Predicting new venture survival and growth: does the fog lift?

This paper investigates whether new venture performance becomes easier to predict as the venture ages: does the fog lift? To address this question we primarily draw upon a theoretical framework, initially formulated in a managerial context by Levinthal (Adm Sci Q 36(3):397–420, 1991) that sees new venture sales as a random walk but survival being determined by the stock of available resources (proxied by size). We derive theoretical predictions that are tested with a 10-year cohort of 6579 UK new ventures in the UK. We observe that our ability to predict firm growth deteriorates in the years after entry—in terms of the selection environment, the ‘fog’ seems to thicken. However, our survival predictions improve with time—implying that the ‘fog’ does lift

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.publishedVersio

GENCODE 2021

© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org.National Human Genome Research Institute of the National Institutes of Health [U41HG007234]; the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health; Wellcome Trust [WT108749/Z/15/Z, WT200990/Z/16/Z]; European Molecular Biology Laboratory; Swiss National Science Foundation through the National Center of Competence in Research ‘RNA & Disease’ (to R.J.); Medical Faculty of the University of Bern (to R.J). Funding for open access charge: National Institutes of Health

GENCODE: reference annotation for the human and mouse genomes in 2023

Data availability: No new data were generated or analysed in support of this research.Copyright © The Author(s) 2022. GENCODE produces high quality gene and transcript annotation for the human and mouse genomes. All GENCODE annotation is supported by experimental data and serves as a reference for genome biology and clinical genomics. The GENCODE consortium generates targeted experimental data, develops bioinformatic tools and carries out analyses that, along with externally produced data and methods, support the identification and annotation of transcript structures and the determination of their function. Here, we present an update on the annotation of human and mouse genes, including developments in the tools, data, analyses and major collaborations which underpin this progress. For example, we report the creation of a set of non-canonical ORFs identified in GENCODE transcripts, the LRGASP collaboration to assess the use of long transcriptomic data to build transcript models, the progress in collaborations with RefSeq and UniProt to increase convergence in the annotation of human and mouse protein-coding genes, the propagation of GENCODE across the human pan-genome and the development of new tools to support annotation of regulatory features by GENCODE. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org.National Human Genome Research Institute of the National Institutes of Health [U41HG007234, R01HG004037]; Wellcome Trust [WT222155/Z/20/Z]; European Molecular Biology Laboratory. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding for open access charge: National Institutes of Health

Innovation and self-employment

The chapter adds to the literature on innovation and employment by looking at the relationship between R&D investment and the rise of alternative work arrangements, particularly self-employment (SE). The chapter first looks at the emergence of nonstandard work, alternative work arrangements and self-employment. General trends of SE in Europe are considered. The contributions that have looked at SE in relation to innovation strategies are surprisingly limited. The empirical contribution is focused on the analysis of local labor markets in the UK (Travel-to-Work Areas, TTWAs), and considers the initial concentration of routinized and non-routinized jobs. The probability that an individual shifts from paid employment to either unemployment or self-employment over the period 2001–2013 as linked to changes in R&D investment in the TTWA is empirically accounted for. Results show that overall R&D has negligible effects on the probability of workers to become self-employed. R&D increases the probability of moving from unemployment to paid employment, especially in routinized areas, and reduces the permeability between routinized and non-routinized workers. Also, a non-negligible increase in the probability that a routinized worker becomes SE as a result of R&D increase is found in low routinized local labor markets but not in highly routinized areas. The chapter sheds new lights on the effect of R&D on employment and self-employment in areas with different degrees of routinization, and adds to the discussion on the more general raise of alternative work arrangements in Europe by disentangling the characteristics of self-employment as resulting from R&D investment