Search Results (1,545)

Normal alcohols (n-alcohols) exhibit contrasting effects on neuronal excitability; specifically, ethanol enhances neuronal firing, while hexanol suppresses it. Both compounds are known to inhibit sodium currents, yet the mechanisms behind their differing effects remain unclear. Our previous studies demonstrated that Kv7 channels are modulated differently by alcohol chain length, prompting investigation into their role in these contrasting effects. We conducted whole-cell patch clamp recordings on neonatal (P5-P7) rat superior cervical ganglion neurons to assess alcohol impacts on action potential firing and ionic currents, utilizing tetrodotoxin (TTX), XE991, and retigabine (RTG). Ethanol (100 mM) increased action potential frequency, whereas hexanol (3 mM) decreased it, despite both inhibiting sodium currents by 12% and 45%, respectively. Notably, ethanol inhibited Kv7 currents by 16%, while hexanol enhanced them by 29%. TTX alone did not affect firing frequency until sodium current inhibition exceeded 76%, indicating moderate sodium channel blockade cannot fully explain the effects of alcohol. XE991 increased firing frequency and depolarized the resting membrane potential, while retigabine produced opposite effects. The combination of TTX with Kv7 modulators replicated the effects observed with each alcohol. These findings suggest Kv7 channel modulation plays an important role in the chain length-dependent effects of alcohol on neuronal excitability. Full article

Background: Renal Cell Carcinoma (RCC) is the most common type of kidney cancer and requires accurate histopathological grading for effective prognosis and treatment planning. However, manual grading is time-consuming, subjective, and susceptible to inter-observer variability. Objective: This study proposes EAT-Net (Efficient Attention Transformer Network), a dual-stream deep learning model designed to automate and enhance RCC grade classification from histopathological images. Method: EAT-Net integrates EfficientNetB0 for local feature extraction and a Vision Transformer (ViT) stream for capturing global contextual dependencies. The architecture incorporates Squeeze-and-Excitation (SE) modules to recalibrate feature maps, improving focus on informative regions. The model was trained and evaluated on two publicly available datasets, KMC-RENAL and RCCG-Net. Standard preprocessing was applied, and the model’s performance was assessed using accuracy, precision, recall, and F1-score. Results: EAT-Net achieved superior results compared to state-of-the-art models, with an accuracy of 92.25%, precision of 92.15%, recall of 92.12%, and F1-score of 92.25%. Ablation studies demonstrated the complementary value of the EfficientNet and ViT streams. Additionally, Grad-CAM visualizations confirmed that the model focuses on diagnostically relevant areas, supporting its interpretability and clinical relevance. Conclusion: EAT-Net offers an accurate, and explainable framework for RCC grading. Its lightweight architecture and high performance make it well-suited for clinical deployment in digital pathology workflows. Full article

Liposomal systems are advanced carriers of active substances which, thanks to their ability to encapsulate these substances, significantly improve their pharmacokinetics, bioavailability, and selectivity. This article presents the results of spectroscopic studies for a selected compound from the 1,3,4-thiadiazole group, namely 4-[5-(naphthalen-1-ylmethyl)-1,3,4-thiadiazol-2-yl]benzene-1,3-diol (NTBD, see below in the text), in selected liposomal systems formed from the phospholipid 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC). Detailed spectroscopic analyses were carried out using electronic absorption and fluorescence spectroscopy; resonance light scattering (RLS) spectra measurements; dynamic light scattering (DLS); as well as time-resolved methods—fluorescence lifetime measurements using the TCSPC technique. Subsequently, based on the interpretation of spectra obtained by FTIR infrared spectroscopy, the preliminary molecular organization of the above-mentioned compounds within lipid multilayers was determined. It was found that NTBD preferentially occupies the region of polar lipid headgroups in the lipid multilayer, although it also noticeably interacts with the hydrocarbon chains of the lipids. Furthermore, X-ray diffraction (XRD) techniques were used to study the effect of NTBD on the molecular organization of DPPC lipid multilayers. Monomeric structures and aggregated forms of the above-mentioned 1,3,4-thiadiazole analogue were characterized using X-ray crystallography. Interesting dual fluorescence effects observed in steady-state fluorescence measurements were linked to the excited-state intramolecular proton transfer (ESIPT) effect (based on our earlier studies), which, in the obtained biophysical systems—liposomal systems with strong hydrophobicity—is greatly enhanced by aggregation-induced emission (AIE) effects. In summary, the research presented in this study, concerning the novel 1,3,4-thiadiazole derivative NTBD, is highly relevant to drug delivery systems, such as various model liposomal systems, as it demonstrates that depending on the concentration of the selected fluorophore, different forms may be present, allowing for appropriate modulation of its biological activity. Full article

This study presents MDSCNet, a compact radar image-based deep learning model for multi-action classification in elderly healthcare scenarios. Motivated by the need for real-time deployment on resource-constrained devices, MDSCNet employs a streamlined architecture with a small number of lightweight expansion–depthwise–projection blocks, removing complex attention and squeeze-and-excitation modules to minimize computational overhead. The model is evaluated on a millimeter-wave radar dataset covering five healthcare-related actions: lying, sitting, standing, bed-exit, and falling, performed by 15 participants on an actual electric nursing bed. The experimental results demonstrate that MDSCNet achieves accuracy comparable to state-of-the-art CNN-based methods while maintaining an extremely compact model size of only 0.29 MB, showing its suitability for practical elderly care applications where both accuracy and efficiency are critical. Full article

The response of tomato plants, Ailsa Craig and the carotenoid mutant tangerine, to five days of treatment by low light intensity at normal and low temperature with respect to the photosynthetic performance as well as their capacity to recover after three days under normal conditions was evaluated. Tangerine plants are characterized by defective prolycopene isomerase (CRTISO) and accumulate tetra-cis lycopene instead of all-trans lycopene. The gas exchange parameters were evaluated on intact plants and the pigment content in leaves was estimated. The photosynthetic competence of photosystem II (PSII) and photosystem I (PSI) and the effectiveness of the energy dissipation were assessed by pulse-amplitude-modulated (PAM) fluorometry. The abundance of reaction center proteins of PSII and PSI was estimated by immunoblotting. The application of low light alone or low light and low temperature reduced the chlorophyll content in both types of plants, which was more strongly expressed in Ailsa Craig. The net photosynthetic rate and photochemical activities of PSII and PSI were negatively affected by low light and much more strongly decreased when low light was applied at low temperature. The low-light-induced increase in excitation pressure on PSII and the effectiveness of non-photochemical quenching were not temperature-dependent. The negative effect of the combined treatment in tangerine was more strongly expressed in comparison with Ailsa Craig with respect to the abundance of reaction center proteins of both photosystems. Most probably, the differential photosynthetic response of the carotenoid mutant tangerine and Ailsa Craig to the combined treatment by low light and low temperature is related to the accumulation of tetra-cis-lycopene instead of all-trans-lycopene. Full article

We investigate the excitation and variability of migrating and non-migrating diurnal and semi-diurnal tides in the mesosphere and lower thermosphere (MLT) during the 2021 Northern Hemisphere sudden stratospheric warming (SSW). Zonal wind data from MERRA-2 reanalysis are decomposed into tidal components using a two-dimensional least-squares harmonic fitting technique. The migrating diurnal tide (DW1) strengthens at low latitudes following the SSW onset, whereas the migrating semi-diurnal tide (SW2) intensifies at high latitudes. Non-migrating diurnal tides (D0, DW2, DW3) arise from nonlinear interactions between DW1 and stationary planetary waves (SPWs), while non-migrating semi-diurnal tides (SW1, SW3) are modulated by stratospheric ozone variability linked to planetary-wave activity. The zonally symmetric semi-diurnal tide (S0) responds primarily to dynamical perturbations associated with the SSW. Eastward non-migrating diurnal tides (DE2, DE3) correlate strongly with total precipitable water vapor (TPWV), indicating tropospheric latent-heat forcing, whereas DE1 exhibits weak coupling. These results reveal distinct, latitude-dependent excitation pathways connecting stratospheric and tropospheric dynamics to tidal variability in the MLT during major SSW events. Full article

