Channel Domain Research Articles

An attentive-based generative model for medical image synthesis

Magnetic resonance (MR) and computer tomography (CT) imaging are valuable tools for diagnosing diseases and planning treatment. However, limitations such as radiation exposure and cost can restrict access to certain imaging modalities. To address this issue, medical image synthesis can generate one modality from another, but many existing models struggle with high-quality image synthesis when multiple slices are present in the dataset. This study proposes an attention-based dual contrast generative model, called ADC-cycleGAN, which can synthesize medical images from unpaired data with multiple slices. The model integrates a dual contrast loss term with the CycleGAN loss to ensure that the synthesized images are distinguishable from the source domain. Additionally, an attention mechanism is incorporated into the generators to extract informative features from both channel and spatial domains. To improve performance when dealing with multiple slices, the $K$-means algorithm is used to cluster the dataset into $K$ groups, and each group is used to train a separate ADC-cycleGAN. Experimental results demonstrate that the proposed ADC-cycleGAN model produces comparable samples to other state-of-the-art generative models, achieving the highest PSNR and SSIM values of 19.04385 and 0.68551, respectively. We publish the code at https://github.com/JiayuanWang-JW/ADC-cycleGAN.

Hybrid Domain Consistency Constraints-Based Deep Neural Network for Facial Expression Recognition.

Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based on a feature constraint method that combines both spatial domain consistency and channel domain consistency. Specifically, first, the proposed HDCNet mines the potential attention consistency feature expression (different from manual features, e.g., HOG and SIFT) as effective supervision information by comparing the original sample image with the augmented facial expression image. Second, HDCNet extracts facial expression-related features in the spatial and channel domains, and then it constrains the consistent expression of features through the mixed domain consistency loss function. In addition, the loss function based on the attention-consistency constraints does not require additional labels. Third, the network weights are learned to optimize the classification network through the loss function of the mixed domain consistency constraints. Finally, experiments conducted on the public RAF-DB and AffectNet benchmark datasets verify that the proposed HDCNet improved classification accuracy by 0.3-3.84% compared to the existing methods.

Ensemble Multiple Distinct ResNet Networks With Channel-Attention Mechanism for Multisensor Fault Diagnosis of Hydraulic Systems

Hydraulic systems are widely used in modern industries, and the study of efficient and accurate fault diagnosis techniques for hydraulic systems can help prevent accidents and reduce economic losses. Most of the existing deep learning-based intelligent diagnosis methods use only single-channel signals for diagnosis, which may ignore some important fault information. In addition, the existing deep learning algorithms cannot distinguish the sensitivity of inter-channel features, and the important features of the channel domain cannot be emphasized. To solve the above problems, this paper proposes a multi-channel data-driven framework integrated distinct ResNet networks with channel-attention mechanism (CM-ResNet) for fault diagnosis of hydraulic systems. Firstly, the multi-channel signals are converted into 2-dimensional feature maps by continuous wavelet transform. Then, multiple CM-ResNet are trained as base-learners based on the signals of each channel. Third, the category probabilities of each base-learner are concatenated into new feature vectors. Finally, the new feature vectors are used to train the meta-learner and perform fault diagnosis. The meta-learner can capture the complex nonlinear relationships between the base-learners and obtain a stronger learner. Experimental results show that the performance of our proposed method outperforms the comparison method and the already existing methods.

Xenon's Sedative Effect Is Mediated by Interaction with the Cyclic Nucleotide-Binding Domain (CNBD) of HCN2 Channels Expressed by Thalamocortical Neurons of the Ventrobasal Nucleus in Mice.

