulate motor control and pain processing in the central nervous system through inhibitory synaptic signaling. The subtype GlyRα3 expressed in nociceptive sensory neurons of the spinal dorsal horn is a key regulator of physiological pain perception. Disruption of spinal glycinergic inhibition is associated with chronic inflammatory pain states, making GlyRα3 an attractive target for pain treatment. GlyRα3 activity is modulated by numerous endogenous and exogenous ligands that consequently affect pain sensitization. To understand the mechanism of two such endogenous modulators, Zn2+ and protons, we have used cryo-electron microscopy to determine structures of full-length human GlyRα3 in various functional states. Whereas acidic pH reduces peak glycine response, Zn2+ displays biphasic modulation in a concentration-dependent manner. Our findings reveal the effector sites and also capture intermediate conformations in the gating cycle. Combined with molecular dynamics simulations and electrophysiology, this work provides important insights into GlyRα3 activation and regulation.

Striatal stimulation enhances cognitive control and evidence processing in rodents and humans.

Brain disorders, in particular mental disorders, might be effectively treated by direct electrical brain stimulation, but clinical progress requires understanding of therapeutic mechanisms. Animal models have not helped, because there are no direct animal models of mental illness. Here, we propose a potential path past this roadblock, by leveraging a common ingredient of most mental disorders: impaired cognitive control. We previously showed that deep brain stimulation (DBS) improves cognitive control in humans. We now reverse translate that result using a set-shifting task in rats. DBS-like stimulation of the midstriatum improved reaction times without affecting accuracy, mirroring our human findings. Impulsivity, motivation, locomotor, and learning effects were ruled out through companion tasks and model-based analyses. To identify the specific cognitive processes affected, we applied reinforcement learning drift-diffusion modeling. This approach revealed that DBS-like stimulation enhanced evidence accumulation rates and lowered decision thresholds, improving domain-general cognitive control. Reanalysis of prior human data showed that the same mechanism applies in humans. This reverse/forward translational model could have near-term implications for clinical DBS practice and future trial design.

Optimal Control of a Harmonic Oscillator with Parametric Excitation

This paper addresses the time-optimal control problem for a harmonic oscillator characterized by a time-dependent frequency. The primary objective is to determine the minimal time required to transition the system from an initial state, defined by a given position and velocity, to a specified final state, while ensuring that the frequency remains within prescribed bounds. The key challenge lies in identifying the optimal switching times between two available frequencies to meet all boundary conditions efficiently. By examining various boundary scenarios, constructing the reachable set of all admissible trajectories, and employing both analytical techniques and control theory, we develop a robust solution strategy. This work holds particular relevance for practical applications demanding rapid state transitions, such as mechanical vibration control and signal processing, where achieving time-optimal performance is critical. Furthermore, the methods presented are adaptable to a wide range of systems facing similar constraints, providing a versatile and effective framework for time-optimal control.

Investigation of functional connectivity differences based on anxiety tendencies

IntroductionAnxiety is an emotion necessary for human survival. However, persistent and excessive anxiety can be clinically challenging. Increased anxiety affects daily life and requires early detection and intervention. Therefore, a better understanding of the neural basis of mild anxiety is needed. However, previous studies have focused primarily on resting-state functional magnetic resonance imaging (rs-fMRI) in patients with psychiatric disorders presenting with anxiety. Notably, only a few studies have been conducted on healthy participants, and the relationship between anxiety and functional brain connectivity in the healthy range remains unclear. Therefore, in this study, we aimed to clarify the differences in functional brain connectivity at different degrees of anxiety among healthy participants.MethodsThis study included 48 healthy participants with no history of psychiatric disorders. Participants were administered The General Health Questionnaire (GHQ) 60, a psychological test for assessing anxiety, and the Manifest Anxiety Scale (MAS). The participants then underwent rs-fMRI. Based on the results of each psychological test, the participants were classified into normal and anxiety groups, and the functional connectivity between the two groups was compared using a seed-to-voxel analysis.ResultsComparison of functional brain connectivity between the normal and anxiety groups classified based on the GHQ60 and MAS revealed differences between brain regions comprising the salience network (SN) in both psychological tests. For the GHQ60, the anxiety group showed reduced connectivity between the right supramarginal gyrus and insular cortex compared with the normal group. However, for the MAS, the anxiety group showed reduced connectivity between the right supramarginal and anterior cingulate cortical gyri compared with the normal group.ConclusionFunctional connectivity within the SN was reduced in the group with higher anxiety when functional brain connectivity at different anxiety levels was examined in healthy participants. This suggests that anxiety is involved in changes in the functional brain connectivity associated with emotional processing and cognitive control.

