Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

Digenean parasites of Chinese marine fishes: a list of species, hosts and geographical distribution

In the literature, 630 species of Digenea (Trematoda) have been reported from Chinese marine fishes. These belong to 209 genera and 35 families. The names of these species, along with their hosts, geographical distribution and records, are listed in this paper

Application of amino acid occurrence for discriminating different folding types of globular proteins

<p>Abstract</p> <p>Background</p> <p>Predicting the three-dimensional structure of a protein from its amino acid sequence is a long-standing goal in computational/molecular biology. The discrimination of different structural classes and folding types are intermediate steps in protein structure prediction.</p> <p>Results</p> <p>In this work, we have proposed a method based on linear discriminant analysis (LDA) for discriminating 30 different folding types of globular proteins using amino acid occurrence. Our method was tested with a non-redundant set of 1612 proteins and it discriminated them with the accuracy of 38%, which is comparable to or better than other methods in the literature. A web server has been developed for discriminating the folding type of a query protein from its amino acid sequence and it is available at http://granular.com/PROLDA/.</p> <p>Conclusion</p> <p>Amino acid occurrence has been successfully used to discriminate different folding types of globular proteins. The discrimination accuracy obtained with amino acid occurrence is better than that obtained with amino acid composition and/or amino acid properties. In addition, the method is very fast to obtain the results.</p

From BIM towards digital twin: Strategy and future development for smart asset management

With the rising adoption of Building Information Model (BIM) for as-set management within architecture, engineering, construction and owner-operated (AECO) sector, BIM-enabled asset management has been increasingly attracting more attentions in both research and practice. This study provides a comprehensive review and analysis of the state-of-the-art latest research and industry standards development that impact upon BIM and asset management within the operations and maintenance (O&M) phase. However, BIM is not always enough in whole-life cycle asset management, especially in the O&M phase. Therefore, a framework for future development of smart asset management are proposed, integrating the concept of Digital Twin (DT). DT integrates artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. The findings will contribute to inspiring novel research ideas and promote wide-spread adoption of smart DT-enabled asset management within the O&M phaseCentre for Digital Built Britain, Innovate U

New Colloidal Lithographic Nanopatterns Fabricated by Combining Pre-Heating and Reactive Ion Etching

We report a low-cost and simple method for fabrication of nonspherical colloidal lithographic nanopatterns with a long-range order by preheating and oxygen reactive ion etching of monolayer and double-layer polystyrene spheres. This strategy allows excellent control of size and morphology of the colloidal particles and expands the applications of the colloidal patterns as templates for preparing ordered functional nanostructure arrays. For the first time, various unique nanostructures with long-range order, including network structures with tunable neck length and width, hexagonal-shaped, and rectangular-shaped arrays as well as size tunable nanohole arrays, were fabricated by this route. Promising potentials of such unique periodic nanostructures in various fields, such as photonic crystals, catalysts, templates for deposition, and masks for etching, are naturally expected

Developing a dynamic digital twin at a building level: Using Cambridge campus as case study

A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoT)-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper

A Piezoelectric Immunosensor Using Hybrid Self-Assembled Monolayers for Detection of Schistosoma japonicum

BACKGROUND: The parasite Schistosoma japonicum causes schistosomiasis disease, which threatens human life and hampers economic and social development in some Asian countries. An important lesson learned from efforts to reduce the occurrence of schistosomiasis is that the diagnostic approach must be altered as further progress is made towards the control and ultimate elimination of the disease. METHODOLOGY/PRINCIPAL FINDINGS: Using mixed self-assembled monolayer membrane (mixed SAM) technology, a mixture of mercaptopropionic acid (MPA) and mercaptoethanol (ME) was self-assembled on the surface of quartz crystals by gold-sulphur-bonds. Soluble egg antigens (SEA) of S. japonicum were then cross-linked to the quartz crystal using a special coupling agent. As compared with the traditional single self-assembled monolayer immobilization method, S. japonicum antigen (SjAg) immobilization using mixed self-assembled monolayers exhibits much greater immunoreactivity. Under optimal experimental conditions, the detection range is 1:1500 to 1:60 (infected rabbit serum dilution ratios). We measured several infected rabbit serum samples with varying S. japonicum antibody (SjAb) concentrations using both immunosensor and ELISA techniques and then produced a correlation analysis. The correlation coefficients reached 0.973. CONCLUSIONS/SIGNIFICANCE: We have developed a new, simple, sensitive, and reusable piezoelectric immunosensor that directly detects SjAb in the serum. This method may represent an alternative to the current diagnostic methods for S. japonicum infection in the clinical laboratory or for analysis outside the laboratory

Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles

Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients