A typical threshold digital signature scheme is analyzed,which is conspiratorially attacked successfully.So it is proved to be unsafe.In addition,other weaknesses of the scheme are discussed.
Protein-protein interactions (PPIs) play essential roles for determining the outcomes of most of the cellular functions of the cell. Although the experimentally detected high-throughput PPI data promise new opportunities for the study of many biological mechanisms including cellular metabolism and protein functions, experimentally detected PPIs have high levels of false positive rate. Therefore, it is of high practical value to develop novel computational tools for pruning low-confidence PPIs. In this paper, we propose a new geometric approach called Leave-One-Out Logistic Metric Embedding (LOO-LME) for assessing the reliability of interactions. Unlike previous approaches which mainly seek to preserve the noisy topological information of the PPI networks in the embedding space, LOO-LME first transforms the learning task into an equivalent discriminant form, then directly deals with the uncertainty in PPI networks using a leave-one-out-style approach. The experimental results show that LOO-LME substantially outperforms previous methods on PPI assessment problems. LOO-LME could thus facilitate further graph-based studies of PPIs and may help infer their hidden underlying biological knowledge.
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-protein binding. These methods can hardly meet the need of consideration of the dependencies between subsequence and the various motif lengths of different translation factors (TFs). To overcome such limitations, we propose a predictive model that utilizes a combination of multi-scale convolutional layers and bidirectional gated recurrent unit (GRU) layer. Multi-scale convolution layer has the ability to capture the motif features of different lengths, and bidirectional GRU layer is able to capture the dependencies among subsequence. Experimental results show that the proposed method performs better than four state-of-the-art methods in this field. In addition, we investigate the effect of model structure on model performance by performing our proposed method with a different convolution layer and a different number of kernel size. We also demonstrate the effectiveness of bidirectional GRU in improving model performance through comparative experiments.
Lactic acid has aroused increasing attention due to its close association with serious diseases. A real-time, dynamic, and intelligent detection method is vital for sensitive detection of lactic acid. Here, a machine learning (ML)-assisted perspiration-driven self-powered sensor (PDS sensor) is fabricated using Ni-ZIF-8@lactate oxidase and pyruvate oxidase (Ni-ZIF-8@LOx&POx)/laser-induced graphene (LIG), bilirubin oxidase (BOD)/LIG, and a microchannel for highly sensitive and real-time monitoring of lactic acid in sweat. Driven by the oxidation reaction of lactic acid, PDS sensors exhibit excellent sensitivity, a wide detection range, good reproducibility, and excellent selectivity for lactic acid detection in sweat. When subjects with different body mass index (BMI) undergo aerobic or anaerobic exercise or maintain a sedentary state, PDS sensors can monitor lactic acid in sweat wirelessly and in real-time. Moreover, a ML algorithm was employed to assist PDS sensors to detect lactic acid in the subjects' sweat with a high prediction accuracy of 96.0%.
In order to make clear the safety of Shegan Mahuang Dilong powder and provide date support for the clinical medication,120 21-day-old Sanhuang broilers were randomly divided into four groups including 3,6,12 times dose group and control group,every group had thirty broilers.When the chickens were 21 and 35 days old,according to the chickens' average weight of each group,the drug of different doses was fed to the chicken and the drug was fed for 2 weeks continuously.When the chickens were 35 and 49 days old,10 chickens in each group were randomly selected for blood collection and necropsy.Then the parameters of kidney and liver functions were measured and the pathological examination of hearts,livers,spleens,lungs and kidneys was carried out.The results showed that there was no regularity between the serum alanine aminotransferase activity,creatinine concentration and drug's dosages of each group while the levels of albumin and urea had no significant differences compared with the control group(P0.05).After administration,the chickens' hearts,livers,spleens,lungs and kidneys were no significant differences in the performance of normal in all groups.The results showed that Shegan Mahuang Dilong powder had small interference to kidney and liver functions of the chickens.And there were no toxic effects to the heart,liver,spleen,lungs and kidney after pathological examination.So it was a low toxic and safe traditional Chinese medicine preparation which was suitable for clinical use.
Abstract Cold-induced thermogenesis increases energy expenditure and can reduce body weight in mammals, so the genes involved in it are thought to be potential therapeutic targets for treating obesity and diabetes. In the quest for more effective therapies, a great deal of research has been conducted to elucidate the regulatory mechanism of cold-induced thermogenesis. Over the last decade, a large number of genes that can enhance or suppress cold-induced thermogenesis have been discovered, but a comprehensive list of these genes is lacking. To fill this gap, we examined all of the annotated human and mouse genes and curated those demonstrated to enhance or suppress cold-induced thermogenesis by in vivo or ex vivo experiments in mice. The results of this highly accurate and comprehensive annotation are hosted on a database called CITGeneDB, which includes a searchable web interface to facilitate broad public use. The database will be updated as new genes are found to enhance or suppress cold-induced thermogenesis. It is expected that CITGeneDB will be a valuable resource in future explorations of the molecular mechanism of cold-induced thermogenesis, helping pave the way for new obesity and diabetes treatments. Database URL: http://citgenedb.yubiolab.org
In order to comprehensively utilize complementary information from multiple types of data for better disease diagnosis, in this study, we applied a network fusion based approach to integrating three types of data including genetic, epigenetic and neuroimaging data from a study of schizophrenia patients (SCZ). A network is a map of interactions, which contributes to investigating the connectivity of components or links between sub-units. We exploited the potential of using networks as features for discriminating SCZ from healthy controls. We first constructed a single network from each type of data. Then we built four fused networks by the network fusion method: three fused networks for each combination of two types of data and one fused network for all three data types. Based on the local consistency of network, we can predict the group of the unlabeled SCZ subjects. The group prediction method was applied to test the power of network-based features and the performance was evaluated by a 10-fold cross validation. The results show that the prediction accuracy is the highest when applying our prediction method to the fused network derived from three data types among 7 tested networks. As a conclusion, integrative approaches that can comprehensively utilize multiple types of data are more useful for diagnosis and prediction.
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