Comprehensive analysis of the Kinetoplastea intron landscape reveals a novel intron-containing gene and the first exclusively trans-splicing eukaryote

BackgroundIn trypanosomatids, a group of unicellular eukaryotes that includes numerous important human parasites, cis-splicing has been previously reported for only two genes: a poly(A) polymerase and an RNA helicase. Conversely, trans-splicing, which involves the attachment of a spliced leader sequence, is observed for nearly every protein-coding transcript. So far, our understanding of splicing in this protistan group has stemmed from the analysis of only a few medically relevant species. In this study, we used an extensive dataset encompassing all described trypanosomatid genera to investigate the distribution of intron-containing genes and the evolution of splice sites.ResultsWe identified a new conserved intron-containing gene encoding an RNA-binding protein that is universally present in Kinetoplastea. We show that Perkinsela sp., a kinetoplastid endosymbiont of Amoebozoa, represents the first eukaryote completely devoid of cis-splicing, yet still preserving trans-splicing. We also provided evidence for reverse transcriptase-mediated intron loss in Kinetoplastea, extensive conservation of 5′ splice sites, and the presence of non-coding RNAs within a subset of retained trypanosomatid introns.ConclusionsAll three intron-containing genes identified in Kinetoplastea encode RNA-interacting proteins, with a potential to fine-tune the expression of multiple genes, thus challenging the perception of cis-splicing in these protists as a mere evolutionary relic. We suggest that there is a selective pressure to retain cis-splicing in trypanosomatids and that this is likely associated with overall control of mRNA processing. Our study provides new insights into the evolution of introns and, consequently, the regulation of gene expression in eukaryotes.

Resting-state functional network segregation of the default mode network predicts valence bias across the lifespan

Abstract The brain is organized into intrinsically connected functional networks that can be reliably identified during resting-state functional magnetic resonance imaging (fMRI). Healthy aging is marked by decreased network segregation, which is linked to worse cognitive functioning, but aging-related changes in emotion are less well characterized. Valence bias, which represents the tendency to interpret emotionally ambiguous information as positive or negative, is more positive in older than younger adults and is associated with differences in task-based fMRI activation in the amygdala, prefrontal cortex, and a cingulo-opercular (CO) network. Here, we examined valence bias, age, and resting-state network segregation of 12 brain networks in a sample of 221 healthy individuals from 6 to 80 years old. Resting-state network segregation decreased linearly with increasing age, extending prior reports of de-differentiation across the lifespan. Critically, a more positive valence bias was related to lower segregation of the default mode network (DMN), due to stronger functional connectivity of the DMN with CO and, to a lesser extent, the ventral attention network (VAN) in all participants. In contrast to this overall segregation effect, in participants over 39 years old (who tend to show a positive valence bias), bias was also related to weaker connectivity between the DMN and Reward networks. The present findings indicate that specific interactions between the DMN, a task control network (CO), an emotion processing network (Reward), and, to a weaker extent, an attention network (VAN), support a more positive valence bias, perhaps through regulatory control of self-referential processing and reduced emotional reactivity in aging. The current work offers further insight into the functional brain network alterations that may contribute to affective well-being and dysfunction across the lifespan.

Na+-doped WO3 double-layer resistive switching device for biomimetic applications

We report on polycrystalline double-layer NaWO3 thin films in a two-terminal device configuration showing resistive switching (RS) based on the neuronal ion Na+ for biomimetic applications. A top NayWO3 layer functions as a Na+ reservoir, enabling fast Na+ ion intercalation into a bottom NaxWO3 layer with x < y, giving good resistance control for analog processing. Operando Raman measurements show Na+ activation under 0.5 V and the electrical characterization proves that the switching is linked to the motion of Na+. The switching mechanism is investigated thoroughly by studying different film layer combinations. The observed RS performance can be explained by the change of electron emission/hopping site energy levels upon Na+ intercalation. The presented devices based on biocompatible materials constitute a promising route towards an analog processing building block for emulating neuronal ion channels for emerging biomimetic and potentially biomedical applications, as well as for flexible electronics.

