Spin Inertia and Auto-Oscillations in Ferromagnets.

Recent experimental confirmation of spin inertia in ferromagnets positions this well-developed material class as a prime candidate for THz frequency applications. Spin-torque driven critical spin dynamics, such as auto-oscillations, play the central role in many spin-based technologies. Yet, the pressing question on spin inertia's effect on spin-torque driven dynamics in ferromagnets has remained unexplored. Here, we develop the theoretical framework of precessional auto-oscillations for ferromagnets with spin inertia. We discover and introduce the concept of nutational auto-oscillations and demonstrate that they can become pivotal for future ultrahigh frequency technologies. We conclude by revealing parallels between spin dynamics in ferrimagnets and inertial ferromagnets and derive an isomorphism that establishes a foundation for synergistic knowledge transfer between these research fields.

A Deep Long-Term Joint Temporal-Spectral Network for Spectrum Prediction.

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal-spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time-frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R2, respectively, which is better than all the baseline deep learning models.

Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features.

Background: Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD. Methods: In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation. Results: The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects. Conclusion: This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.

Using Computer Graphics to Make Science Visible in Engineering Education.

Science plays a crucial role in engineering. But science tends to be obscure to students, especially when they are overwhelmed by complex engineering design challenges that involve many variables. This article shows how computer graphics can be used to visualize science concepts and operationalize inquiry practices in engineering design to support integrated learning and teaching of science and engineering. Based on these graphical capabilities, generative design driven by evolutionary computation can also be visually illustrated to give students a glimpse into how artificial intelligence is transforming engineering design. The article provides real-world examples in the field of sustainable energy engineering based on Aladdin, an open-source design and analysis Web app. It also presents evidence of learning from pilot tests at culturally diverse high schools. Science educators interested in incorporating engineering design into their lesson plans may find this article helpful.

Depletion of G9A attenuates imiquimod-induced psoriatic dermatitis via targeting EDAR-NF-κB signaling in keratinocyte.

Psoriasis is a common and recurrent inflammatory skin disease characterized by inflammatory cells infiltration of the dermis and excessive proliferation, reduced apoptosis, and abnormal keratosis of the epidermis. In this study, we found that G9A, an important methyltransferase that mainly mediates the mono-methylation (me1) and di-methylation (me2) of histone 3 lysine 9 (H3K9), is highly expressed in lesions of patients with psoriasis and imiquimod (IMQ)-induced psoriasis-like mouse model. Previous studies have shown that G9A is involved in the pathogenesis of various tumors by regulating apoptosis, proliferation, differentiation, and invasion. However, the role of G9A in skin inflammatory diseases such as psoriasis remains unclear. Our data so far suggest that topical administration of G9A inhibitor BIX01294 as well as keratinocyte-specific deletion of G9A greatly alleviated IMQ-induced psoriatic alterations in mice for the first time. Mechanistically, the loss function of G9A causes the downregulation of Ectodysplasin A receptor (EDAR), consequently inhibiting the activation of NF-κB pathway, resulting in impaired proliferation and increased apoptosis of keratinocytes, therefore ameliorating the psoriatic dermatitis induced by IMQ. In total, we show that inhibition of G9A improves psoriatic-like dermatitis mainly by regulating cell proliferation and apoptosis rather than inflammatory processes, and that this molecule may be considered as a potential therapeutic target for keratinocyte hyperproliferative diseases such as psoriasis.

Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism.

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration.

BACKGROUND: Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. METHODS: The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program. RESULTS: Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib. CONCLUSIONS: We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.

Ligustrazine alleviates psoriasis-like inflammation through inhibiting TRAF6/c-JUN/NFκB signaling pathway in keratinocyte.

