Search Results (71)

Disease-specific diversity in RNA transcripts stems from RNA splicing, ribosomal abnormalities, and other factors. However, the mechanisms underlying the regulation of rRNA expression in the nucleolus and mRNA expression in the cytoplasm during cancer and neuronal differentiation remain largely unknown. In this article, we review current knowledge and discuss the regulatory mechanisms of rRNA and mRNA expression in human diseases using the splicing model of PUF60 (poly(U) binding splicing factor 60)—also known as FUSE-binding protein-interacting repressor (FIR) (FUBP1-interacting repressor), RoBPI, SIAHBP1, and VRJS (Gene ID: 22827). Noncoding RNAs, much like coding RNAs, have been found to be translated into proteins with significant physiological functions. Splicing is also involved in dominant ORF RNAs implicated in the expression of both noncoding and coding RNAs. Here, we analyze recent findings regarding gene splicing, ribosome formation, and the determination of selected ORFs (dominant ORFs) in a system modeled on FIR splicing in two databases (RefSeq and ENSEMBL). rRNA transcription affects ribosomes, whereas mRNA expression and splicing affect the intracellular proteome. Our objective is to develop efficient methods for identifying biomarkers for disease diagnosis and therapeutic targets. In the field of cancer treatment, therapeutic drugs targeting intracellular signaling have proven effective. Full article

We present LazyNet, a compact one-step neural-ODE model for single-cell CRISPR activation/interference (A/I) that operates directly on two-snapshot (“pre → post”) measurements and yields parameters with clear mechanistic meaning. The core log–linear–exp residual block exactly represents multiplicative effects, so synergistic multi-locus responses appear as explicit components rather than opaque composites. On a 53k-cell × 18k-gene neuronal Perturb-seq matrix, a three-replica LazyNet ensemble trained under a matched 1 h budget achieved strong threshold-free ranking and competitive error (genome-wide r ≈ 0.67) while running on CPUs. For comparison, we instantiated transformer (scGPT-style) and state-space (RetNet/CellFM-style) architectures from random initialization and trained them from scratch on the same dataset and within the same 1 h cap on a GPU platform, without any large-scale pretraining or external data. Under these strictly controlled, low-data conditions, LazyNet matched or exceeded their predictive performance while using far fewer parameters and resources. A T-cell screen included only for generalization showed the same ranking advantage under the identical evaluation pipeline. Beyond prediction, LazyNet exposes directed, local elasticities; averaging Jacobians across replicas produces a consensus interaction matrix from which compact subgraphs are extracted and evaluated at the module level. The resulting networks show coherent enrichment against authoritative resources (large-scale co-expression and curated functional associations) and concordance with orthogonal GPX4-knockout proteomes, recovering known ferroptosis regulators and nominating testable links in a lysosomal–mitochondrial–immune module. These results position LazyNet as a practical option for from-scratch, low-data CRISPR A/I studies where large-scale pretraining of foundation models is not feasible. Full article

Astrocytes play a pivotal role in shaping synaptic function and in learning, memory, and emotion. Recent studies show that perisynaptic astrocytic processes form structured interactions with pre- and postsynaptic elements, which extends synaptic diversity beyond neuron–neuron connections. Accumulating evidence indicates that astrocytic Ca²⁺ signaling, gliotransmission, and local translation modulate synaptic efficacy and contribute to the formation and stabilization of memory traces. It is therefore essential to define how astrocytic microdomains, multisynaptic leaflet domains, and network-level ensembles cooperate to regulate circuit computation across space and time. Advances in super-resolution and volumetric in vivo imaging and spatial transcriptomics now enable detailed, cell-type- and compartment-specific analysis of astrocyte–synapse interactions in vivo. In this review, we highlight these approaches and synthesize classical and emerging mechanisms by which astrocytes read neuronal activity, write to synapses, and coordinate network states. We also discuss theoretical frameworks such as neuron–astrocyte associative memory models that formalize astrocytic calcium states as distributed substrates for storage and control. This integrated view provides new insight into the multicellular logic of memory and suggests paths toward understanding and treating neurological and psychiatric disorders. Full article