Previous studies have shown that xenon reduces hyperpolarization-activated cyclic nucleotide-gated channels type-2 (HCN2) channel-mediated current (Ih) amplitude and shifts the half-maximal activation voltage (V1/2) in thalamocortical circuits of acute brain slices to more hyperpolarized potentials. HCN2 channels are dually gated by the membrane voltage and via cyclic nucleotides binding to the cyclic nucleotide-binding domain (CNBD) on the channel. In this study, we hypothesize that xenon interferes with the HCN2 CNBD to mediate its effect. Using the transgenic mice model HCN2EA, in which the binding of cAMP to HCN2 was abolished by two amino acid mutations (R591E, T592A), we performed ex-vivo patch-clamp recordings and in-vivo open-field test to prove this hypothesis. Our data showed that xenon (1.9 mM) application to brain slices shifts the V1/2 of Ih to more hyperpolarized potentials in wild-type thalamocortical neurons (TC) (V1/2: -97.09 [-99.56--95.04] mV compared to control -85.67 [-94.47--82.10] mV; p = 0.0005). These effects were abolished in HCN2EA neurons (TC), whereby the V1/2 reached only -92.56 [-93.16- -89.68] mV with xenon compared to -90.03 [-98.99--84.59] mV in the control (p = 0.84). After application of a xenon mixture (70% xenon, 30% O2), wild-type mice activity in the open-field test decreased to 5 [2-10] while in HCN2EA mice it remained at 30 [15-42]%, (p = 0.0006). In conclusion, we show that xenon impairs HCN2 channel function by interfering with the HCN2 CNBD site and provide in-vivo evidence that this mechanism contributes to xenon-mediated hypnotic properties.

A Lightweight Attentional Shift Graph Convolutional Network for Skeleton-Based Action Recognition

In the field of skeleton-based human behavior recognition, graph convolutional neural networks have made remarkable achievements. However, high precision networks are often accompanied by numerous parameters and computational cost, and their application in mobile devices has considerable limitations. Aiming at the problem of excessive spatiotemporal complexity of high-accuracy methods, this paper further analyzes the lightweight human action recognition model and proposes a lightweight architecture attentional shift graph convolutional network. There are three main improvements in this model. Firstly, shift convolution is a lightweight convolution method that can be combined with graph convolution to effectively reduce its complexity. At the same time, a shallow architecture for multi-stream early fusion is designed to reduce the network scale by merging multi-stream networks and reducing the number of network layers. In addition, the efficient channel attention module is introduced into the model to capture the underlying characteristic information in the channel domain. Experiments are conducted on the three existing skeleton datasets, NTU RGB+D, NTU-120 RGB+D, and Northwestern-UCLA. Results demonstrate that the proposed model is not only competitive in accuracy, but also outperforms current mainstream methods in parameter count and computational cost, and supports running in some devices with limited computing and storage resources.

A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images

Current deep learning-based change detection approaches mostly produce convincing results by introducing attention mechanisms to traditional convolutional networks. However, given the limitation of the receptive field, convolution-based methods fall short of fully modelling global context and capturing long-range dependencies, thus insufficient in discriminating pseudo changes. Transformers have an efficient global spatio-temporal modelling capability, which is beneficial for the feature representation of changes of interest. However, the lack of detailed information may cause the transformer to locate the boundaries of changed regions inaccurately. Therefore, in this article, a hybrid CNN-transformer architecture named CTCANet, combining the strengths of convolutional networks, transformer, and attention mechanisms, is proposed for high-resolution bi-temporal remote sensing image change detection. To obtain high-level feature representations that reveal changes of interest, CTCANet utilizes tokenizer to embed the features of each image extracted by convolutional network into a sequence of tokens, and the transformer module to model global spatio-temporal context in token space. The optimal bi-temporal information fusion approach is explored here. Subsequently, the reconstructed features carrying deep abstract information are fed to the cascaded decoder to aggregate with features containing shallow fine-grained information, through skip connections. Such an aggregation empowers our model to maintain the completeness of changes and accurately locate small targets. Moreover, the integration of the convolutional block attention module enables the smoothing of semantic gaps between heterogeneous features and the accentuation of relevant changes in both the channel and spatial domains, resulting in more impressive outcomes. The performance of the proposed CTCANet surpasses that of recent certain state-of-the-art methods, as evidenced by experimental results on two publicly accessible datasets, LEVIR-CD and SYSU-CD.

PO-01-178 MAPPING OF PATHOGENIC VARIANT HOTSPOTS ON THE STRUCTURE OF RYANODINE RECEPTOR TYPE 2 DRIVES A RATIONAL DRUG DESIGN APPROACH FOR CATECHOLAMINERGIC POLYMORPHIC VENTRICULAR TACHYCARDIA

Heritable mutations in RYR2 cause sudden-death predisposing arrhythmia syndromes, such as catecholaminergic polymorphic ventricular tachycardia (CPVT), through increased sarcoplasmic reticulum (SR) diastolic Ca2+ leak. RYR2-encoded ryanodine receptor 2 is an intracellular transmembrane Ca2+ release channel in the SR of cardiomyocytes. Even on optimal medical therapy, breakthrough life-threatening arrhythmias in patients with CPVT are common. We sought to correlate identified pathogenic hotspots with key functional domains on the 3D structure of RyR2 and possible mobile binding sites to determine small molecule targets for reducing SR Ca2+ leak. We performed “signal” to “noise” (S:N) analysis in RYR2 to establish pathogenic hotspots. The minor allele frequency (MAF) of literature-derived disease-associated genetic variants at a given amino acid locus was normalized to the MAF of rare population-based variants in Genome Aggregation Database (gnomAD). Using PyMOL, a heat map overlay was generated using S:N levels and possible molecular binding pockets were identified using software PyMOL and Maestro. The highest cumulative S:N intensity of pathogenic hotspots of each domain localized on the channel domain (28.2%), helical domain 1 (25.6%), central domain (17.7%), and N-terminal domain (16.5%) of RyR2. The SPRY1 (0.498%), P1 (0.147%), handle domain (2.32%), and central U motif (8.88%) had lower S:N. Cumulative S:N intensities were nonexistent for SPRY2, SPRY3, P2, and helical domain 2. Putative small molecule binding pockets were identified in all domains while the largest number localized to the channel domain. Once binding pockets were combined with the heat map structure of RyR2, activator binding pockets of low S:N threshold in the open state of RyR2 were shown to be located heavily on the P2 domain, where CaMKII and PKA binds. The highest S:N intensity was in the channel domain with high S:N levels in other proximal domains localizing near the SR transmembrane in the lumen. Mobile binding pockets between the open and closed state of RyR2 with low S:N intensity may be alternative areas for drug targeting, especially on domains that contain binding pockets for activators of RYR2.

Cyclic nucleotide-regulated channels (CNG) in GtoPdb v.2023.1

Cyclic nucleotide-gated (CNG) channels are responsible for signalling in the primary sensory cells of the vertebrate visual and olfactory systems. CNG channels are voltage-independent cation channels formed as tetramers. Each subunit has 6TM, with the pore-forming domain between TM5 and TM6. CNG channels were first found in rod photoreceptors [83, 120], where light signals through rhodopsin and transducin to stimulate phosphodiesterase and reduce intracellular cyclic GMP level. This results in a closure of CNG channels and a reduced ‘dark current’. Similar channels were found in the cilia of olfactory neurons [181] and the pineal gland [71]. The cyclic nucleotides bind to a domain in the C terminus of the subunit protein: other channels directly binding cyclic nucleotides include hyperolarisation-activated, cyclic nucleotide-gated channels (HCN), ether-a-go-go and certain plant potassium channels.The HCN channels are cation channels that are activated by hyperpolarisation at voltages negative to ~-50 mV. The cyclic nucleotides cyclic AMP and cyclic GMP directly bind to the cyclic nucleotide-binding domain of HCN channels and shift their activation curves to more positive voltages, thereby enhancing channel activity. HCN channels underlie pacemaker currents found in many excitable cells including cardiac cells and neurons [65, 192]. In native cells, these currents have a variety of names, such as Ih, Iq and If. The four known HCN channels have six transmembrane domains and form tetramers. It is believed that the channels can form heteromers with each other, as has been shown for HCN1 and HCN4 [2]. High resolution structural studies of CNG and HCN channels has provided insight into the the gating processes of these channels [139, 146, 140]. A standardised nomenclature for CNG and HCN channels has been proposed by the NC-IUPHAR Subcommittee on voltage-gated ion channels [108].

RMA-CNN: A Residual Mixed-Domain Attention CNN for Bearings Fault Diagnosis and its Time-Frequency Domain Interpretability

Early fault diagnosis of bearings is crucial for ensuring safe and reliable operations. Convolutional Neural Networks (CNNs) have achieved significant breakthroughs in machinery fault diagnosis. However, complex and varying working conditions can lead to inter-class similarity and intra-class variability in datasets, making it more challenging for CNNs to learn discriminative features. Furthermore, CNNs are often considered "black boxes" and lack sufficient interpretability in the fault diagnosis field. To address these issues, this paper introduces a Residual Mixed-Domain AttentionCNN method, referred to as RMA-CNN. This method comprises multiple ResidualMixed Domain Attention Modules (RMAMs), each employing one attention mechanism to emphasize meaningful features in both time and channel domains. This significantly enhances the network's ability to learn fault-related features. Moreover, we conduct an in-depth analysis of the inherent feature learning mechanism of the attention module RMAM to improve the interpretability of CNNs in fault diagnosis applications. Experiments conducted on two datasets—a high-speed aeronautical bearing dataset and a motor bearing dataset—demonstrate that the RMA-CNN achieves remarkable results in diagnostic tasks.

Intestinal hypomagnesemia in an Iranian patient with a novel TRPM6 variant: a case report and review of the literature.

TRPM6 is predominantly expressed in the kidney and colon and encodes a protein containing an ion channel domain and a protein kinase domain. It is crucial for magnesium homeostasis and plays important roles in epithelial magnesium transport and the active magnesium absorption. In this study, we present a 70-day-old Iranian female patient from consanguineous parents with hypomagnesemia and secondary hypocalcemia. She presented with seizures 19days after birth and refractory watery non-bloody diarrhea. She consequently had failure to thrive. Other features included hypotonia, wide anterior fontanel, ventriculomegaly, and pseudotumor cerebri following administration of nalidixic acid. She had severe hypomagnesemia and hypocalcemia which were treated with magnesium and calcium supplementation. Despite initial unstable response to supplemental magnesium, she eventually improved and the diarrhea discontinued. The patient was discharged by magnesium and calcium therapy. At the last follow-up at age 2.5years, the patient remained well without any recurrence or complication. Genetic testing by whole-exome sequencing revealed a novel homozygous frameshift insertion-deletion (indel) variant in exon 26 of the TRPM6 gene, c.3693-3699del GCAAGAG ins CTGCTGTTGACATCTGCT, p.L1231Ffs*36. Segregation analysis revealed the TRPM6 heterozygous variant in both parents. Patients with biallelic TRPM6 pathogenic variants typically exhibit hypomagnesemia with secondary hypocalcemia and present with neurologic manifestations including seizures. In some patients, this is also complicated by chronic diarrhea and failure to thrive. Long-term complications are rare and most of the patients show a good prognosis with supplemental magnesium therapy.

Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability

Image animation aims to transfer the posture change of a driving video to the static object of the source image, and has potential applications in various domains, such as film and game industries. The essential part in this task is to generate a video by learning the motion from the driving video while preserving the appearance from the source image. As a result, a new object with the same motion will be generated in the animated video. However, it is a significant challenge if the object pose shows large-scale change. Even the most recent method failed to achieve this correctly with good visual effects. In order to solve the problem of poor visual effects in the videos with the large-scale pose change, a novel method based on an improved first-order motion model (FOMM) with enhanced dense motion and repair ability was proposed in this paper. Firstly, when generating optical flow, we propose an attention mechanism that optimizes the feature representation of the image in both channel and spatial domains through maximum pooling. This enables better distortion of the source image into the feature domain of the driving image. Secondly, we further propose a multi-scale occlusion restoration module that generates a multi-resolution occlusion map by upsampling the low-resolution occlusion map. Following this, the generator redraws the occluded part of the reconstruction result across multiple scales through the multi-resolution occlusion map to achieve more accurate and vivid visual effects. In addition, the proposed model can be trained effectively in an unsupervised manner. We evaluated the proposed model on three benchmark datasets. The experimental results showed that multiple evaluation indicators were improved by our proposed method, and the visual effect of the animated videos obviously outperformed the FOMM. On the Voxceleb1 dataset, the pixel error, average keypoints distance and average Euclidean distance by our proposed method were reduced by 6.5%, 5.1% and 0.7%, respectively. On the TaiChiHD dataset, the pixel error, average keypoints distance and missing keypoints rate measured by our proposed method were reduced by 4.9%, 13.5% and 25.8%, respectively.