Isolation and Molecular Profiling of Nuclei of Specific Neuronal Types from Human Cerebral Cortex and Striatum.

Most pathological conditions of the central nervous system do not affect all cell types to the same extent. Delineation of molecular events underlying disease symptoms, including genetic, epigenetic, and transcriptional changes, thus relies on the ability to characterize a specific cell type separately from others. We have developed a methodology for the collection of nuclear RNA and genomic DNA of specific cell types from frozen post-mortem striatum and cerebral cortex. This allows deep transcriptomic profiling of specific cell populations and characterization of their genomes and epigenetic properties. The method is based on the purification of cell nuclei, followed by fluorescence-activated sorting of nuclei (FANS) labeled with nucleic acid probes or antibodies binding to targets present in specific cell types. The protocol describes in detail the procedure for isolating and labeling neuronal and glial nuclei from human brain tissue, the steps that can be taken to extract RNA and genomic DNA, a way to combine the procedure with ATAC-seq to yield information about chromatin accessibility, as well as computational measures for assessing the quality of cell type-specific RNA-seq and ATAC-seq datasets. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Tissue homogenization, isolation of cell nuclei by ultracentrifugation and formaldehyde-fixation Basic Protocol 2: Antibody-based labeling and isolation of nuclei of specific neocortical neuron types Support Protocol 1: Generation of ATAC-seq libraries from the nuclei of specific neuron types of the cerebral cortex Basic Protocol 3: Nucleic acid hybridization-based labeling and isolation of nuclei of specific striatal projection neuron types Alternate Protocol 1: Labeling and isolation of nuclei of specific striatal interneuron types Support Protocol 2: Generation of ATAC-seq libraries from the nuclei of specific striatal neuron types Basic Protocol 4: Extraction of genomic DNA and nuclear RNA and preparation of sequencing libraries Basic Protocol 5: Processing and quality control of FANS-seq and ATAC-seq data.

Exoskeleton Robot Based on Brain-Computer Interface

Brain-computer interface (BCI) technology represents a means of facilitating human-computer interaction. One of the most widely accepted paradigms of brain-computer interface is motor imagery, which enables the recognition of electroencephalogram (EEG) signals generated in a specific brain region by imagining the movement of a limb. Following the acquisition, preprocessing, feature processing, and signal classification of the EEG signals, the complex signals are accurately recognized. Therefore, by creating a control system that translates the recognized EEG signals into movement commands for the robot and transmits them to the robot, it is possible to control the robot's movements by motor imagery. The convolutional neural network is the most popular signal processing algorithm due to its high EEG recognition accuracy, excellent performance in feature extraction, and superior performance in end-to-end learning. The convolutional neural network is an optimal method for signal processing in robot control. This makes CNN an optimal choice for processing EEG signals in robot control, enhancing both the effectiveness and user experience of BCI systems by enabling more intuitive and responsive interactions with robotic devices.

Kisspeptin fiber and receptor distribution analysis suggests its potential role in central sensorial processing and behavioral state control.

Kisspeptin (KP) signaling in the brain is defined by the anatomical distribution of KP-producing neurons, their fibers, receptors, and connectivity. Technological advances have prompted a re-evaluation of these chemoanatomical aspects, originally studied in the early years after the discovery of KP and its receptor Kiss1r. We have previously characterized(1) seven KP neuronal populations in the mouse brain at the mRNA level, including two novel populations, and examined their short-term response to gonadectomy. In this study, we mapped KP fiber distribution in rats and mice using immunohistochemistry under intact and short- and long-term post-gonadectomy conditions. Kiss1r mRNA expression was examined via RNAscope, in relation to vesicular GABA transporter ( Slc32a1 ) in whole mouse brain and to KP and vesicular glutamate transporter 2 ( Kiss1 and Slc17a6 ) in hypothalamic RP3V and arcuate regions. We identified KP fibers in 118 brain regions, primarily in extra-hypothalamic areas associated with sensorial processing and behavioral state control. KP-immunoreactive fiber density and distribution were largely unchanged by gonadectomy. Kiss1r was expressed prominently in sensorial and state control regions such as septal nuclei, the suprachiasmatic nucleus, locus coeruleus, hippocampal layers, thalamic nuclei, and cerebellar structures. Co-expression of Kiss1r and Kiss1 was observed in hypothalamic neurons, suggesting both autocrine and paracrine KP signaling mechanisms. These findings enhance our understanding of KP signaling beyond reproductive functions, particularly in sensorial and behavioral state regulation. This study opens new avenues for investigating KP's role in controlling complex physiological processes, including those not related to reproduction.

