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Evolution on the 15-year period of the medical qualities and also outcomes of significantly sick people together with severe community-acquired pneumonia.
As the cornerstone for joint dimension reduction and feature extraction, extensive linear projection algorithms were proposed to fit various requirements. When being applied to image data, however, existing methods suffer from representation deficiency since the multi-way structure of the data is (partially) neglected. To solve this problem, we propose a novel Low-Rank Preserving t-Linear Projection (LRP-tP) model that preserves the intrinsic structure of the image data using t-product-based operations. BMS-232632 inhibitor The proposed model advances in four aspects 1) LRP-tP learns the t-linear projection directly from the tensorial dataset so as to exploit the correlation among the multi-way data structure simultaneously; 2) to cope with the widely spread data errors, e.g., noise and corruptions, the robustness of LRP-tP is enhanced via self-representation learning; 3) LRP-tP is endowed with good discriminative ability by integrating the empirical classification error into the learning procedure; 4) an adaptive graph considering the similarity and locality of the data is jointly learned to precisely portray the data affinity. We devise an efficient algorithm to solve the proposed LRP-tP model using the alternating direction method of multipliers. Extensive experiments on image feature extraction have demonstrated the superiority of LRP-tP compared to the state-of-the-arts.Female breast cancer is one of the leading types of cancers worldwide. This paper presents a case study of Malwa Belt in India that has witnessed the proliferation in the overall mortality rate due to breast cancer. The paper researches mortality aspect of disease and its association with the various risk parameters including demographic characteristics, percentage of pesticides residue present in the water and soil, life style of the women in the affected area, water intake, and the amount of pesticide exposure to the patient. The levels of organochlorine pesticides like DDT and its metabolites and isomers of HCH in blood, tumor and surrounding adipose are estimated. Additionally, an extent of exposure of the subjects to environmental pollutants like heavy metals (Lead, Copper, Iron, Zinc, Calcium, Selenium, and Chromium etc.) are also examined. For the obtained experimental data, an efficient ensemble machine learning based framework called Bagoost is proposed to predict the risk of breast cancer in Malwa women.The performance of the proposed machine learning model results in an accuracy of 98.21%, when empirically tested using K fold cross validation over the real time data of malignant and benign cases and is established to be efficacious than the existing approaches.The protein fold recognition is a fundamental and crucial step of tertiary structure determination. In this regard, several computational predictors have been proposed. Recently, the predictive performance has been obviously improved by the fold-specific features generated by deep learning techniques. However, these methods failed to measure the global associations among residues or motifs along the protein sequences. Furthermore, these deep learning techniques are often treated as black boxes without interpretability. Inspired by the similarities between protein sequences and natural language sentences, we applied the self-attention mechanism derived from natural language processing (NLP) field to protein fold recognition. The motif-based self-attention network (MSAN) and the residue-based self-attention network (RSAN) capture the global associations among the structure motifs and residues along the protein sequences, respectively. The fold-specific attention features trained and generated from the training set were then combined with Support Vector Machines (SVMs) to predict the samples in the widely used LE benchmark dataset. Experimental results showed that the proposed two SelfAT-Fold predictors outperformed 34 existing state-of-the-art computational predictors. The two SelfAT-Fold predictors were further tested on an independent dataset SCOP_TEST, and they can achieve stable performance. The models and data of SelfAT-Fold can be downloaded from http//bliulab.net/selfAT_fold/.This work demonstrates the ultrasonic imaging ability of piezoelectric micromachined ultrasonic transducer (PMUT) for the detection of defects deep in solids by total-focus imaging algorithm. The 3-MHz PMUT array uses thin-film PZT as the material for energy transformation because of its high piezoelectric coefficient. Six columns with 12 PMUT units each exhibit an acoustic pressure of 137 kPa measured in water at 1 cm away when driven by a 27-Vpp input. Butter is chosen experimentally as the coupling agent to solids because of its low noise level and short cycling down. Through multilevel processing of echoes and total-focus algorithm, the 2-D image of a graphite plant was obtained with four holes identified based on our customized impedance-matched imaging system. The 16 columns of PMUT array with an area of 5.8 mm ×4.2 mm exhibit the imaging ability of an area of 40 mm ×40 mm in the graphite plant with identified defects deep as 3 cm. These results indicate that PMUT has the detection and imaging ability for defects deep in solids, not merely surface within hundreds of micrometers, and it has the potential for 3-D imaging, especially those occasions in limited space.During a study of yeast diversity in Azorean vineyards, four strains were isolated which were found to represent a novel yeast species based on the sequences of the internal transcribed spacer (ITS) region (ITS1-5.8S-ITS2) and of the D1/D2 domain of the large subunit (LSU) rRNA gene, together with their physiological characteristics. An additional strain isolated from Drosophila suzukii in Italy had identical D1/D2 sequences and very similar ITS regions (five nucleotide substitutions) to the Azorean strains. Phylogenetic analysis using sequences of the ITS region and D1/D2 domain showed that the five strains are closely related to Clavispora lusitaniae, although with 56 nucleotide differences in the D2 domain. Intraspecies variation revealed between two and five nucleotide differences, considering the five strains of Clavispora santaluciae. Some phenotypic discrepancies support the separation of the new species from their closely related ones, such as the inability to grow at temperatures above 35 °C, to produce acetic acid and the capacity to assimilate starch.
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