Explore the vast range of titles available.

The number and activity of Cav1.2 channels in the cardiomyocyte sarcolemma tunes the magnitude of Ca2+-induced Ca2+ release and myocardial contraction. β-Adrenergic receptor (βAR) activation stimulates sarcolemmal insertion of Cav1.2. This supplements the preexisting sarcolemmal Cav1.2 population, forming large “superclusters” wherein neighboring channels undergo enhanced cooperative-gating behavior, amplifying Ca2+ influx and myocardial contractility. Here, we determine this stimulated insertion is fueled by an internal reserve of early and recycling endosome-localized, presynthesized Cav1.2 channels. βAR-activation decreased Cav1.2/endosome colocalization in ventricular myocytes, as it triggered “emptying” of endosomal Cav1.2 cargo into the t-tubule sarcolemma. We examined the rapid dynamics of this stimulated insertion process with live-myocyte imaging of channel trafficking, and discovered that Cav1.2 are often inserted into the sarcolemma as preformed, multichannel clusters. Similarly, entire clusters were removed from the sarcolemma during endocytosis, while in other cases, a more incremental process suggested removal of individual channels. The amplitude of the stimulated insertion response was doubled by coexpression of constitutively active Rab4a, halved by coexpression of dominant-negative Rab11a, and abolished by coexpression of dominant-negative mutant Rab4a. In ventricular myocytes, βAR-stimulated recycling of Cav1.2 was diminished by both nocodazole and latrunculin-A, suggesting an essential role of the cytoskeleton in this process. Functionally, cytoskeletal disruptors prevented βAR-activated Ca2+ current augmentation. Moreover, βAR-regulation of Cav1.2 was abolished when recycling was halted by coapplication of nocodazole and latrunculin-A. These findings reveal that βAR-stimulation triggers an on-demand boost in sarcolemmal Cav1.2 abundance via targeted Rab4a- and Rab11a-dependent insertion of channels that is essential for βAR-regulation of cardiac Cav1.2.

SignificanceOur data challenge the generally accepted view that Kv2.1 proteins regulate arterial smooth muscle function by regulating their membrane potential. Rather, we discovered that Kv2.1 plays both conductive and structural roles with opposing functional consequences on arterial myocytes, with the former predominating in males, the latter in females. Opening of Kv2.1 channels opposes vasoconstriction by inducing membrane hyperpolarization. In addition to this conductive function, Kv2.1 promotes the structural clustering of CaV1.2 channels, thereby enhancing Ca2+ influx and inducing vasoconstriction. These two functions are highlighted by differences in the regulation of membrane potential, intracellular Ca2+, and myogenic tone between males and females. Our data suggest that these disparities derive from sex-specific variations in Kv2.1 expression levels in male versus female myocytes. The accepted role of the protein Kv2.1 in arterial smooth muscle cells is to form K+ channels in the sarcolemma. Opening of Kv2.1 channels causes membrane hyperpolarization, which decreases the activity of L-type CaV1.2 channels, lowering intracellular Ca2+ ([Ca2+]i) and causing smooth muscle relaxation. A limitation of this model is that it is based exclusively on data from male arterial myocytes. Here, we used a combination of electrophysiology as well as imaging approaches to investigate the role of Kv2.1 channels in male and female arterial myocytes. We confirmed that Kv2.1 plays a canonical conductive role but found it also has a structural role in arterial myocytes to enhance clustering of CaV1.2 channels. Less than 1% of Kv2.1 channels are conductive and induce membrane hyperpolarization. Paradoxically, by enhancing the structural clustering and probability of CaV1.2–CaV1.2 interactions within these clusters, Kv2.1 increases Ca2+ influx. These functional impacts of Kv2.1 depend on its level of expression, which varies with sex. In female myocytes, where expression of Kv2.1 protein is higher than in male myocytes, Kv2.1 has conductive and structural roles. Female myocytes have larger CaV1.2 clusters, larger [Ca2+]i, and larger myogenic tone than male myocytes. In contrast, in male myocytes, Kv2.1 channels regulate membrane potential but not CaV1.2 channel clustering. We propose a model in which Kv2.1 function varies with sex: in males, Kv2.1 channels control membrane potential but, in female myocytes, Kv2.1 plays dual electrical and CaV1.2 clustering roles. This contributes to sex-specific regulation of excitability, [Ca2+]i, and myogenic tone in arterial myocytes.