Prostate stem cell antigen (PSCA) is a Ly6/uPAR protein that targets neuronal nicotinic acetylcholine receptors (nAChRs). It exists in membrane-tethered and soluble forms, with the latter upregulated in Alzheimer’s disease. We hypothesize that PSCA may be linked to a wider spectrum of neurological diseases and could induce neuroinflammation. Indeed, PSCA expression is significantly upregulated in the brain of patients with multiple sclerosis, Huntington’s disease, Down syndrome, bipolar disorder, and HIV-associated dementia. To investigate PSCA’s structure, pharmacology, and inflammatory function, we produced a correctly folded water-soluble recombinant analog (ws-PSCA). In primary hippocampal neurons and astrocytes, ws-PSCA differently regulates secretion of inflammatory factors and adhesion molecules and induces pro-inflammatory responses by increasing TNFβ secretion. Heteronuclear NMR and ¹⁵N relaxation measurements reveal a classical β-structural three-finger fold with conformationally disordered loops II and III. Positive charge clustering on the molecular surface suggests the functional importance of ionic interactions by these loops. Electrophysiological studies in Xenopus oocytes point on ws-PSCA inhibition of α3β2-, high-, and low-sensitive variants of α4β2- (IC₅₀ ~50, 27, and 15 μM, respectively) but not α4β4-nAChRs, suggesting targeting of the β2 subunit. Ensemble docking and molecular dynamics simulations predict PSCA binding to high-sensitive α4β2-nAChR at α4/β2 and β2/β2 interfaces. Complexes are stabilized by ionic and hydrogen bonds between PSCA’s loops II and III and the primary and complementary receptor subunits, including glycosyl groups. This study gives new structural and functional insights into PSCA’s interaction with molecular targets and provides clues to understand its role in the brain function and mental disorders. Full article

Background/Objectives: Radiotherapy can cause severe and irreversible brain damage, including cognitive impairment, increased dementia risk, debilitating depression, and other neuropsychiatric disorders. Current radioprotective drugs face limitations, such as single-target inefficacy or manufacturing hurdles. Isoliquiritigenin (ISL), a natural flavonoid derived from licorice root, exhibits broad bioactivities. It exhibits anti-inflammatory, anti-cancer, immunoregulatory, hepatoprotective, and cardioprotective activities. This study aimed to elucidate ISL’s neuronal radiation mitigation effects and key targets. Methods: In vitro and in vivo models of radiation-induced neuronal injury were established. ISL’s bioactivities were evaluated through cellular cytotoxicity assays, LDH release, ROS, ATP, glutamate, and GSH levels. In vivo, ISL’s radiation mitigation effect was evaluated with sucrose preference test, IL-β level, histopathological analysis, and Golgi-Cox staining analysis. Proteomics, pathway enrichment, and ensemble models (four machine learning models, weighted gene co-expression network, protein–protein interaction) identified core targets. Molecular docking and dynamic simulations validated ISL’s binding stability with key targets. Results: ISL attenuated radiation-induced cellular cytotoxicity, reduced LDH/ROS, restored ATP, elevated GSH, and mitigated glutamate accumulation. In rats, ISL alleviated anhedonia-like phenotypes and hippocampal synaptic loss. ISL also significantly suppressed radiation-induced neuroinflammation, as evidenced by reduced levels of the pro-inflammatory cytokine IL-1β. Proteomic analysis revealed that ISL’s main protective pathways included the synaptic vesicle cycle, glutamatergic synapse, MAPK signaling pathway, SNARE interactions in vesicular transport, insulin signaling pathway, and insulin secretion. Grm8, Grik3, and Grin3a were identified as key targets using the integrated models. The expression of these targets was upregulated post-radiation and restored by ISL. Molecular docking and dynamic simulations indicated that ISL showed stable binding to these receptors compared to native ligands. Conclusions: ISL demonstrates multi-scale radiation mitigation activities in vitro and in vivo by modulating synaptic and inflammatory pathways, with glutamate receptors as core targets. This work nominates ISL as an important natural product for mitigating radiotherapy-induced neural damage. Full article

