Fuels and chemicals from biomass using solar thermal energy

The significant nearer term opportunities for the application of solar thermal energy to the manufacture of fuels and chemicals from biomass are summarized, with some comments on resource availability, market potential and economics. Consideration is given to the production of furfural from agricultural residues, and the role of furfural and its derivatives as a replacement for petrochemicals in the plastics industry

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Fabrication of full-scale fiber-reinforced hot-gas filters by chemical vapor depostion. Final report, November 1, 1994 -- December 32, 1995

The overall goal of this contract and its extensions has been to develop a hot gas candle filter which is light weight, has a thin wall, resists mechanical and thermal shock, and is resistive to alkali attack. A ceramic fiber reinforced, ceramic matrix composite approach has been followed to fabricate this new candle filter. Past reports covered the first test results of two ceramic composite candle filters at the Westinghouse Science and Technology Center in March of 1993, subsequent improvements made in the filters construction and fabrication processing, and the testing of six improved full size, 60 mm diameter by 1575 mm length, filters that met or exceeded performance requirements set for them. Completion of the 172 hours of simulated PFBC testing and thermal transients plus maintaining less than 4 ppm clean side ash concentration provided a basis for moving to the next step of testing in the Tidd PFBCC Demonstration Project. In this contract extension 3M fabricated 110 filters to be used for tests in demonstration power plant facilities and other tests that become available. The filters were tested to meet all quality assurance specifications and inventoried for Oak Ridge National Laboratory, ORNL. The filters are being shipped to various industrial, university, and national laboratory test facilities as requested by ORNL. Ten ceramic composite filters were installed in December, 1994 in the Tidd PFBC Demonstration Project filter vessel for their test period No. 5. Five filters were installed in a top cluster and five in a bottom cluster. The filters were removed in May 1995 after operating for 1 1 1 0 hours in a temperature range of 760{degrees}C to 843{degrees}C, with 80% of the run above 815{degrees}C