Neural circuit basis of pathological anxiety.

Anxiety disorders are the most prevalent mental health conditions worldwide. Unfortunately, the understanding of the precise neurobiological mechanisms that underlie these disorders remains limited. Current diagnostic classifications, based on observable symptoms rather than underlying pathophysiology, do not capture the heterogeneity within and across anxiety disorders. Recent advances in functional neuroimaging have provided new insights into the neural circuits implicated in pathological anxiety, revealing dysfunctions that cut across traditional diagnostic boundaries. In this Review, we synthesize evidence that highlights abnormalities in neurobehavioural systems related to negative valence, positive valence, cognitive systems and social processes. We emphasize that pathological anxiety arises not only from heightened reactivity in acute threat ('fear') circuits but also from alterations in circuits that mediate distant (potential) and sustained threat, reward processing, cognitive control and social processing. We discuss how circuit vulnerabilities can lead to the emergence and maintenance of pathological anxiety. Once established, these neural abnormalities can be exacerbated by maladaptive behaviours that prevent extinction learning and perpetuate anxiety disorders. By delineating the specific neural mechanisms in each neurobiological system, we aim to contribute to a more comprehensive understanding of the neurobiology of anxiety disorders, potentially informing future research directions in this field.

Exploring the connectivity of dorsolateral prefrontal cortex and the modulatory impact of transcranial magnetic stimulation in adolescents with depression: a focus on pain-related cognitive processing

BackgroundAbnormal cognitive response to pain is consistently associated with deleterious outcomes among adolescents with depression. Highly relevant to both pain and cognition, dorsolateral prefrontal cortex (DLPFC) is important to understanding pain cognition. Our study aimed to characterize the circuit of DLPFC and the efficacy of transcranial magnetic stimulation (TMS) over DLPFC on pain cognition in adolescents with depression.MethodsUsing neuroimaging data, we first compared functional connectivity (FC) of DLPFC between 60 adolescents with depression and 65 controls. The patients were then divided into add-on TMS group (N = 30) and Sertraline group (N = 30). Clinical outcome was determined using Pain Vigilance and Awareness Questionnaire (PVAQ) and Pain Catastrophizing Scale (PCS). Finally, we conducted regression analysis to assess the effect that FC of DLPFC contributes to in predicting clinical outcome.ResultsFC analysis showed that compared to controls, patients displayed hyperconnection of left DLPFC - left triangular part of inferior frontal gyrus (IFG), which was significantly correlated with higher PCS total-, magnification- and helplessness-scores. Patients also showed hyperconnection of right DLPFC - right supramarginal gyrus (SMG), which was correlated with lower PCS total- and rumination- scores. After intervention, the add-on TMS group displayed significantly decreased score on PVAQ, PCS total and rumination. FC of left DLPFC - bilateral triangular part of IFG, - right SMG, as well as right DLPFC - left putamen, could predict the improvement of pain vigilance and magnification.ConclusionOur results point to a key role of DLPFC acting as a connection linking cognitive control and pain processing in adolescents with depression.Trial registrationThe study is registered in https://www.chictr.org.cn/ with a registration number ChiCTR2000039503 (date: 10.30.2020).