Ligusticum chuanxiong Hort (Ligusticum; Apiaceae) (accepted name, Ligusticum striatum DC, on "The Plant List" for the latest version) is a Chinese herbal medicine (CHM) which mainly distributed in Sichuan Basin, China. Chuanxiong is the dried rhizome of Ligusticum chuanxiong Hort. Ligustrazine, also known as tetramethylpyrazine (TMP), is a main active fraction of chuanxiong. The aim of this study was to clarify the underlying mechanisms by which TMP protect against psoriasis-like inflammation in keratinocytes. Here, we demonstrated that TMP alleviated the severity and PASI scores of IMQ-induced psoriasis-like skin lesion in vivo. For the histopathology level, TMP inhibited the over-proliferation of keratinocytes in the epidermis and the substantial immune cells influx in dermis. For the mechanism of the ability of TMP on regulating inflammation, we confirmed that TMP regulate the TRAF6/c-JUN/NFκB signaling pathway through analyzing the proteomics profiling and verifying the expression of TRAF6, pho-c-Jun, pho-NFκB, so that the downstream psoriasis-relevant genes transcribed by c-JUN or NFκB were down-regulated. Furthermore, we predicted TRAF6 as the potential binding point of TMP. Accordingly, our study demonstrated that TMP regulated psoriasis-like inflammation through inhibiting TRAF6/c-JUN/NFκB signaling pathway in keratinocytes, which potentially provides evidence of the mechanism of TMP in the treatment and prevention of psoriasis.

Perceptual hue, lightness, and chroma are represented in a multidimensional functional anatomical map in macaque V1.

Humans perceive millions of colors along three dimensions of color space: hue, lightness, and chroma. A major gap in knowledge is where the brain represents these specific dimensions in cortex, and how they relate to each other. Previous studies have shown that brain areas V4 and the posterior inferotemporal cortex (PIT) are central to computing color dimensions. To determine the contribution of V1 to setting up these downstream processing mechanisms, we studied cortical color responses in macaques-who share color vision mechanisms with humans. We used two-photon calcium imaging at both meso- and micro-scales and found that hue and lightness are laid out in orthogonal directions on the cortical map, with chroma represented by the strength of neuronal responses, as previously shown in PIT. These findings suggest that the earliest cortical stages of vision determine the three primary dimensions of human color perception.

Blood Immune Cell Composition Associated with Obesity and Drug Repositioning Revealed by Epigenetic and Transcriptomic Conjoint Analysis.

This research was designed to analyze the composition of immune cells in obesity and identify novel and potent drugs for obesity management by epigenetic and transcriptomic conjoint analysis. DNA methylation data set (GSE166611) and mRNA expression microarray (GSE18897) were obtained from the Gene Expression Omnibus database. A total of 72 objects (35 obese samples and 37 controls) were included in the study. Immune cell composition analysis, drug repositioning, and gene set enrichment analysis (GSEA) were performed using CIBERSORT, connectivity map (CMap), and GSEA tools. Besides, we performed a single-cell RNA-seq of the immune cells from whole blood samples obtained from one obese patient and one healthy control. mRNA levels of drug target genes were analyzed by qPCR assay in blood samples from six patients and six healthy controls. Immune cell composition analysis found that CD8 + T cells and NK cells were significantly lower in the obese group. 11 drugs/compounds are considered to possess obesity-control potential, such as atorvastatin. Moreover, the expression of drug targets (STAT3, MCL1, PMAIP1, SOD2, FOX O 3, FOS, FKBP5) in obese patients were higher than those in controls. In conclusion, immune cells are potential therapeutic targets for obesity. Our results also contribute to accelerate research on drug development of obesity.

Ligustrazine Alleviate Acute Lung Injury Through Suppressing Pyroptosis and Apoptosis of Alveolar Macrophages.

Ligustrazine (Tetramethylpyrazine, TMP) is an active substance extracted from the Umbelliferae plant Ligusticum chuanxiong. It has been proven to have antioxidant and inflammation effects. The study was designed to explore the efficacy and specific mechanism of TMP for ALI/ARDS treatment. Here, we confirmed that TMP decreased the infiltration of inflammatory cells in alveoli and the secretion of pro-inflammatory factors, which is comparable to glucocorticoids in vivo. In vitro, TMP inhibited the polarization of M1-type macrophages, and to a certain extent, promoted M2-type repolarization, thus reducing LPS-induced massive transcription and secretion of IL-1ß, IL-18, TNF-ɑ and other inflammatory factors. Besides, TMP reduced expression of NLRP3, inhibited the formation of inflammasome complexes, and decreased the cleavage of caspase-1, leading to reduced cell pyroptosis and accompanying inflammation. TMP also inhibited apoptosis through caspase-8/caspase-3 signaling pathways. Our study indicates that TMP improved ALI through inhibiting the TLR4/TRAF6/NFκB/NLRP3/caspase-1 and TLR4/caspase-8/caspase-3 signaling pathways, which reversed macrophages polarization, reduced cell pyroptosis and apoptosis, which provides a theoretical basis of using TMP in treating ALI in the future.

