This paper documents the evolution of Korea’s digital platforms. By using a historical approach in tandem with platformization — helpful in determining the causes behind the changing processes of new ...technologies — we examine the advancement of digital platforms. The digital platform era can be divided into three significant periods based on major technical advancements and corporate transformations, including the early construction of ICT infrastructure between the mid-1990s and early 2000s; the early platformization period of Internet portals amidst the smartphone revolution between the mid-2000s and mid-2010s; and the duopoly market of Naver and Kakao from the mid-2010s, after the merge of Daum and Kakao, to the present. Multiple causes led to the advent of digital platforms, both in terms of technologies and systems. Power relations developed between several major players, such as the government, corporations, and global forces. This work ultimately describes the relationship between sociocultural transitions and accompanying structural changes in digital platforms and relevant policies.
Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to ...state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
AbstractEntrepreneurship is widely acknowledged to be a phenomenon of central importance in our society. Within the arena of entrepreneurship policy, a more specific trend has been an increase in ...recent years in informal entrepreneurship, especially in developing countries. Therefore, this article seeks to contribute to filling a knowledge gap in the entrepreneurship literature using empirical evidence to assess the impact of the implementation of the Individual Micro Entrepreneur Act on the formalization of small businesses in Brazil. Based on empirical data retrieved from e-government portals, our study provides evidence that this legislation is being used as a large-scale legal instrument for labor market deregulation in many sectors. Additionally, we could also observe a strong increase in the number of Brazilian citizens officially registered as individual micro entrepreneurs, especially in urban areas and focusing on activities, such as hairdressers, manicures, pedicures, masonry works, and sales promotion. It was possible to observe that rather many informal workers did not become an entrepreneur, but a formal employee after the implementation if the Individual Micro Entrepreneur Act. Key-words: informal entrepreneurship; firm formalization; public policy; regional development; Brazil
AbstractObjectiveTo systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging ...with that of expert clinicians.DesignSystematic review.Data sourcesMedline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019.Eligibility criteria for selecting studiesRandomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax.Review methodsAdherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies.ResultsOnly 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required.ConclusionsFew prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.Study registrationPROSPERO CRD42019123605.
BACKGROUND Patient portals provide parents access to their child’s health information and direct communication with providers. Our study aimed to improve portal activation rates of newborns during ...nursery hospitalization to >70% over 6 months. Secondarily, we describe the facilitators and barriers to portal use. METHODS The study design used a mixed-methodology framework of quality improvement (QI) and cross-sectional analyses. The Model for Improvement guided QI efforts. The primary outcome was the proportion of portals activated for newborns during nursery hospitalization. Interventions included portal activation algorithm, staff huddles, and documentation templates. Telephone interviews were conducted with a randomized sample of mothers of infants who activated the portal. These mothers were divided into portal “users” and “nonusers.” We examined sociodemographic variables and health care utilization outcomes in the 2 groups. RESULTS Portal activation increased from 12.9% to 85.4% after interventions. Among 482 mothers with active portals, 127 (26.3%) were interviewed. Of those, 70% (89 of 127) reported using the portal, and 85.4% (76 of 89) found it useful. Reasons for accessing the portal included checking appointments and reviewing test results. Lack of knowledge of portal functionality was the main barrier to portal use (42.1%). Portal users were less likely to have a no-show to primary care appointments compared with nonusers (44.9% versus 78.9%, P < .001). CONCLUSIONS Portal activation rates increased after QI interventions in the nursery. Most parents accessed the portal and found it useful. Portals can improve health care delivery and patient engagement in the newborn period.
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