Stokes flow solutions in infinite and semi‐infinite porous channels

AbstractWe derive a class of exact solutions for Stokes flow in infinite and semi‐infinite channel geometries with permeable walls. These simple, explicit, series expressions for both pressure and Stokes flow are valid for all permeability values. At the channel walls, we impose a no‐slip condition for the tangential fluid velocity and a condition based on Darcy's law for the normal fluid velocity. Fluid flow across the channel boundaries is driven by the pressure drop between the channel interior and exterior; we assume the exterior pressure to be constant. We show how the ground state is an exact solution in the infinite channel case. For the semi‐infinite channel domain, the ground‐state solutions approximate well the full exact solution in the bulk and we derive a method to improve their accuracy at the transverse wall. This study is motivated by the need to quantitatively understand the detailed fluid dynamics applicable in a variety of engineering applications including membrane‐based water purification, heat and mass transfer, and fuel cells.

Association of novel mutation in TRPV4 with familial nonsyndromic craniosynostosis with complete penetrance and variable expressivity.

The aim of this study was to characterize a novel pathogenic variant in the transient receptor potential vanilloid 4 (TRPV4) gene, causing familial nonsyndromic craniosynostosis (CS) with complete penetrance and variable expressivity. Whole-exome sequencing was performed on germline DNA of a family with nonsyndromic CS to a mean depth coverage of 300× per sample, with greater than 98% of the targeted region covered at least 25×. In this study, the authors detected a novel variant, c.496C>A in TRPV4, exclusively in the four affected family members. The variant was modeled using the structure of the TRPV4 protein from Xenopus tropicalis. In vitro assays in HEK293 cells overexpressing wild-type TRPV4 or TRPV4 p.Leu166Met were used to assess the effect of the mutation on channel activity and downstream MAPK signaling. The authors identified a novel, highly penetrant heterozygous variant in TRPV4 (NM_021625.4:c.496C>A) causing nonsyndromic CS in a mother and all three of her children. This variant results in an amino acid change (p.Leu166Met) in the intracellular ankyrin repeat domain distant from the Ca2+-dependent membrane channel domain. In contrast to other TRPV4 mutations in channelopathies, this variant does not interfere with channel activity as identified by in silico modeling and in vitro overexpression assays in HEK293 cells. Based on these findings, the authors hypothesized that this novel variant causes CS by modulating the binding of allosteric regulatory factors to TRPV4 rather than directly modifying its channel activity. Overall, this study expands the genetic and functional spectrum of TRPV4 channelopathies and is particularly relevant for the genetic counseling of CS patients.

Multi-scale Triplet Hashing for Medical Image Retrieval

For medical image retrieval task, deep hashing algorithms are widely applied in large-scale datasets for auxiliary diagnosis due to the retrieval efficiency advantage of hash codes. Most of which focus on features learning, whilst neglecting the discriminate area of medical images and hierarchical similarity for deep features and hash codes. In this paper, we tackle these dilemmas with a new Multi-scale Triplet Hashing (MTH) algorithm, which can leverage multi-scale information, convolutional self-attention and hierarchical similarity to learn effective hash codes simultaneously. The MTH algorithm first designs multi-scale DenseBlock module to learn multi-scale information of medical images. Meanwhile, a convolutional self-attention mechanism is developed to perform information interaction of the channel domain, which can capture the discriminate area of medical images effectively. On top of the two paths, a novel loss function is proposed to not only conserve the category-level information of deep features and the semantic information of hash codes in the learning process, but also capture the hierarchical similarity for deep features and hash codes. Extensive experiments on the Curated X-ray Dataset, Skin Cancer MNIST Dataset and COVID-19 Radiography Dataset illustrate that the MTH algorithm can further enhance the effect of medical retrieval compared to other state-of-the-art medical image retrieval algorithms.

