Intelligent automatic sleep staging model based on CNN and LSTM

Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value

Two Novel Tyrosinase Inhibitory Sesquiterpenes Induced by CuCl2 from a Marine-Derived Fungus Pestalotiopsis sp. Z233

Two new sesquiterpenes, 1β,5α,6α,14-tetraacetoxy-9α-benzoyloxy-7β H-eudesman-2β,11-diol (1) and 4α,5α-diacetoxy-9α-benzoyloxy-7βH-eudesman-1β,2β,11, 14-tetraol (2), were produced as stress metabolites in the cultured mycelia of Pestalotiopsis sp. Z233 isolated from the algae Sargassum horneri in response to abiotic stress elicitation by CuCl2. Their structures were established by spectroscopic means. New compounds 1 and 2 showed tyrosinase inhibitory activities with IC50 value of 14.8 µM and 22.3 µ

Development of a sensitive nested-polymerase chain reaction (PCR) assay for the detection of Ustilago scitaminea

A species-specific polymerase chain reaction (PCR) assay was developed for rapid and accurate detection of Ustilago scitaminea, the causal agent of sugarcane smut disease. Based on nucleotide differences in the internal transcribed spacer (ITS) sequences of U. scitaminea, a pair of species-specific primers, SL1 (5`-CAGTGCACGAAAGTACCTGTGG-3`) and SR2 (5`-CTAGGGCGGTGTTCAGAAGCAC-3`) was designed by using a panel of fungal and bacterial species as controls. The primers SL1/SR2 specifically amplified a unique PCR product about 530 bp in length from U. scitaminea strains with a detecting sensitivity at 200 fg of the fungal genomic DNA in a 25 μl reaction solution. To increase sensitivity, a nested-PCR protocol was further established, which used ITS4/ITS5 as the first-round primers followed by the primer pair SL1/SR2. This protocol increased the detection sensitivity by 10,000-fold compared to the PCR method and could detect the fungal DNA as low as 20 ag. The nested-PCR detected U. scitaminea from young sugarcane leaves with no visible smut disease symptoms. The findings from this study provide a sensitive and reliable technique for the early detection of U. scitaminea, which would be useful for sugarcane quarantine and production of germ-free seedcanes.Keywords: Sugarcane, Ustilago scitaminea, nested-polymerase chain reaction (PCR), molecular detectio

Genetic diversity of Ustilago scitaminea Syd. in Southern China revealed by combined ISSR and RAPD analysis

The polymorphism and similarity relationships among 35 mating-type isolates of Ustilago scitaminea collected from Southern China were determined with random amplified polymorphic DNA (RAPD) and inter-simple  sequence repeat (ISSR) analyses. These fungal isolates were collected from 16 sugarcane cultivars including F134 that is resistant to the physiological race 1 but susceptible to the race 2 of U. scitaminea, and N: Co376  that is immune to both races 1 and 2. Unweighted pair group method with arithmetic mean (UPGMA) cluster  analysis revealed that the U. scitaminea isolates could be divided into 2 groups with a coefficient of 0.74. The  first group comprises two isolates collected from the sugarcane cultivar F134, while the remaining 33 isolates were clustered into the second group. The second group was further divided into two subgroups with most of  the isolates from Guangdong Province which clustered in the same subgroup, and all the isolates from Guangxi  and Yunnan Provinces were clustered in another subgroup. Given that the member of the second group could  infect the cultivar N:Co376, which is immune to the races 1 and 2, our results suggest that majority of U.  scitaminea in sugarcane-producing regions of Southern China may belong to or genetically similar to race 3.Key words: Ustilago scitaminea, sugarcane, inter-simple sequence repeat (ISSR), random amplified  polymorphic deoxyribonucleic acid (DNA) (RAPD), genetic diversity

A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services

The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.Comment: To Appear in the Network and Distributed System Security Symposium (NDSS) 2024, San Diego, CA, US

Revealing ecotype influences on Cistanche sinensis: from the perspective of endophytes to metabolites characteristics