Fine-grained visual classification aims to identify images that belong to multiple subcategories within the same category. This is a challenging task as there are only subtle regional differences between subcategories. Most of the existing methods utilize neural networks to extract global image features and quickly lock local feature regions by adding various external attention mechanisms. This type of approach may ignore the details that are inherent in the feature map itself. This paper proposes an efficient global channel position-aware interaction method to solve this problem. Specifically, we first hierarchically group the original features and take advantage of the translation-invariant linearity and local weight sharing of convolutional networks to propose a hierarchical structure that enhances the receptive field of global features. Then, same-direction location attention interaction is performed based on the global feature with rich fields of view, thus encouraging the model to capture its common areas of interest according to the feature’s own learning ability. Finally, multiple attention feature map is obtained based on the relative position interactions of the global features. We again use convolutional networks to learn the discriminative features of the attention target regions and perform feature clustering optimization on the discriminative feature regions to guide the classification process. The proposed model performs well on three datasets, i.e. CUB-200-2011, Stanford Cars, and FGVC Aircraft.

Electroencephalography (EEG) provides a non-invasive and high-temporal-resolution modality for decoding cognitive states, but high-density recordings remain challenging for Transformer-based models because self-attention scales quadratically with the number of channels. In addition, conventional Euclidean representations do not fully capture the intrinsic geometry of EEG covariance features, which may limit robustness in cross-subject settings. To address these issues, we propose EEG-RCformer, a Riemannian geometry-informed channel clustering Transformer for EEG decoding. The model first computes per-channel symmetric positive definite (SPD) covariance matrices from windowed EEG features and uses the affine-invariant Riemannian metric (AIRM) to identify trial-specific functional hubs. These hubs are then integrated with capacity-constrained spatial clustering to generate anatomically plausible and computationally efficient channel groups, which are encoded as tokens for a Transformer classifier. We evaluated EEG-RCformer on the MODMA and SEED datasets under both subject-dependent and -independent paradigms, achieving area under the curve (AUC) values of 0.9802 and 0.7154 on MODMA and 0.8541 and 0.8011 on SEED, respectively. Paired statistical tests further showed significant gains for MODMA in both the subject-dependent and -independent settings and for SEED in the subject-dependent setting, while SEED still showed a positive but non-significant mean improvement in the subject-independent setting.

In recent years, it has become clear that intrinsically disordered protein segments play diverse functional roles in many cellular processes, thus leading to a reassessment of the classical structure–function paradigm. One class of intrinsically disordered protein segments is entropic clocks, corresponding to unstructured random protein chains involved in timing cellular processes. Such clocks were shown to modulate ion channel processes underlying action potential generation, propagation, and transmission. In this review, we survey the role of entropic clocks in timing intra- and inter-molecular binding events of voltage-activated potassium channels involved in gating and clustering processes, respectively, and where both are known to occur according to a similar ‘ball and chain’ mechanism. We begin by delineating the thermodynamic and timing signatures of a ‘ball and chain’-based binding mechanism involving entropic clocks, followed by a detailed analysis of the use of such a mechanism in the prototypical Shaker voltage-activated K+ channel model protein, with particular emphasis on ion channel clustering. We demonstrate how ‘chain’-level alternative splicing of the Kv channel gene modulates entropic clock-based ‘ball and chain’ inactivation and clustering channel functions. As such, the Kv channel model system exemplifies how linkage between alternative splicing and intrinsic disorder enables the functional diversity underlying changes in electrical signaling.

