Trends in worldwide nanotechnology patent applications: 1991 to 2008

Nanotechnology patent applications published during 1991–2008 have been examined using the “title–abstract” keyword search on esp@cenet “worldwide” database. The longitudinal evolution of the number of patent applications, their topics, and their respective patent families have been evaluated for 15 national patent offices covering 98% of the total global activity. The patent offices of the United States (USA), People’s Republic of China (PRC), Japan, and South Korea have published the largest number of nanotechnology patent applications, and experienced significant but different growth rates after 2000. In most repositories, the largest numbers of nanotechnology patent applications originated from their own countries/regions, indicating a significant “home advantage.” The top applicant institutions are from different sectors in different countries (e.g., from industry in the US and Canada patent offices, and from academe or government agencies at the PRC office). As compared to 2000, the year before the establishment of the US National Nanotechnology Initiative (NNI), numerous new invention topics appeared in 2008, in all 15 patent repositories. This is more pronounced in the USA and PRC. Patent families have increased among the 15 patent offices, particularly after 2005. Overlapping patent applications increased from none in 1991 to about 4% in 2000 and to about 27% in 2008. The largest share of equivalent nanotechnology patent applications (1,258) between two repositories was identified between the US and Japan patent offices

RNA-Seq Mapping and Detection of Gene Fusions with a Suffix Array Algorithm

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions–particularly those expressed with low abundance– is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample

Plasma miRNA as Biomarkers for Assessment of Total-Body Radiation Exposure Dosimetry

The risk of radiation exposure, due to accidental or malicious release of ionizing radiation, is a major public health concern. Biomarkers that can rapidly identify severely-irradiated individuals requiring prompt medical treatment in mass-casualty incidents are urgently needed. Stable blood or plasma-based biomarkers are attractive because of the ease for sample collection. We tested the hypothesis that plasma miRNA expression profiles can accurately reflect prior radiation exposure. We demonstrated using a murine model that plasma miRNA expression signatures could distinguish mice that received total body irradiation doses of 0.5 Gy, 2 Gy, and 10 Gy (at 6 h or 24 h post radiation) with accuracy, sensitivity, and specificity of above 90%. Taken together, these data demonstrate that plasma miRNA profiles can be highly predictive of different levels of radiation exposure. Thus, plasma-based biomarkers can be used to assess radiation exposure after mass-casualty incidents, and it may provide a valuable tool in developing and implementing effective countermeasures

An Experimental assessment of the performance of Linear and Kernel-based Methods for Face Recognition

This paper presents the results of a comparative study of linear and kernel-based methods for face recognition. These experimental results include: (1) a comparative study of linear methods for feature extraction, such as Principal Component Analysis (PCA), Fisher’s Linear Discriminant Analysis (FDA), and kernel based methods for feature extraction, such as Kernel based Principal Component Analysis (KPCA), Kernel based Discriminant Analysis (KDA). (2) a comparative study of linear methods for recognition or classification, such as Nearest Neighbor (NN), Linear Support Vector Machine (LSVM), and kernel based methods for classification, such as Kernel based Nearest Neighbor (KNN), Nonlinear Support Vector Machine (NSVM). In addition, we also obtain some interesting conclusions after all of these methods are performed on several well-known face database, i.e. ORL, YALE and UMIST Face Database, respectively. Key words: Linear methods; kernel based methods; feature extraction; face recognition; data structure of face databas