IntroductionPlant microorganism is critical to plant health, adaptability, and productive forces. Intriguingly, the metabolites and microorganisms can act upon each other in a plant. The union of metabolomics and microbiome may uncover the crucial connections of the plant to its microbiome. It has important benefits for the agricultural industry and human being health, particularly for Chinese medical science investigation.MethodsIn this last 2 years study, on the strength of the UPLC–MS/MS detection platform, we accurately qualitatively, and quantitatively measured the Cistanche sinensis fleshy stems of two ecotypes. Thereafter, through high-throughput amplicon sequencing 16S/ITS sequences were procured.ResultsPhGs metabolites including echinacoside, isoacteoside, and cistanoside A were significantly downregulated at two ecotypes of C. sinensis. Add up to 876 metabolites were monitored and 231 differential metabolites were analyzed. Further analysis of 34 core differential metabolites showed that 15 compounds with up-regulated belonged to phenolic acids, flavonoids, and organic acids, while 19 compounds with down-regulated belonged to phenolic acids, flavonoids, alkaloids, amino acids, lipids, and nucleotides. There was no noteworthy discrepancy in the endophytic bacteria’s α and β diversity between sandy and loam ecotypes. By comparison, the α and β diversity of endophytic fungi was notably distinct. The fungal community of the loam ecotype is more abundant than the sandy ecotype. However, there were few such differences in bacteria. Most abundant genera included typical endophytes such as Phyllobacterium, Mycobacterium, Cistanche, Geosmithia, and Fusarium. LEfSe results revealed there were 11 and 20 biomarkers of endophytic bacteria and fungi in C. sinensis at two ecotypes, respectively. The combination parsing of microflora and metabolites indicated noteworthy relativity between the endophytic fungal communities and metabolite output. Key correlation results that Anseongella was positive relation with Syringin, Arsenicitalea is negative relation with 7-methylxanthine and Pseudogymnoascus is completely positively correlated with nepetin-7-O-alloside.DiscussionThe aim of this research is: (1) to explore firstly the influence of ecotype on C. sinensis from the perspective of endophytes and metabolites; (2) to investigate the relationship between endophytes and metabolites. This discovery advances our understanding of the interaction between endophytes and plants and provides a theoretical basis for cultivation of C. sinensis in future

Symptoms of anxiety and depression predicting fall-related outcomes among older Americans: a longitudinal study

BackgroundAnxiety and depressive symptoms are associated with fear of falling and fear of falling-related activity restrictions. However, it remains unknown whether anxiety or depressive symptoms alone could predict fear of falling and activity restrictions in older adults. We sought to determine if anxiety and depressive symptoms alone could be an independent predictor of fear of falling and activity restrictions in community-dwelling older adults.MethodsThis longitudinal analysis used waves 5 (time 1, [T1]) and 6 (time 2, [T2], 1 year from T1) data (N = 6376) from the National Health and Aging Trends Study. The Generalized Anxiety Disorder Scale 2 and Patient Health Questionnaire 2 were used to assess anxiety and depressive symptoms, respectively. Interview questions included demographics, health-related data, and fall worry levels (no fear of falling, fear of falling but no activity restrictions, and activity restrictions). Using multinomial logistic regression models, we examined whether anxiety and depressive symptoms (T1) predicted fear of falling and activity restrictions (T2).ResultsIn wave 5 (T1, mean age: 78 years, 58.1% female), 10 and 13% of participants reported anxiety and depressive symptoms. About 19% of participants experienced fear of falling but not activity restrictions, and 10% of participants developed activity restrictions in wave 6 (T2), respectively. Participants with anxiety symptoms at T1 had a 1.33 times higher risk of fear of falling (95% CI = 1.02–1.72) and 1.41 times higher risk of activity restrictions (95% CI = 1.04–1.90) at T2. However, having depressive symptoms did not show any significance after adjusting for anxiety symptoms.ConclusionsAnxiety symptoms seemed to be an independent risk factor for future fear of falling and activity restrictions, while depressive symptoms were not. To prevent future fear of falling and activity restrictions, we should pay special attention to older individuals with anxiety symptoms.</p