Microglia: A Double-Edged Sword in Intracerebral Hemorrhage From Basic Mechanisms to Clinical Research.

Microglia are the resident immune cells of the central nervous system (CNS). It is well established that microglia are activated and polarized to acquire different inflammatory phenotypes, either pro-inflammatory or anti-inflammatory phenotypes, which act as a critical component in the neuroinflammation following intracerebral hemorrhage (ICH). Microglia produce pro-inflammatory mediators at the early stages after ICH onset, anti-inflammatory microglia with neuroprotective effects appear to be suppressed. Previous research found that driving microglia towards an anti-inflammatory phenotype could restrict inflammation and engulf cellular debris. The principal objective of this review is to analyze the phenotypes and dynamic profiles of microglia as well as their shift in functional response following ICH. The results may further the understanding of the body's self-regulatory functions involving microglia following ICH. On this basis, suggestions for future clinical development and research are provided.

Clustered functional domains for curves and corners in cortical area V4.

The ventral visual pathway is crucially involved in integrating low-level visual features into complex representations for objects and scenes. At an intermediate stage of the ventral visual pathway, V4 plays a crucial role in supporting this transformation. Many V4 neurons are selective for shape segments like curves and corners; however, it remains unclear whether these neurons are organized into clustered functional domains, a structural motif common across other visual cortices. Using two-photon calcium imaging in awake macaques, we confirmed and localized cortical domains selective for curves or corners in V4. Single-cell resolution imaging confirmed that curve- or corner-selective neurons were spatially clustered into such domains. When tested with hexagonal-segment stimuli, we find that stimulus smoothness is the cardinal difference between curve and corner selectivity in V4. Combining cortical population responses with single-neuron analysis, our results reveal that curves and corners are encoded by neurons clustered into functional domains in V4. This functionally specific population architecture bridges the gap between the early and late cortices of the ventral pathway and may serve to facilitate complex object recognition.

Corrigendum: Blood Immune Cell Composition Associated With Obesity and Drug Repositioning Revealed by Epigenetic and Transcriptomic Conjoint Analysis.

[This corrects the article DOI: 10.3389/fphar.2021.714643.].

An Open Resource for Non-human Primate Optogenetics.

Optogenetics has revolutionized neuroscience in small laboratory animals, but its effect on animal models more closely related to humans, such as non-human primates (NHPs), has been mixed. To make evidence-based decisions in primate optogenetics, the scientific community would benefit from a centralized database listing all attempts, successful and unsuccessful, of using optogenetics in the primate brain. We contacted members of the community to ask for their contributions to an open science initiative. As of this writing, 45 laboratories around the world contributed more than 1,000 injection experiments, including precise details regarding their methods and outcomes. Of those entries, more than half had not been published. The resource is free for everyone to consult and contribute to on the Open Science Framework website. Here we review some of the insights from this initial release of the database and discuss methodological considerations to improve the success of optogenetic experiments in NHPs.

Long-term all-optical interrogation of cortical neurons in awake-behaving nonhuman primates.

Whereas optogenetic techniques have proven successful in their ability to manipulate neuronal populations-with high spatial and temporal fidelity-in species ranging from insects to rodents, significant obstacles remain in their application to nonhuman primates (NHPs). Robust optogenetics-activated behavior and long-term monitoring of target neurons have been challenging in NHPs. Here, we present a method for all-optical interrogation (AOI), integrating optical stimulation and simultaneous two-photon (2P) imaging of neuronal populations in the primary visual cortex (V1) of awake rhesus macaques. A red-shifted channel-rhodopsin transgene (ChR1/VChR1 [C1V1]) and genetically encoded calcium indicators (genetically encoded calmodulin protein [GCaMP]5 or GCaMP6s) were delivered by adeno-associated viruses (AAVs) and subsequently expressed in V1 neuronal populations for months. We achieved optogenetic stimulation using both single-photon (1P) activation of neuronal populations and 2P activation of single cells, while simultaneously recording 2P calcium imaging in awake NHPs. Optogenetic manipulations of V1 neuronal populations produced reliable artificial visual percepts. Together, our advances show the feasibility of precise and stable AOI of cortical neurons in awake NHPs, which may lead to broad applications in high-level cognition and preclinical testing studies.