Abstract-In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks' parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective way to diagnosis Parkinson's disease by using the speech signal.
Single mutations can confer resistance to antibiotics. Identifying such mutations can help to develop and improve drugs. Here, we systematically screen for candidate quinolone resistance-conferring mutations. We sequenced highly diverse wastewater E. coli and performed a genome-wide association study (GWAS) to determine associations between over 200,000 mutations and quinolone resistance phenotypes. We uncovered 13 statistically significant mutations including 1 located at the active site of the biofilm dispersal gene bdcA and 6 silent mutations in the aminoacyl-tRNA synthetase valS. The study also recovered the known mutations in the topoisomerases gyrase (gyrA) and topoisomerase IV (parC). In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations without a priori knowledge of targets and mode of action. The results suggest that mutations in the bdcA and valS genes, which are involved in biofilm dispersal and translation, may lead to novel resistance mechanisms.
Biofilm-forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit submerged and intertidal substrata. The development of a unique form of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. Gliding motility is hypothesized to be driven by an intracellular actin-myosin motor and requires the secretion of polysaccharide- and protein-based adhesive materials. To date, the structure-function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Proteomics analysis of the adhesive material from Craspedostauros australis revealed eight novel, diatom-specific proteins. Four of them constitute a new family of proteins, named Trailins, which contain an enigmatic domain termed Choice-of-Anchor-A (CAA). Immunostaining demonstrated that Trailins are only present in the adhesive trails required to generate traction on native substrata, but are absent from the extracellular matrix of biofilms. Phylogenetic analysis and Protein 3D structure prediction suggests that the CAA-domains in Trailins were obtained from bacteria by horizontal gene transfer, and exhibit a striking structural similarity to ice-binding proteins. Our work advances the understanding of the molecular basis for diatom underwater adhesion and biofilm formation providing evidence that there is a molecular switch between proteins required for initial surface colonization and those required for 3D biofilm matrix formation.
Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.
For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.
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