Siamese-Based Twin Attention Network for Visual Tracking

Recently, object tracking have achieved remarkable progress in terms of both efficiency and accuracy. However, exiting methods still cannot satisfy challenging tasks under complicated scenarios, such as occlusion, scale variations, and etc. To this end, we propose a novel Siamese-based Twin Attention Network for visual tracking. First, a multi-branch fusion module is presented. By leveaging the fusion scheme, we merge the low-level features with the high-level features extracted from different convolution layers. Then, the representation ability of the target can be enhanced effectively. Second, to fully capture the contextual information in the tracking process, we introduced a global context module into the search branch. Third, to attain robust performance, a saliency mine scheme is employed in the proposed network. Specifically, the self-attention operation is utilized to capture the contextual information from the spatial and channel domain, while the cross-attention operation is to enrich the contextual information relevance by fusing the features between the template and search region. By utilizing these schemes, our tracker can cope well with different challenging scenes. Extensive experiments were conducted on several popular benchmarks, including VOT2016, VOT2018, VOT2019, VOT2021, OTB2013, OTB2015, GOT10k, LaSOT, and NFS. The results demonstrate that the proposed method is effective and achieves competitive results.

Mechanisms of ligand regulation in human voltage-gated proton channel.

Voltage-gated proton (Hv) channels are standalone voltage sensors without separate pore-forming domains in canonical voltage-gated ion channels. They normally function as acid extruders for their unique voltage- and pH-dependent gating. Aside from voltage and pH, Hv channels are also gated by multiple cellular ligands, including Zn2+, polyunsaturated arachidonic acid, albumin, and membrane cholesterol, identified in our recent work. Our previous results have established the S4 segment as the central gating machinery in hHv1 channels by showing that voltage activation enriched its opening conformations while inhibitory ligands, including proton, Zn2+ and cholesterol, stabilized it at resting state conformations. How is the function of hHv1 channels regulated in cells? Our functional characterizations on purified hHv1 channels and those expressed in cells indicated that arachidonic acid has very strong activating effects on hHv1 channels, even being able to reverse both Zn2+ and cholesterol inhibition at physiological levels. Examining the structural effects of arachidonic acid on hHv1 channels using single-molecule FRET further revealed that it activates hHv1 channels by promoting ‘pre-opening’ rather than the opening conformations. In many cells, hHv1 channels are closely associated with NADPH oxidases to compensate for charge/pH imbalances, and both were found in cholesterol-rich lipid rafts. Our results propose that cholesterol perhaps determines the basal activities of the hHv1 channel in cells, while releases of arachidonic acid following irritation or injury may serve as a major mechanism to activate hHv1 channels to promote respiration burst mediating by NADPH oxidases.

Novel mechanism of EAG channel inhibition by imipramine.

Ether-a-go-go (EAG) potassium channels regulate neuronal excitability and tumorigenesis. Upregulation of EAG channel activity is associated with severe neurological abnormalities and cancer. Therefore, studies of molecular mechanisms of EAG current inhibition have therapeutic importance. It has been reported that imipramine inhibits EAG channels by binding intracellularly inside the open pore of the channel (García-Ferreiro, et al., 2004, JGP). Recently we have shown that a tricyclic drug chlorpromazine inhibits EAG channels by binding to their intracellular N-terminal Per-Arnt-Sim (PAS) domain. Since both imipramine and chlorpromazine share high structural similarity and both belong to the tricyclic compound family, we tested if imipramine can also inhibit EAG channels via binding to the PAS domain. Our surface plasmon resonance based experiments showed that imipramine binds to the isolated PAS domain of EAG channels in a concentration-dependent manner. As reported before, we also observed imipramine concentration-dependent inhibition of currents from EAG channels recorded using two-electrode voltage-clamp technique. Importantly, the inhibition was substantially decreased for the mutant channels lacking the N-terminal PAS domain. These results indicate that, in addition to the open-pore inhibition mechanism, imipramine inhibits EAG channels via binding to the intracellular PAS domain. Our results suggest that the tricyclic compounds share a common mechanism of EAG channel inhibition via the intracellular PAS domains.