Oceanic whitecaps are caused by wave breaking and are very important in air–sea interactions. Usually, whitecap coverage is considered a key factor in representing the role of whitecaps. However, the accurate identification of whitecap coverage in videos under dynamic marine conditions is a tough task. An EMA-SE-ResUNet deep learning model was proposed in this study to address this challenge. Based on a foundation of residual network (ResNet)-50 as the encoder and U-Net as the decoder, the model incorporated efficient multi-scale attention (EMA) module and squeeze-and-excitation network (SENet) module to improve its performance. By employing a dynamic weight allocation strategy and a channel attention mechanism, the model effectively strengthens the feature representation capability for whitecap edges while suppressing interference from wave textures and illumination noise. The model’s adaptability to complex sea surface scenarios was enhanced through the integration of data augmentation techniques and an optimized joint loss function. By applying the proposed model to a dataset collected by a shipborne camera system deployed during a comprehensive fishery resource survey in the northwest Pacific, the model results outperformed main segmentation algorithms, including U-Net, DeepLabv3+, HRNet, and PSPNet, in key metrics: whitecap intersection over union (IoU_W) = 73.32%, pixel absolute error (PAE) = 0.081%, and whitecap F1-score (F1_W) = 84.60. Compared to the traditional U-Net model, it achieved an absolute improvement of 2.1% in IoU_W while reducing computational load (GFLOPs) by 57.3% and achieving synergistic optimization of accuracy and real-time performance. This study can provide highly reliable technical support for studies on air–sea flux quantification and marine aerosol generation. Full article

Astrocytic calcium signaling is a central mechanism of neuron-glia communication that operates across multiple spatial and temporal scales. Traditionally, research has focused on intracellular Ca²⁺ oscillations that regulate gliotransmitter release, ion homeostasis, and metabolic support. Recent evidence, however, reveals that extracellular calcium ([Ca²⁺]o) is not a passive reservoir but a dynamic signaling mediator capable of influencing neuronal excitability within milliseconds. Through mechanisms such as calcium-sensing receptor (CaSR) activation, ion channel modulation, surface charge effects, and ephaptic coupling, astrocytes emerge as active partners in both slow and rapid modes of communication. This dual perspective reshapes our understanding of brain physiology and disease. Disrupted Ca²⁺ signaling contributes to network instability in epilepsy, synaptic dysfunction in Alzheimer’s and Parkinson’s disease, and impaired maturation in neurodevelopmental disorders. Methodological advances, including Ca²⁺-selective microelectrodes, genetically encoded extracellular indicators, and computational modeling, are beginning to uncover the richness of extracellular Ca²⁺ dynamics, though challenges remain in achieving sufficient spatial and temporal resolution. By integrating classical intracellular pathways with emerging insights into extracellular signaling, this review highlights astrocytes as central architects of the ionic landscape. Recognizing calcium as both an intracellular messenger and an extracellular signaling mediator provides a unifying framework for neuron–glia interactions and opens new avenues for therapeutic intervention. Full article

Background/Objectives: Empathy is essential for successful social functioning, mediating different aspects of social cognition in everyday life. An intriguing aspect is the involvement of empathy even in basic neural mechanisms of action perception, thanks to its association with the Mirror Neuron System (MNS). The present retrospective study explores whether individual differences in cognitive and affective empathy, measured by the Interpersonal Reactivity Index (IRI) questionnaire, can predict motor resonance—the enhancement of motor cortex reactivity during the observation of biological movements—during transitive and intransitive action observation. Methods: Data from 160 healthy subjects who participated in transcranial magnetic stimulation (TMS) experiments assessing corticospinal excitability during action observation were retrospectively analyzed using multiple linear regression models. Participants filled the IRI and observed intransitive single-digit finger movements (n = 80) or grasping actions directed at different targets (intransitive, object-directed, social-directed; n = 80) synchronized with TMS over the primary motor cortex, allowing the investigation of how action features modulate the relationship between participants’ empathic traits and motor resonance magnitude. Results: Results show that empathic traits do not affect motor resonance during intransitive movements, whereas they do when motor resonance is measured during the observation of transitive actions. Cognitive empathy, particularly the perspective-taking scale, significantly predicts motor resonance magnitude when observing goal-directed actions. Meanwhile, affective empathy, specifically the empathic concern scale, predicts motor resonance while observing social action. Conclusions: These findings highlight that different facets of empathy are significantly related to humans’ ability to understand others’ actions through inner simulation mechanisms, particularly concerning action goals and social relevance. Full article

