The steady-state visual evoked potential (SSVEP)-based brain-computer interface has received extensive attention in research due to its simple system, less training data, and high information ...transfer rate. There are currently two prominent methods dominating the classification of SSVEP signals. One is the knowledge-based task-related component analysis (TRCA) method, whose core idea is to find the spatial filters by maximizing the inter-trial covariance. The other is the deep learning-based approach, which directly learns a classification model from data. However, how to integrate the two methods to achieve better performance has not been studied before.
In this study, we develop a novel algorithm named TRCA-Net (TRCA-Net) to enhance SSVEP signal classification, which enjoys the advantages of both the knowledge-based method and the deep model. Specifically, the proposed TRCA-Net first performs TRCA to obtain spatial filters, which extract task-related components of data. Then the TRCA-filtered features from different filters are rearranged as new multi-channel signals for a deep convolutional neural network (CNN) for classification. Introducing the TRCA filters to a deep learning-based approach improves the signal-to-noise ratio of input data, hence benefiting the deep learning model.
We evaluate the performance of TRCA-Net using two publicly available large-scale benchmark datasets, and the results demonstrate the effectiveness of TRCA-Net. Additionally, offline and online experiments separately testing ten and five subjects further validate the robustness of TRCA-Net. Further, we conduct ablation studies on different CNN backbones and demonstrate that our approach can be transplanted into other CNN models to boost their performance.
The proposed approach is believed to have a promising potential for SSVEP classification and promote its practical applications in communication and control. The code is available athttps://github.com/Sungden/TRCA-Net.
The coupling and possible nonequilibrium between magnons and other energy carriers have been used to explain several recently discovered thermally driven spin transport and energy conversion ...phenomena. Here, we report experiments in which local nonequilibrium between magnons and phonons in a single crystalline bulk magnetic insulator, Y_{3}Fe_{5}O_{12}, has been created optically within a focused laser spot and probed directly via micro-Brillouin light scattering. Through analyzing the deviation in the magnon number density from the local equilibrium value, we obtain the diffusion length of thermal magnons. By explicitly establishing and observing local nonequilibrium between magnons and phonons, our studies represent an important step toward a quantitative understanding of various spin-heat coupling phenomena.
Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathological images are ubiquitous. If clinically ...significant gene mutations can be predicted only through pathological images, it will greatly promote the widespread use of gene mutation detection in clinical practice. However, current gene mutation prediction methods based on pathological images are ineffective because of the inability to identify mutated regions in gigapixel Whole Slide Image (WSI). To address this challenge, hereby we propose a carefully designed framework for WSI-based gene mutation prediction, which consists of three parts. (i) The first part of cancerous area segmentation, based on supervised learning, quickly filters out a large number of non-mutated regions; (ii) the second part of cancerous patch clustering, based on the representations derived from contrastive learning, ensures the comprehensiveness of patch selection; and (iii) the third part of mutation classification, based on the proposed hierarchical deep multi-instance learning method (HDMIL), ensures that sufficient patches are considered and inaccurate selections are ignored. In addition, benefiting from a two-stage attention mechanism in HDMIL, the patches that are highly correlated with gene mutations can be identified. This interpretability can help a pathologist to analyze the correlation between gene mutation and histopathological morphology. Experimental results demonstrate that the proposed gene mutation prediction framework significantly outperforms the state-of-the-art methods. In the TCGA bladder cancer dataset, five clinically relevant gene mutations are well predicted.
•A novel gene mutation prediction framework considering representative patches in WSI.•The patches that are highly correlated with a gene mutation can be identified.•Five clinically relevant gene mutations in bladder cancer can be well predicted.
Nonequilibrium phenomena are ubiquitous in nature and in a wide range of systems, including cold atomic gases and solid-state materials. While these phenomena are challenging to describe both ...theoretically and experimentally, they are essential for the fundamental understanding of many-body systems and practical devices. In the context of spintronics, when a magnetic insulator (MI) is subjected to a thermal gradient, a pure spin current is generated in the form of magnons without the presence and dissipation of a charge current—attractive for reducing energy consumption and central to the emerging field of spin caloritronics. However, the experimental methods for directly quantifying a spin current in insulators and for probing local phonon-magnon nonequilibrium and the associated magnon chemical potential are largely missing. Here, we apply a heating laser to generate a thermal gradient in the MI yttrium iron garnet (YIG),Y3Fe5O12, and evaluate two components of the spin current, driven by temperature and chemical potential gradients, respectively. The experimental method and theory approach for evaluating quasiparticle chemical potential can be applied for analogous phenomena in other many-body systems.
Climate change has impacted the distribution and abundance of numerous plant and animal species during the last century. Orchidaceae is one of the largest yet most threatened families of flowering ...plants. However, how the geographical distribution of orchids will respond to climate change is largely unknown. Habenaria and Calanthe are among the largest terrestrial orchid genera in China and around the world. In this paper, we modeled the potential distribution of eight Habenaria species and ten Calanthe species in China under the near-current period (1970–2000) and the future period (2081–2100) to test the following two hypotheses: 1) narrow-ranged species are more vulnerable to climate change than wide-ranged species; 2) niche overlap between species is positively correlated with their phylogenetic relatedness. Our results showed that most Habenaria species will expand their ranges, although the climatic space at the southern edge will be lost for most Habenaria species. In contrast, most Calanthe species will shrink their ranges dramatically. Contrasting range changes between Habenaria and Calanthe species may be explained by their differences in climate-adaptive traits such as underground storage organs and evergreen/deciduous habits. Habenaria species are predicted to generally shift northwards and to higher elevations in the future, while Calanthe species are predicted to shift westwards and to higher elevations. The mean niche overlap among Calanthe species was higher than that of Habenaria species. No significant relationship between niche overlap and phylogenetic distance was detected for both Habenaria and Calanthe species. Species range changes in the future was also not correlated with their near current range sizes for both Habenaria and Calanthe. The results of this study suggest that the current conservation status of both Habenaria and Calanthe species should be adjusted. Our study highlights the importance of considering climate-adaptive traits in understanding the responses of orchid taxa to future climate change.
Display omitted
•Most Habenaria species will expand their ranges, while all ten Calanthe species will shrink their ranges dramatically.•Niche overlap was not correlated with phylogenetic distance for both Habenaria and Calanthe species.•Narrow-ranged species are not necessarily more vulnerable to future climate change than broad-ranged species.•The current conservation status of both Habenaria and Calanthe species should be adjusted.
Successful infection depends on the ability of the pathogen to gain nutrients from the host. The extracellular pathogenic bacterium group A Streptococcus (GAS) causes a vast array of human diseases. ...By using the quorum-sensing sil system as a reporter, we found that, during adherence to host cells, GAS delivers streptolysin toxins, creating endoplasmic reticulum stress. This, in turn, increases asparagine (ASN) synthetase expression and the production of ASN. The released ASN is sensed by the bacteria, altering the expression of ∼17% of GAS genes of which about one-third are dependent on the two-component system TrxSR. The expression of the streptolysin toxins is strongly upregulated, whereas genes linked to proliferation are downregulated in ASN absence. Asparaginase, a widely used chemotherapeutic agent, arrests GAS growth in human blood and blocks GAS proliferation in a mouse model of human bacteremia. These results delineate a pathogenic pathway and propose a therapeutic strategy against GAS infections.
Display omitted
•Group A streptococcus (GAS) induces ER stress and asparagine production•Asparagine alters GAS gene expression and increases bacterial multiplication•Expression of streptolysins causing ER stress is augmented in asparagine absence•Asparaginase inhibits GAS bacteremia
The human pathogen group A streptococcus delivers streptolysin toxins into host cells, triggering endoplasmic reticulum stress and production of asparagine. Asparagine is utilized by GAS to alter its own gene expression program and enhance proliferation, demonstrating how an extracellular pathogen manipulates host metabolism for its own benefit.
Semi-Supervised Natural Face De-Occlusion Cai, Jiancheng; Han, Hu; Cui, Jiyun ...
IEEE transactions on information forensics and security,
2021, Letnik:
16
Journal Article
Recenzirano
Odprti dostop
Occlusions are often present in face images in the wild, e.g., under video surveillance and forensic scenarios. Existing face de-occlusion methods are limited as they require the knowledge of an ...occlusion mask. To overcome this limitation, we propose in this paper a new generative adversarial network (named OA-GAN) for natural face de-occlusion without an occlusion mask, enabled by learning in a semi-supervised fashion using (i) paired images with known masks of artificial occlusions and (ii) natural images without occlusion masks. The generator of our approach first predicts an occlusion mask, which is used for filtering the feature maps of the input image as a semantic cue for de-occlusion. The filtered feature maps are then used for face completion to recover a non-occluded face image. The initial occlusion mask prediction might not be accurate enough, but it gradually converges to the accurate one because of the adversarial loss we use to perceive which regions in a face image need to be recovered. The discriminator of our approach consists of an adversarial loss, distinguishing the recovered face images from natural face images, and an attribute preserving loss, ensuring that the face image after de-occlusion can retain the attributes of the input face image. Experimental evaluations on the widely used CelebA dataset and a dataset with natural occlusions we collected show that the proposed approach can outperform the state of the art methods in natural face de-occlusion.
The medical literature has been growing exponentially, and its size has become a barrier for physicians to locate and extract clinically useful information. As a promising solution, natural language ...processing (NLP), especially machine learning (ML)‐based NLP is a technology that potentially provides a promising solution. ML‐based NLP is based on training a computational algorithm with a large number of annotated examples to allow the computer to “learn” and “predict” the meaning of human language. Although NLP has been widely applied in industry and business, most physicians still are not aware of the huge potential of this technology in medicine, and the implementation of NLP in breast cancer research and management is fairly limited. With a real‐world successful project of identifying penetrance papers for breast and other cancer susceptibility genes, this review illustrates how to train and evaluate an NLP‐based medical classifier, incorporate it into a semiautomatic meta‐analysis procedure, and validate the effectiveness of this procedure. Other implementations of NLP technology in breast cancer research, such as parsing pathology reports and mining electronic healthcare records, are also discussed. We hope this review will help breast cancer physicians and researchers to recognize, understand, and apply this technology to meet their own clinical or research needs.
PDF