Background: The red nucleus (RN) is a phylogenetically conserved structure within the midbrain that is traditionally associated with general motor coordination; however, its specific role in controlling directional movement remains poorly understood. Methods: This study systematically investigates the function and mechanism of RN neurons in directional movement by combining stereotactic brain injections, fiber photometry recordings, multi-unit in vivo electrophysiological recordings, optogenetic manipulation, and anterograde trans-synaptic tracing. Results: We analyzed mice performing standardized T-maze turning tasks and revealed that anatomically distinct RN neuronal ensembles exhibit direction-selective activity patterns. These neurons demonstrate preferential activation during ipsilateral turning movements, with activity onset consistently occurring after movement initiation. We establish a causal relationship between RN neuronal activity and directional motor control: selective activation of RN glutamatergic neurons facilitates ipsilateral turning, whereas temporally precise inhibition significantly impairs the execution of these movements. Anterograde trans-synaptic tracing using H129 reveals that RN neurons selectively project to spinal interneuron populations responsible for ipsilateral flexion and coordinated limb movements. Conclusions: These findings offer a framework for understanding asymmetric motor control in the brain. This work redefines the RN as a specialized hub within midbrain networks that mediate lateralized movements and offers new avenues for neuromodulatory treatments for neurodegenerative and post-injury motor disorders. Full article

Investigating brain area functions requires advanced technologies, but meaningful insights depend on correlating neural signals with behavior. Traditional mice behavior annotation methods, including manual and semi-automated approaches, are limited by subjectivity and time constraints. To overcome these limitations, our study employs the YOLO neural network for precise mice tracking and composite RGB frames for behavioral scoring. Our model, trained on over 10,000 frames, accurately classifies sitting, running, and grooming behaviors. Additionally, we provide statistical metrics and data visualization tools. We further combined AI-powered behavior labeling to examine hippocampal neuronal activity using fluorescence microscopy. To analyze neuronal circuit dynamics, we utilized a manifold analysis approach, revealing distinct functional patterns corresponding to transgenic 5xFAD Alzheimer’s model mice. This open-source software enhances the accuracy and efficiency of behavioral and neural data interpretation, advancing neuroscience research. Full article

Accurately forecasting the degradation trajectory of proton exchange membrane fuel cells (PEMFCs) across a spectrum of operational scenarios is indispensable for effective maintenance scheduling and robust health surveillance. However, this task is highly intricate due to the fluctuating nature of dynamic operating conditions and the limitations inherent in short-term forecasting techniques, which collectively pose significant challenges to achieving reliable predictions. To enhance the accuracy of PEMFC degradation forecasting, this research proposes an integrated approach that combines the complete ensemble empirical mode decomposition with the variational mode decomposition (CEEMD-VMD) and triple echo state network (TriESN) to predict the deterioration process precisely. Decomposition can filter out high-frequency noise and retain low-frequency degradation information effectively. Among data-driven methods, the echo state network (ESN) is capable of estimating the degradation performance of PEMFCs. To tackle the problem of low prediction accuracy, this study proposes a novel TriESN that builds upon the classical ESN. The proposed enhancement method seeks to refine the ESN architecture by reducing the impact of surrounding neurons and sub-reservoirs on active neurons, thus realizing partial decoupling of the ESN. On this basis of decoupling, the method takes into account the multi-timescale aging characteristics of PEMFCs to achieve precise prediction of remaining useful life. Overall, combining CEEMD-VMD with the TriESN strengthens feature depiction, fosters sparsity, diminishes the likelihood of overfitting, and augments the network’s capacity for generalization. It has been shown that the TriESN markedly improved the accuracy of long-term PEMFC degradation predictions in three different dynamic contexts. Full article

