Orwellian Tourism 2020? China's Social Credit Score.

In 2014 the Chinese Communist Party (CCP) outlined their plans for the construction of a Social Credit System (SCS) with nation-wide implementation by 2020 (State Council of the People's Republic of China, 2014). The stated purpose is the continuous development of the socialist market economy through increasing trustworthiness and transparency of debtors and creditors, reducing risk of fraud and not fulfilling credit obligations (Baidu Baike, 2018). Details of how exactly the system will be implemented are blurry. The People’s Republic of China’s (PRC) government is monitoring how Chinese tech-giant “Alibaba” uses its large data base to compile individual “credit scores” for its users. At the same time, various Social Credit Systems are rolled out locally by the cities (Zhou, 2018). Users are rated based on a score between 350 and 950 points. Five factors are taken in account, the first one being “credit history”, the second “fulfilment capacity”, the third is “personal information”, the fourth is “behaviour and preference”, and the last is “interpersonal relationships” (Botsman, 2017). Currently, the SCS is not mandatory, but millions of people voluntarily signed up for trial runs. Higher scores enable Chinese citizens to access loans for shopping online, rent cars without deposits, fast-check in to hotels and Beijing airport, as well as getting fast-tracked for European Schengen visas (Carney, 2018). How the mandatory implementation of the SCS will affect the world’s largest outbound tourism market is however anyone’s guess

An attribute-based approach to classifying community-based tourism networks

2012-2013 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Annotation Practices in Computational Pathology: A European Society of Digital and Integrative Pathology (ESDIP) Survey Study

Integrating digital pathology and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists, and other researchers to ensure data quality and consistency. The lack of standardization and scalability is a significant challenge when generating annotations and annotated data sets. Recognizing these limitations, the Scientific Committee of the European Society of Digital and Integrative Pathology (ESDIP) performed a comprehensive international survey to understand the current state of annotation practices and identify actionable areas to address critical needs in the annotation process. The analysis and summary of the survey results provide several insights for all stakeholders involved in data preparation and ground truthing, ultimately contributing to the advancement of AI in computational pathology

MULTILAYER STRUCTURES BASED ON PCM WITH TUNABLE REFLECTION AND TRANSMISSION CHARACTERISTICS FOR FULLY OPTICAL ROUTING DEVICES

The reported study was funded by RFBR according to the research project # 19-37-60023

The Use of High Performance Liquid Chromatography for the Characterization of the Unfolding and Aggregation of Dairy Proteins

peer-reviewedHigh-performance liquid chromatography (HPLC) is routinely used to identify and characterize proteins. HPLC can help to understand protein aggregation processes in dairy products, which are induced by common industrial processing steps such as heat treatment. In this chapter, three complementary chromatographic methods are described, which are based on the principles of size exclusion and reversed-phase chromatography. These methods are used to determine the degree of denaturation and aggregation of proteins, and estimate the molecular weight of these aggregates

The clinical relevance of oliguria in the critically ill patient : Analysis of a large observational database

Funding Information: Marc Leone reports receiving consulting fees from Amomed and Aguettant; lecture fees from MSD, Pfizer, Octapharma, 3 M, Aspen, Orion; travel support from LFB; and grant support from PHRC IR and his institution. JLV is the Editor-in-Chief of Critical Care. The other authors declare that they have no relevant financial interests. Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Urine output is widely used as one of the criteria for the diagnosis and staging of acute renal failure, but few studies have specifically assessed the role of oliguria as a marker of acute renal failure or outcomes in general intensive care unit (ICU) patients. Using a large multinational database, we therefore evaluated the occurrence of oliguria (defined as a urine output 16 years) patients in the ICON audit who had a urine output measurement on the day of admission were included. To investigate the association between oliguria and mortality, we used a multilevel analysis. Results: Of the 8292 patients included, 2050 (24.7%) were oliguric during the first 24 h of admission. Patients with oliguria on admission who had at least one additional 24-h urine output recorded during their ICU stay (n = 1349) were divided into three groups: transient - oliguria resolved within 48 h after the admission day (n = 390 [28.9%]), prolonged - oliguria resolved > 48 h after the admission day (n = 141 [10.5%]), and permanent - oliguria persisting for the whole ICU stay or again present at the end of the ICU stay (n = 818 [60.6%]). ICU and hospital mortality rates were higher in patients with oliguria than in those without, except for patients with transient oliguria who had significantly lower mortality rates than non-oliguric patients. In multilevel analysis, the need for RRT was associated with a significantly higher risk of death (OR = 1.51 [95% CI 1.19-1.91], p = 0.001), but the presence of oliguria on admission was not (OR = 1.14 [95% CI 0.97-1.34], p = 0.103). Conclusions: Oliguria is common in ICU patients and may have a relatively benign nature if only transient. The duration of oliguria and need for RRT are associated with worse outcome.publishersversionPeer reviewe

Pathogenic and targetable genetic alterations in 70 urachal adenocarcinomas

Urachal cancer (UrC) is a rare but aggressive malignancy often diagnosed in advanced stages requiring systemic treatment. Although cytotoxic chemotherapy is of limited effectiveness, prospective clinical studies can hardly be conducted. Targeted therapeutic treatment approaches and potentially immunotherapy based on a biological rationale may provide an alternative strategy. We therefore subjected 70 urachal adenocarcinomas to targeted next-generation sequencing, conducted in situ and immunohistochemical analyses (including PD-L1 and DNA mismatch repair proteins (MMR)) and evaluated the microsatellite instability (MSI) status. The analytical findings were correlated with clinicopathological and outcome data and Kaplan-Meier and univariable/multivariable Cox regression analyses were performed. The patients had a mean age of 50 years, 66% were male and a 5-year overall survival (OS) of 58% and recurrence-free survival (RFS) of 45% was detected. Sequence variations were observed in TP53 (66%), KRAS (21%), BRAF (4%), PIK3CA (4%), FGFR1 (1%), MET (1%), NRAS (1%), and PDGFRA (1%). Gene amplifications were found in EGFR (5%), ERBB2 (2%), and MET (2%). We detected no evidence of MMR-deficiency (MMR-d)/MSI-high (MSI-h), whereas 10 of 63 cases (16%) expressed PD-L1. Therefore, anti-PD-1/PD-L1 immunotherapy approaches might be tested in UrC. Importantly, we found aberrations in intracellular signal transduction pathways (RAS/RAF/PI3K) in 31% of UrCs with potential implications for anti-EGFR therapy. Less frequent potentially actionable genetic alterations were additionally detected in ERBB2 (HER2), MET, FGFR1, and PDGFRA. The molecular profile strengthens the notion that UrC is a distinct entity on the genomic level with closer resemblance to colorectal than to bladder cancer. This article is protected by copyright. All rights reserved

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer

The aggressiveness of prostate cancer is primarily assessed from histopathological data using the Gleason scoring system. Conventional artificial intelligence (AI) approaches can predict Gleason scores, but often lack explainability, which may limit clinical acceptance. Here, we present an alternative, inherently explainable AI that circumvents the need for post-hoc explainability methods. The model was trained on 1,015 tissue microarray core images, annotated with detailed pattern descriptions by 54 international pathologists following standardized guidelines. It uses pathologist-defined terminology and was trained using soft labels to capture data uncertainty. This approach enables robust Gleason pattern segmentation despite high interobserver variability. The model achieved comparable or superior performance to direct Gleason pattern segmentation (Dice score: 0:713 ± 0:003 vs. 0:691 ± 0:010) while providing interpretable outputs. We release this dataset to encourage further research on segmentation in medical tasks with high subjectivity and to deepen insights into pathologists’ reasoning