Voltage sensor targeted activation of Kv7 channels by QO-58.

Kv7 (KCNQ) voltage-gated potassium channels are key modulators of neuronal excitability and have been a promising target for developing novel drugs to treat epilepsy and pain. Drug discovery has focused on identifying small molecules that modulate channel function and understanding their mechanisms of action. Special attention has been given to Kv7 channel activators, which may have therapeutic benefits in epilepsy or pain. In this study, we compared the effects and mechanism of a candidate Kv7 activator, QO-58, which has been previously described but has an unclear site and mechanism of action. We compared QO-58 actions with other well-characterized activators that influence either the VSD (ICA-069673) or pore domain (ML213) of Kv7 channels. QO-58 exhibits strong subtype specificity for Kv7.2 over Kv7.3, similar to VSD-targeted activators. Additionally, QO-58 exhibits prominent state-dependent actions, as it requires channels to activate before binding, and dramatically decelerates closure of Kv7.2. We also tested the effects of multiple VSD mutations on QO-58 and ICA-069673 actions. Interestingly, we found that the Kv7.2[F168L] mutation, previously shown to abolish ICA-069673 sensitivity, does not markedly affect QO-58 mediated effects. Notably, Kv7.2[I134A] and Kv7.2[I173T] weaken the effects of ICA-069673, but are hypersensitive to QO-58. This finding may suggest that dimensions of the VSD binding pocket influence the potency of smaller compounds (e.g. ICA-069673) relative to bulkier ones (e.g. QO-58), despite overlap in their binding sites. Our findings highlight that key contacts underlying sensitivity to VSD-targeted activators may vary significantly depending on the chemical and steric features of the drug. We anticipate that further investigation of VSD-targeted activators will continue to clarify the complex pharmacophore of the VSD binding pocket in Kv7 channels.

A severely range ambiguous clutter suppression method based on multi‐domain cascaded signal processing for space‐based early warning radar

AbstractThe non‐stationary clutter phenomenon and the severe range ambiguity make space‐based early warning radar as an intricate model of received ground and sea clutter. The received clutter is severely broadened in Doppler dimension, and the degrees of freedom (DOFs) of the received clutter is severely increased. Because the DOFs of the system is always lower than the theoretical DOFs of the received clutter, the adaptive processing capability of the system is insufficient and the performance of clutter suppression is seriously degraded. A multi‐domain cascaded adaptive processing method is proposed in this study. The proposed method takes full advantage of the digital antenna array to suppress a portion of the clutter energy by introducing the transmit pulse coding domain, and the remaining influential clutter energy is suppressed by introducing the elevation‐dimensional receive channel domain. Finally, the clutter after the two‐dimensional (2D) pre‐processed is suppressed by the 2D adaptive processing in the azimuthal receive beam‐receive Doppler domain. This method pre‐processes the clutter data by extending the processing dimension to meet the freedom requirements of the adaptive signal processing, and the capability of clutter suppression is significantly improved. Simulation examples show the effectiveness of the proposed method.

Discovery and validation of new Hv1 proton channel inhibitors with onco-therapeutic potential

The voltage-gated hydrogen channel Hv1 encoded in humans by the HVCN1 gene is a highly selective proton channel that allows large fluxes of protons across biological membranes. Hv1 form functional dimers of four transmembrane spanning proteins resembling the voltage sensing domain of potassium channels. Each subunit is highly selective for protons and is controlled by changes in the transmembrane voltage and pH gradient. Hv1 is most expressed in phagocytic cells where it sustains NADPH oxidase-dependent bactericidal function and was reported to facilitate antibody production by B cells and to promote the maturation and motility of spermatocytes. Hv1 contributes to neuroinflammation following brain damage and favors cancer progression possibly by extruding protons generated during aerobic glycolysis of cancer cells. Lack of specific Hv1 inhibitors has hampered translation of this knowledge to treat immune, fertility, or malignancy diseases. In this study, we show that the genetic deletion of Hv1 delays tumor development in a mouse model of granulocytic sarcoma and report the discovery and characterization of two novel bioavailable inhibitors of Hv1 channels that we validate by orthogonal assays and electrophysiological recordings.