This study investigates the dynamic interplay between student engagement and self-regulated learning (SRL) in an online project management course. Using learning management system trace data combined with longitudinal self-reported SRL measures, we analysed data from 165 first-year business students through multi-channel sequencing and clustering algorithms. Results revealed three distinct patterns of learning behaviour with significant implications for intervention design: Low-regulating improvers (4.85%) who showed potential for growth with targeted support, Disengaged low regulators (44.85%) requiring comprehensive intervention in both engagement and self-regulation skills, and High-regulating improvers (50.30%) who demonstrated successful adaptation to online learning. Our innovative approach of analysing student progress individually rather than using fixed time points enabled more precise identification of support needs in asynchronous learning settings. The findings demonstrate that both engagement and self-regulation skills can develop over time with appropriate support, suggesting opportunities for adaptive intervention throughout the course. This research provides actionable insights for developing targeted support strategies, including early warning systems for at-risk students and personalized scaffolding approaches based on students' behavioural patterns. These findings advance learning analytics by providing a framework for real-time identification of student needs and evidence-based intervention design in online education. The results particularly emphasize the importance of continuous, pattern-based support for developing self-regulation skills in online environments.

KcsA, a prokaryote tetrameric potassium channel, was the first ion channel ever to be structurally solved at high resolution. This, along with the ease of its expression and purification, made KcsA an experimental system of choice to study structure–function relationships in ion channels. In fact, much of our current understanding on how the different channel families operate arises from earlier KcsA information. Being an integral membrane protein, KcsA is also an excellent model to study how lipid–protein and protein–protein interactions within membranes, modulate its activity and structure. In regard to the later, a variety of equilibrium and non-equilibrium methods have been used in a truly multidisciplinary effort to study the effects of lipids on the KcsA channel. Remarkably, both experimental and “in silico” data point to the relevance of specific lipid binding to two key arginine residues. These residues are at non-annular lipid binding sites on the protein and act as a common element to trigger many of the lipid effects on this channel. Thus, processes as different as the inactivation of channel currents or the assembly of clusters from individual KcsA channels, depend upon such lipid binding.

Wireless sensor network is a network consisting of many sensor nodes that function to scan certain phenomena around it. WSN has quite a large problem in the form of delay and data loss which results in low WSN performance. This study aims to improve WSN performance by developing a cluster-based routing protocol. The cluster formation is carried out in several stages. The first is the formation of the cluster head which is the channel reference to be used by node members by means of probability calculations. The second determines the closest node using the Euclidean approach when looking for the closest member of the node to the cluster head. The third is determination of the node members by means of single linkage grouping by looking for proximity to CH. The performance of the proposed MCCH method is then tested and evaluated using QoS parameters. The results of this research evaluation use QoS parameters for testing the MCCH method, channel 1 throughput 508.165, channel 2 throughput 255.5661, channel 3 throughput 479.8289, channel 4 throughput 646.5618.

This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive deterioration of cognitive functions. Cortical and hippocampal hyperexcitability intervenes in the pathological derangement of brain activity leading to cognitive decline. As key regulators of neuronal excitability, the voltage-gated K+ channels (KV) might play a crucial role in the AD pathophysiology. Among them, the KV2.1 channel, the main α subunit mediating the delayed rectifier K+ currents (IDR) and controlling the intrinsic excitability of pyramidal neurons, has been poorly examined in AD. In the present study, we investigated the KV2.1 protein expression and activity in hippocampal neurons from the Tg2576 mouse, a widely used transgenic model of AD. To this aim we performed whole-cell patch-clamp recordings, Western blotting, and immunofluorescence analyses. Our Western blotting results reveal that KV2.1 was overexpressed in the hippocampus of 3-month-old Tg2576 mice and in primary hippocampal neurons from Tg2576 mouse embryos compared with the WT counterparts. Electrophysiological experiments unveiled that the whole IDR were reduced in the Tg2576 primary neurons compared with the WT neurons, and that this reduction was due to the loss of the KV2.1 current component. Moreover, we found that the reduction of the KV2.1-mediated currents was due to increased channel clustering, and that glutamate, a stimulus inducing KV2.1 declustering, was able to restore the IDR to levels comparable to those of the WT neurons. These findings add new information about the dysregulation of ionic homeostasis in the Tg2576 AD mouse model and identify KV2.1 as a possible player in the AD-related alterations of neuronal excitability.