Supply Chain Analytics, Definitions, Characteristics and Applications: A Systematic Literature Review

While there is a high uptake of BDA in the realm of supply chain management, in the view of automation supply chains and improving their value proposition by providing more accurate data for demand forecasting. There are material knowledge gaps on the SC-specific analytics applied to match demand, albeit the existing knowledge could be more amorphous. From this backdrop, the study endeavored to analyze extant literature within the ambit of BDA to unpack the current trends and possible future research directions to foster the application of BDA in SC contexts. The study adopted a systematic literature review of the extant literature published between 2014-2023. The study adopted the five-stage iterative procedure used in the systematic review methodology. The review&amp;apos;s findings depict extensive use of big data analytics in matching demand and supply and supply chain optimization. The findings of this study adduce almost non-rebuttable evidence that big data analytics can be applied in procurement, inventory control, logistics, and order processing. Under the auspices of BDA is the SCA. Arguably, extant research has demonstrated the capability of SCA in mitigating contemporary SC risks such as mismatches between demand and supply, sub-optimal SCs, and underutilization of the supply chain infrastructures at the cusps of various echelons.

Omics-Integrated Approach (Metabolomics, Proteomics and Lipidomics) to Assess the Quality Control of Aquatic and Seafood Products

Since the demand for seafood products is growing and aquaculture provides more than fifty percent of the aquatic food as reported by FAO, the development of more accurate and sensitive analytical techniques in order to screen and evaluate the safety and quality of seafood products is needed. At this point, several omic techniques like proteomics, lipidomics, and metabolomics, or combinations of them, are used for integration into seafood processing and quality control. Moreover, according to the literature, using the respective techniques can prevent, control, and treat diseases in fish as well as address several issues in aquaculture. Proteomic techniques are used for the expression of proteins and their modifications. Metabolomic techniques are used for accurate identification of species, while lipidomics techniques are used for the identification of different or specific lipid molecules in fish species, as well as fatty acid composition and location distribution. This review is to cover the recent proteomics, metabolomics, and lipidomics studies on aquatic and seafood products in the areas of quality, safety, processing, and breeding of fish.

Worry and rumination elicit similar neural representations: neuroimaging evidence for repetitive negative thinking.

Repetitive negative thinking (RNT) captures shared cognitive and emotional features of content-specific cognition, including future-focused worry and past-focused rumination. The degree to which these distinct but related processes recruit overlapping neural structures is undetermined, because most neuroscientific studies only examine worry or rumination in isolation. To address this, we developed a paradigm to elicit idiographic worries and ruminations during an fMRI scan in 39 young adults with a range of trait RNT scores. We measured concurrent emotion ratings and heart rate as a physiological metric of arousal. Multivariate representational similarity analysis revealed that regions distributed across default mode, salience, and frontoparietal control networks encode worry and rumination similarly. Moreover, heart rate did not differ between worry and rumination. Capturing the shared neural features between worry and rumination throughout networks supporting self-referential processing, memory, salience detection, and cognitive control provides novel empirical evidence to bolster cognitive and clinical models of RNT.

Dynamical response in an electromechanical arm driven by temperature-dependent neural circuit

The thermosensitive neurons can detect changes in external temperature, and the external physical information is converted into neural signals. The external temperature is translated into an equivalent electrical stimulus, and the excitability of these neurons is controlled by adjusting the external electrical signal. These neurons exhibit a certain degree of adaptability, inducing appropriate discharge patterns only when subjected to external stimuli within a specific temperature range. Therefore, thermosensitive neurons formed by temperature regulated neural circuits can be used to study signal processing of thermosensitive neurons and control of mechanical arm. A temperature sensing mechanical arm system is constructed based on neural circuits in this article, and the influence of temperature on the dynamic behaviors of a mechanical arm system regulated by thermosensitive neurons is studied. Numerical simulations are performed to research the effect of external stimulation current and temperature on pattern formation. The bifurcation analysis of the membrane potential reveals the conversion in electrical activity and pattern selection. The bifurcation analysis of membrane potential reveals the mode switching of electrical activity and its impact on the displacement of the mechanical arm. The results indicate that as the temperature changes, the membrane potential exhibits different discharge patterns, which in turn affect the maximum displacement of the mechanical arm. The coupling between neural circuits and mechanical arm promotes effective signal propagation in the system. It is found that there is an appropriate temperature range that allowed the mechanical arm of the coupled system to move steadily back and forth. The research results confirm that external temperature can affect the firing mode of neurons and control the displacement of the mechanical arm.