Laser-induced breakdown spectroscopy (LIBS), limited by matrix effects, self-absorption in complex samples, and ambient atmospheric influences, still requires further improvement in detection sensitivity and signal stability. In this work, the excitation beam of LIBS is modulated into an optical vortex by an optical phase element, and optical vortex-induced LIBS is used to detect and analyze coal samples. Building on the uniform annular intensity distribution and orbital angular momentum (OAM) carried by the optical vortex, it is anticipated that spectral signal intensity can be enhanced by improving plasma ablation efficiency, reducing shielding effects, and increasing electron collision frequency, thereby reducing signal uncertainty and enhancing LIBS analytical performance. For the first time, a classification model combining principal component analysis (PCA) and support vector machine (SVM) is developed, integrating optical vortex-induced LIBS technology with machine learning. Using the PCA-SVM model, optical vortex-based LIBS attained a coal classification accuracy of 95%, significantly higher than the 88% achieved with Gaussian beams, thereby markedly improving classification performance for complex matrix samples. These results demonstrate that optical vortex-induced LIBS possesses strong potential for efficient detection of samples with complex matrices. Full article

Breast cancer is one of the most prevalent malignant tumors among women worldwide, underscoring the urgent need for early and accurate diagnosis to reduce mortality. To address this, A Multi-Feature Fusion Classification Network (MFF-ClassificationNet) is proposed for breast histopathological image classification. The network adopts a two-branch parallel architecture, where a convolutional neural network captures local details and a Transformer models global dependencies. Their features are deeply integrated through a Multi-Feature Fusion module, which incorporates a Convolutional Block Attention Module—Squeeze and Excitation (CBAM-SE) fusion block combining convolutional block attention, squeeze-and-excitation mechanisms, and a residual inverted multilayer perceptron to enhance fine-grained feature representation and category-specific lesion characterization. Experimental evaluations on the BreakHis dataset achieved accuracies of 98.30%, 97.62%, 98.81%, and 96.07% at magnifications of 40×, 100×, 200×, and 400×, respectively, while an accuracy of 97.50% was obtained on the BACH dataset. These results confirm that integrating local and global features significantly strengthens the model’s ability to capture multi-scale and context-aware information, leading to superior classification performance. Overall, MFF-ClassificationNet surpasses conventional single-path approaches and provides a robust, generalizable framework for advancing computer-aided diagnosis of breast cancer. Full article

Nitrogen-fused conjugated heterocycles have attracted growing interest owing to their tunable electronic properties and potential in organic optoelectronics. In this study, two centrosymmetric donor–acceptor-type emitters PP-6F and PPA-3F were designed by incorporating trifluorophenyl and anthracene acceptor units into a pyrrolo[3,2-b]pyrrole (PP) framework. The experimental and theoretical results reveal that subtle modulations in molecular conformation and π-conjugation pathways strongly affect the excited-state characteristics. PP-6F, featuring a nearly coplanar donor–acceptor configuration, exhibits efficient π-electron delocalization and a dominant local excitation (LE) emission with a large oscillator strength. In contrast, the bulky anthracene in PPA-3F increases the donor–acceptor dihedral angle, reduces conjugation coupling, and promotes orbital separation, leading to a hybrid intramolecular charge transfer and local excitation (ICT/LE) excited state. The rigid anthracene framework suppresses structural reorganization and nonradiative decay, allowing PPA-3F to retain a relatively high oscillator strength despite its charge-transfer nature. This work demonstrates that fine-tuning donor–acceptor dihedral angles and conjugation continuity within PP-based systems is an effective strategy for balancing LE and ICT emissions and developing high-efficiency nitrogen-fused organic emitters and scintillators. Full article

In this paper, the optimization of a harmonic-excited synchronous machine (HESM) is carried out. A two-phase harmonic exciter winding of the HESM provides brushless excitation and sufficient starting torque at any rotor position. The HESM under consideration is intended to be used for applications requiring speed control, especially in the field-weakening region. The novelty of the proposed approach is that a two-level optimization based on a two-stage model is used to reduce the computational burden. It includes a finite-element model that takes into account only the fundamental current harmonic (basic model). Using the output of the basic model, a reduced-order model (ROM) is parametrized. The ROM considers pulse-width-modulated components of the inverter output current, zero-sequence current injected into the stator winding, and harmonic excitation winding currents. A two-level optimization technique is developed based on the Nelder–Mead method, taking into account the significantly different computational complexity of the basic and reduced-order models. Optimization is performed considering two operating points: base and maximum speed. The results show that an optimized design provides significantly higher efficiency and reduced inverter power requirements. This allows the use of more compact and cheaper power switches. Therefore, the advantage of the presented approach lies in the computationally effective optimization of HESMs (optimization time is reduced by approximately three orders of magnitude compared to calculations using FEA alone), which enhances HESMs’ performance in various applications. Full article

Rett syndrome (RTT) is a severe neurodevelopmental disorder caused primarily by mutations in the gene encoding the methyl-CpG-binding protein 2 (Mecp2). Mecp2 binds to methylated cytosines, playing a crucial role in chromatin organization and transcriptional regulation. At the neurobiological level, RTT is characterized by dendritic spine dysgenesis and altered excitation–inhibition balance, drawing attention to the mechanisms that scale from mutations in a nuclear protein to altered neuronal connectivity. Although Mecp2 dysfunction disrupts multiple neuronal processes, emerging evidence highlights altered calcium (Ca²⁺) signaling as a central contributor to RTT pathophysiology. This review explores the link between Mecp2 and Ca²⁺ regulation by highlighting how Mecp2 affects Ca²⁺-dependent transcriptional pathways, while Ca²⁺ modulates Mecp2 function by inducing post-translational modifications. We discuss this crosstalk in light of evidence from RTT models, with a particular focus on the brain-derived neurotrophic factor BDNF-miR132-Mecp2 axis and the dysregulation of ryanodine receptors (RyRs). Additionally, we examine how these perturbations contribute to the reduced structural plasticity and the altered activity-driven gene expression that characterizes RTT. Understanding the intersection between Mecp2 function and Ca²⁺ homeostasis will provide critical insights into RTT pathogenesis and potential therapeutic targets aimed at restoring neuronal connectivity. Full article

Spinal cord injury (SCI) is a devastating, debilitating, and life-altering condition that lacks a cure or effective treatment as of today. An altered excitation/inhibition ratio after an injury, with an increase in inhibitory input, limits motor and sensory function. Together with the limited endogenous regeneration capacity of the affected neuronal circuits, this results in further loss of function. Hingorani and collaborators recently reported that transplantation of dissociated sensory neurons from neonatal dorsal root ganglia (DRGs) expressing the bacterial sodium channel NaChBac significantly improved locomotion in a severe SCI by increasing the excitatory neuronal input at the injury site. Here, we additionally target the potential axonal regeneration of endogenous and transplanted cells, using cytoskeleton-modulating drugs to enhance axonal length. We employ, alone or in combination, blebbistatin and epothilone B, tested in vitro. We found that individually, each compound significantly induced the sensory neurons’ axonal elongation; however, their combination completely abolished it. Interestingly, a combinatory treatment including the modification of DRGs to express the NaChBac sodium channel and the treatment with blebbistatin increased the axonal elongation in vitro. Nevertheless, when applied in vivo in a model of SCI, local and single para-amino-blebbistatin (a stable analogue of blebbistatin) administration and the transplanted NaChBac expressing sensory neurons limit the functional recovery enabled by neuronal transplantation alone. Thus, despite the beneficial outputs of isolated neuronal cultures that allow selection of in vivo combinatory strategies, the multifaced characteristics of CNS injuries limit the potential success of single and local treatment administration, demanding extended and sustained treatments. Full article