The paraventricular nucleus of the hypothalamus (PVN) regulates, among others, the stress response, sexual behavior, and energy metabolism through its magnocellular and parvocellular neurosecretory cells. Within the PVN, ensemble coordination occurs through the many long-range synaptic afferents, whose activity in time relies on retrograde neuromodulation by, e.g., endocannabinoids. However, the nanoarchitecture of endocannabinoid signaling in the PVN, especially during neuronal development, remains undescribed. By using single-cell RNA sequencing, in situ hybridization, and immunohistochemistry during fetal and postnatal development in mice, we present a spatiotemporal map of both the 2-arachidonoylglycerol (2-AG) and anandamide (AEA) signaling cassettes, with a focus on receptors and metabolic enzymes, in both molecularly defined neurons and astrocytes. We find type 1 cannabinoid receptors (Cnr1), but neither Cnr2 nor Gpr55, expressed in neurons of the PVN. Dagla and Daglb, which encode the enzymes synthesizing 2-AG, were found in all neuronal subtypes of the PVN, with a developmental switch from Daglb to Dagla. Mgll, which encodes an enzyme degrading 2-AG, was only found sporadically. Napepld and Faah, encoding enzymes that synthesize and degrade AEA, respectively, were sparsely expressed in neurons throughout development. Notably, astrocytes expressed Mgll and both Dagl isoforms. In contrast, mRNA for any of the three major cannabinoid-receptor subtypes could not be detected. Immunohistochemistry validated mRNA expression and suggested that endocannabinoid signaling is configured to modulate the activity of afferent inputs, rather than local neurocircuits, in the PVN. Full article

The SCN9A gene, a critical regulator of pain perception, encodes the voltage-gated sodium channel Nav1.7, a key mediator of pain signal transmission. This study conducts a multimodal assessment of SCN9A, integrating genetic variation, structural architecture, and molecular dynamics to elucidate its role in pain regulation. Using advanced computational methods, I-TASSER simulations generated structural decoys of the SCN9A homology domain, producing an ensemble of conformational states. SPICKER clustering identified five representative models with a C-score of −3.19 and TM-score of 0.36 ± 0.12, reflecting moderate structural similarity to experimental templates while highlighting deviations that may underpin functional divergence. Validation via ProSA-web supported model reliability, yielding a Z-score of −1.63, consistent with native-like structures. Central to the analysis was the R1150W non-synonymous variant, a potential pathogenic variant. Structural modeling revealed localized stability in the mutant conformation but disrupted hydrogen bonding and altered charge distribution. Its pathogenicity was underscored by a high MetaRNN score (0.7978498) and proximity to evolutionarily conserved regions, suggesting functional importance. Notably, the variant lies within the Sodium-Ion-Transport-Associated Domain, where perturbations could impair ion conductance and channel gating—mechanisms critical for neuronal excitability. These findings illuminate how SCN9A variants disrupt pain signaling, linking genetic anomalies to molecular dysfunction. While computational insights advance mechanistic understanding, experimental validation is essential to confirm the variant’s impact on Nav1.7 dynamics and cellular physiology. By refining SCN9A’s molecular blueprint and highlighting its therapeutic potential as a target for precision analgesics, this work provides a roadmap for mitigating pain-related disorders through channel-specific modulation. Integrating structural bioinformatics with functional genomics, this study deciphers SCN9A’s role in pain biology, laying the groundwork for novel strategies to manage pathological pain. Full article

Theories of consciousness grounded in neuroscience must explain the phenomenal binding problem, e.g., how micro-units of information are combined to create the macro-scale conscious experience common to human phenomenology. An example is how single ‘pixels’ of a visual scene are experienced as a single holistic image in the ‘mind’s eye’, rather than as individual, separate, and massively parallel experiences, corresponding perhaps to individual neuron activations, neural ensembles, or foveal saccades, any of which could conceivably deliver identical functionality from an information processing point of view. There are multiple contested candidate solutions to the phenomenal binding problem. This paper explores how the metaphysical infrastructure of Integrated Information Theory (IIT) v4.0 can provide a distinctive solution. The solution—that particular entities aggregable from multiple units (‘complexes’) define existence—might work in a static picture, but introduces issues in a dynamic system. We ask what happens to our phenomenal self as the main complex moves around a biological neural network. Our account of conscious entities developing through time leads to an apparent dilemma for IIT theorists between non-local entity transitions and contiguous selves: the ‘dynamic entity evolution problem’. As well as specifying the dilemma, we describe three ways IIT might dissolve the dilemma before it gains traction. Clarifying IIT’s position on the phenomenal binding problem, potentially underpinned with novel empirical or theoretical research, helps researchers understand IIT and assess its plausibility. We see our paper as contributing to IIT’s current research emphasis on the shift from static to dynamic analysis. Full article

The main object of our study is travelling waves in vast neuronal ensembles modelled using neural field equations. We obtained conditions that guarantee the existence of travelling wave solutions and their continuous dependence under the transition from sigmoidal neuronal activation functions to the Heaviside activation function. We, thus, filled the gap between the continuous and the discontinuous approaches to the formalization of the neuronal activation process in studies of travelling waves. We provided conditions for admissibility to operate with simple closed-form expressions for travelling waves, as well as to significantly simplify their numerical investigation. This opens the possibilities of linking characteristics of cortical travelling waves, e.g., the wave shape and the wave speed, to the physiological parameters of the neural medium, e.g., the lengths and the strengths of neuronal connections and the neuronal activation thresholds, in the framework of the neural field theory. Full article

Deep connectionist models are characterized by many neurons grouped together in many successive layers. As a result, their data classifications are difficult to understand. We present two novel algorithms which explain the responses of several black-box machine learning models. The first is Fidex, which is local and thus applied to a single sample. The second, called FidexGlo, is global and uses Fidex. Both algorithms generate explanations by means of propositional rules. In our framework, the discriminative boundaries are parallel to the input variables and their location is precisely determined. Fidex is a heuristic algorithm that, at each step, establishes where the best hyperplane is that has increased fidelity the most. The algorithmic complexity of Fidex is proportional to the maximum number of steps, the number of possible hyperplanes, which is finite, and the number of samples. We first used FidexGlo with ensembles and support vector machines (SVMs) to show that its performance on three benchmark problems is competitive in terms of complexity, fidelity and accuracy. The most challenging part was then to apply it to convolutional neural networks. We achieved this with three classification problems based on images. We obtained accurate results and described the characteristics of the rules generated, as well as several examples of explanations illustrated with their corresponding images. To the best of our knowledge, this is one of the few works showing a global rule extraction technique applied to both ensembles, SVMs and deep neural networks. Full article

Plate heat exchangers have the advantages of high heat transfer coefficients and compact structures, and they are widely used in aerospace, nuclear power, and other fields. Nevertheless, several scalability challenges have emerged during the utilization process. If not addressed promptly, the issue will reduce heat transfer efficiency, consequently causing energy waste, diminished production capacity, and a shortened lifespan. In this study, we employed the long short-term memory (LSTM) algorithm model and the multi-layer perceptron (MLP) algorithm model to monitor the health status of plate heat exchangers. This was achieved by fine-tuning the hidden layers and neurons of the models. The individual model exhibiting the highest prediction accuracy was incorporated into a more sophisticated ensemble model to monitor the health status of plate heat exchangers. The study revealed that the MLP 2 × 64 + LSTM 2 × 64 model achieved the highest prediction accuracy, scoring 0.9942. According to the simulation program for plate heat exchangers, the fouling thermal resistance was determined to be 0.0003 m²·K/W when the heat exchange efficiency decreased by 50%. An early warning threshold was established within the health condition value (HCV), triggering an alert when the heat transfer efficiency of the plate heat exchanger fell below 50%. Combining the LSTM and MLP algorithms provides new ideas and technical support for the health assessment and maintenance of plate heat exchangers. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article