Detection of Nuclear RNA Decay Intermediates Using a Modified Oxford Nanopore RNA Sequencing Strategy.

The nuclear RNA exosome complex is crucial for noncoding RNA processing and RNA quality control in the nucleus. Identifying substrates and intermediates of RNA decay pathways, such as those mediated by the exosome complex using Oxford Nanopore sequencing can be difficult in part because a simple method to detect them has been lacking and also because some of these RNAs lack abundant poly(A) tails which are required for Oxford Nanopore-based sequencing. Here we describe an Oxford nanopore-based approach which can be used to identify long reads corresponding to precursors and products of nuclear exosome processing. We are able to observe accumulation of unprocessed snoRNAs, cleavage products of the yeast nuclear RNase III endonuclease Rnt1p when the nuclear exosome component Rrp6p is inactivated.

Stepwise Assembly of DNA Nanostructures in a Surface-Based Method.

Hierarchical assembly of DNA nanostructures has already led to superstructures of ever-increasing level of complexity. Processing control in building nanostructures hierarchically is desirable but remains underexplored. Here, we present the stepwise assembly of DNA origami nanostructures by a surface-based method. With solid support of magnetic beads or glass slides, we demonstrate hierarchical assembly of preformed DNA origami units to a number of superstructures. The anchoring of DNA constructs on the surface results in better programmability and controllability for DNA self-assembly, suggesting a potential for our surface-based strategy to become a general and standardized assembly methodology of DNA nanostructures and beyond.

The effects of embedded quizzes on self‐regulated processes and learning performance during a multimedia lesson

AbstractBackgroundPrevious research has demonstrated that quizzing can improve self‐regulation processes and learning performances. However, it remains unclear whether quizzes in multimedia material bring similar benefits, and whether interindividual differences such as working memory capacity (WMC) modulate quizzing effects.AimsThis study aimed to examine the effects of embedded quizzes (multiple quizzes within the learning sequence) with feedback during a multimedia learning sequence on self‐regulated processing (monitoring and control) and learning performances on repeated (material already tested) and new (untested) questions, and possible WMC moderation on performances.Sample and MethodsParticipants were 59 students who learned a multimedia neuroscience course module in either a no‐quiz or embedded quizzes condition. With regard to self‐regulated processing, control was assessed with eye‐tracking measures (fixation durations and transitions between texts and illustrations), and monitoring by comparing self‐reported judgements of learning with actual performances (calibration). At the end, students' WMC was measured, and learning performances were assessed with repeated and new questions.ResultsQuizzing improved learning performances on repeated and new questions, and enhanced monitoring (better calibration). Students with low WMC benefited more from quizzing for new questions than those with high WMC. However, contrary to our hypothesis, the embedded quizzes group allocated less time to the learning than the no‐quiz group.ConclusionsEmbedded quizzes are relevant tools to help students recalibrate their monitoring process and foster more effective self‐regulated learning. Results confirm that the use of quizzes can be recommended to improve the learning of multimedia material, especially among students with low WMC.

Synthetic translational coupling element for multiplexed signal processing and cellular control.

Repurposing natural systems to develop customized functions in biological systems is one of the main thrusts of synthetic biology. Translational coupling is a common phenomenon in diverse polycistronic operons for efficient allocation of limited genetic space and cellular resources. These beneficial features of translation coupling can provide exciting opportunities for creating novel synthetic biological devices. Here, we introduce a modular synthetic translational coupling element (synTCE) and integrate this design with de novo designed riboregulators,toehold switches. A systematic exploration of sequence domain variants for synTCEs led to the identification of critical design considerations for improving the system performance. Next, this design approach was seamlessly integrated into logic computations and applied to construct multi-output transcripts with well-defined stoichiometric control. This module was further applied to signaling cascades for combined signal transduction and multi-input/multi-output synthetic devices. Further, the synTCEs can precisely manipulate the N-terminal ends of output proteins, facilitating effective protein localization and cellular population control. Therefore, the synTCEs could enhance computational capability and applicability of riboregulators for reprogramming biological systems, leading to future applications in synthetic biology, metabolic engineering and biotechnology.