Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition

A Built-In Mechanism to Mitigate the Spread of Insect-Resistance and Herbicide-Tolerance Transgenes into Weedy Rice Populations

BACKGROUND: The major challenge of cultivating genetically modified (GM) rice (Oryza sativa) at the commercial scale is to prevent the spread of transgenes from GM cultivated rice to its coexisting weedy rice (O. sativa f. spontanea). The strategic development of GM rice with a built-in control mechanism can mitigate transgene spread in weedy rice populations. METHODOLOGY/PRINCIPAL FINDINGS: An RNAi cassette suppressing the expression of the bentazon detoxifying enzyme CYP81A6 was constructed into the T-DNA which contained two tightly linked transgenes expressing the Bt insecticidal protein Cry1Ab and the glyphosate tolerant 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), respectively. GM rice plants developed from this T-DNA were resistant to lepidopteran pests and tolerant to glyphosate, but sensitive to bentazon. The application of bentazon of 2000 mg/L at the rate of 40 mL/m(2), which is approximately the recommended dose for the field application to control common rice weeds, killed all F(2) plants containing the transgenes generated from the Crop-weed hybrids between a GM rice line (CGH-13) and two weedy rice strains (PI-63 and PI-1401). CONCLUSIONS/SIGNIFICANCE: Weedy rice plants containing transgenes from GM rice through gene flow can be selectively killed by the spray of bentazon when a non-GM rice variety is cultivated alternately in a few-year interval. The built-in control mechanism in combination of cropping management is likely to mitigate the spread of transgenes into weedy rice populations

Contemporary Economic Policy, forthcoming, 2002 URBANIZATION AND HEALTH CARE IN RURAL CHINA

Since the early 1980s, strong economic growth has led to remarkable urbanization across many urban and rural areas in China. Using longitudinal data from the China Health and Nutrition Survey (CHNS), this study obtained the first empirical estimates of how health insurance and access to care among the rural population were changed in response to the process of urbanization and other factors. The study results revealed strong evidence indicating that urbanization led to a significant and equitable increase in health insurance coverage for the rural population. Individual income also served as a critical determinant of insurance status. Moreover, adverse selection seemed to play an important role in deriving the individual demand for insurance. In analyzing the probability of seeking care when ill, the study found both the severity of illness (the need) and insurance coverage (enabling) to be crucial determinants. However, after controlling for insurance coverage, little variation in seeking care remained attributable to urbanization. Based on the findings, it can be concluded that financial barriers remain a major threat to seeking medical care in rural population. Urbanization may well help develop the local community-based insurance market in rural areas, thereby improving people’s insurance status and access to care

URBANIZATION AND HEALTH CARE IN RURAL CHINA

Strong economic growth has led to remarkable urbanization in China. Using the China Health and Nutrition Survey, this study provides the first empirical evidence documenting the impact of urbanization on rural health care and insurance. The primary finding is that urbanization leads to a significant and equitable increase in insurance coverage, which in turn plays a critical role in access to care. In addition, adverse selection exists in the demand for insurance. Income is also a significant determinant of insurance coverage. This study concludes that urbanization can help make substantial changes in rural health care and insurance status. Copyright 2003 Western Economic Association International.

A review of vision-based road detection technology for unmanned vehicles

With the development of unmanned vehicle technology, unmanned vehicles have played a huge role in logistics transportation, emergency rescue and disaster relief, etc., so the research on unmanned vehicles is becoming more and more important. Road detection is an important part of environmental perception and an important factor in the realization of assisted driving and unmanned driving technology. High-precision road detection technology can provide important environmental information for efficient planning and reasonable decision-making of unmanned vehicles. Firstly, the technical framework of road detection is given, and the road detection process is introduced in detail. Then, the vision-based road detection algorithm is introduced. Finally, some related data sets in the field of road detection are collected, which provides new ideas and methods for road detection researchers.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin