123 research outputs found
Induced defense strategies of plants against Ralstonia solanacearum
Plants respond to Ralstonia solanacearum infestation through two layers of immune system (PTI and ETI). This process involves the production of plant-induced resistance. Strategies for inducing resistance in plants include the formation of tyloses, gels, and callose and changes in the content of cell wall components such as cellulose, hemicellulose, pectin, lignin, and suberin in response to pathogen infestation. When R. solanacearum secrete cell wall degrading enzymes, plants also sense the status of cell wall fragments through the cell wall integrity (CWI) system, which activates deep-seated defense responses. In addition, plants also fight against R. solanacearum infestation by regulating the distribution of metabolic networks to increase the production of resistant metabolites and reduce the production of metabolites that are easily exploited by R. solanacearum. We review the strategies used by plants to induce resistance in response to R. solanacearum infestation. In particular, we highlight the importance of plant-induced physical and chemical defenses as well as cell wall defenses in the fight against R. solanacearum
Detect and remove watermark in deep neural networks via generative adversarial networks
Deep neural networks (DNN) have achieved remarkable performance in various
fields. However, training a DNN model from scratch requires a lot of computing
resources and training data. It is difficult for most individual users to
obtain such computing resources and training data. Model copyright infringement
is an emerging problem in recent years. For instance, pre-trained models may be
stolen or abuse by illegal users without the authorization of the model owner.
Recently, many works on protecting the intellectual property of DNN models have
been proposed. In these works, embedding watermarks into DNN based on backdoor
is one of the widely used methods. However, when the DNN model is stolen, the
backdoor-based watermark may face the risk of being detected and removed by an
adversary. In this paper, we propose a scheme to detect and remove watermark in
deep neural networks via generative adversarial networks (GAN). We demonstrate
that the backdoor-based DNN watermarks are vulnerable to the proposed GAN-based
watermark removal attack. The proposed attack method includes two phases. In
the first phase, we use the GAN and few clean images to detect and reverse the
watermark in the DNN model. In the second phase, we fine-tune the watermarked
DNN based on the reversed backdoor images. Experimental evaluations on the
MNIST and CIFAR10 datasets demonstrate that, the proposed method can
effectively remove about 98% of the watermark in DNN models, as the watermark
retention rate reduces from 100% to less than 2% after applying the proposed
attack. In the meantime, the proposed attack hardly affects the model's
performance. The test accuracy of the watermarked DNN on the MNIST and the
CIFAR10 datasets drops by less than 1% and 3%, respectively
Brain Age from the Electroencephalogram of Sleep
The human electroencephalogram (EEG) of sleep undergoes profound changes with
age. These changes can be conceptualized as "brain age", which can be compared
to an age norm to reflect the deviation from normal aging process. Here, we
develop an interpretable machine learning model to predict brain age based on
two large sleep EEG datasets: the Massachusetts General Hospital sleep lab
dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health
Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean
absolute deviation of 8.1 years between brain age and chronological age in the
healthy participants in the MGH dataset. As validation, we analyze a subset of
SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years
difference in brain age. Participants with neurological and psychiatric
diseases, as well as diabetes and hypertension medications show an older brain
age compared to chronological age. The findings raise the prospect of using
sleep EEG as a biomarker for healthy brain aging
Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement
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