SARS Coronavirus 3b Accessory Protein Modulates Transcriptional Activity of RUNX1b

BACKGROUND: The causative agent of severe acute respiratory syndrome, SARS coronavirus (SARS-CoV) genome encodes several unique group specific accessory proteins with unknown functions. Among them, accessory protein 3b (also known as ORF4) was lately identified as one of the viral interferon antagonist. Recently our lab uncovered a new role for 3b in upregulation of AP-1 transcriptional activity and its downstream genes. Thus, we believe that 3b might play an important role in SARS-CoV pathogenesis and therefore is of considerable interest. The current study aims at identifying novel host cellular interactors of the 3b protein. METHODOLOGY/PRINCIPAL FINDINGS: In this study, using yeast two-hybrid and co-immunoprecipitation techniques, we have identified a host transcription factor RUNX1b (Runt related transcription factor, isoform b) as a novel interacting partner for SARS-CoV 3b protein. Chromatin immunoprecipitaion (ChIP) and reporter gene assays in 3b expressing jurkat cells showed recruitment of 3b on the RUNX1 binding element that led to an increase in RUNX1b transactivation potential on the IL2 promoter. Kinase assay and pharmacological inhibitor treatment implied that 3b also affect RUNX1b transcriptional activity by regulating its ERK dependent phosphorylation levels. Additionally, mRNA levels of MIP-1α, a RUNX1b target gene upregulated in SARS-CoV infected monocyte-derived dendritic cells, were found to be elevated in 3b expressing U937 monocyte cells. CONCLUSIONS/SIGNIFICANCE: These results unveil a novel interaction of SARS-CoV 3b with the host factor, RUNX1b, and speculate its physiological relevance in upregulating cytokines and chemokine levels in state of SARS virus infection

Global profiling of co- and post-translationally N-myristoylated proteomes in human cells

Protein N-myristoylation is a ubiquitous co- and post-translational modification that has been implicated in the development and progression of a range of human diseases. Here, we report the global N-myristoylated proteome in human cells determined using quantitative chemical proteomics combined with potent and specific human N-myristoyltransferase (NMT) inhibition. Global quantification of N-myristoylation during normal growth or apoptosis allowed the identification of >100 N-myristoylated proteins, >95% of which are identified for the first time at endogenous levels. Furthermore, quantitative dose response for inhibition of N-myristoylation is determined for >70 substrates simultaneously across the proteome. Small-molecule inhibition through a conserved substrate-binding pocket is also demonstrated by solving the crystal structures of inhibitor-bound NMT1 and NMT2. The presented data substantially expand the known repertoire of co- and post-translational N-myristoylation in addition to validating tools for the pharmacological inhibition of NMT in living cells

AI recognition of patient race in medical imaging: a modelling study

Background Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91-0·99], CT chest imaging [0·87-0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study. Interpretation The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. Funding National Institute of Biomedical Imaging and Bioengineering, MIDRC grant of National Institutes of Health, US National Science Foundation, National Library of Medicine of the National Institutes of Health, and Taiwan Ministry of Science and Technology

Reading Race: AI Recognises Patient's Racial Identity In Medical Images

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to

Design and Evaluation of Vacuum Central Drum Seed Metering Device

For the purpose of reducing the number of conventional seed-metering devices and high demand for vacuums, this study designed a vacuum central drum seed-metering device, that is intended to replace multiple seed-metering devices with one, which is comprised of the inner and outer drum. This can be replaced with different diameters of suction holes, ventilation housing, seeding tube, agitating devices, seed box, and seed-cleaning devices, etc. A hybrid rice seed Jingliangyou 1212 was applied as an experimental material, and a JPS-12 computer vision metering device test bench was used to test singular-factor and multi-factor seeding performance of the seed-metering device. The singular-factor performance test of the metering device was conducted under negative pressure of seed suction in the range of 1∼1.5 kPa, at the metering device rotation speed of 10∼60 rpm, with diameters of inlet holes being 2 mm (chamfer: 45°), 2, and 1.5 mm, respectively. The number of seeds was counted by a sucking hole under different factor combinations. The multi-factor test was carried out by rotation rate, negative pressure, and types of sucking holes. A rate of 2 ± 1 rice seed per sucking hole is regarded as the qualified standard. It shows that the qualification rate (2 ± 1 rice seed per sucking hole) of seed suction can reach 97.4% under a combination of metering device rotation speed of 30 r/min, negative pressure of 1.0 kPa, and suction hole diameters of 1.5 mm. High-speed photography was used to study the trajectory of seed-metering at different rotation rates, a locomotive axis was applied to fit the motion curve, and 3D-printing was used to make the seed-metering tube so that the seed collisions could be reduced. This study provides evidence for further optimizing the performance of the vacuum central drum direct-seeding machine for hybrid rice

Design and Evaluation of Vacuum Central Drum Seed Metering Device

For the purpose of reducing the number of conventional seed-metering devices and high demand for vacuums, this study designed a vacuum central drum seed-metering device, that is intended to replace multiple seed-metering devices with one, which is comprised of the inner and outer drum. This can be replaced with different diameters of suction holes, ventilation housing, seeding tube, agitating devices, seed box, and seed-cleaning devices, etc. A hybrid rice seed Jingliangyou 1212 was applied as an experimental material, and a JPS-12 computer vision metering device test bench was used to test singular-factor and multi-factor seeding performance of the seed-metering device. The singular-factor performance test of the metering device was conducted under negative pressure of seed suction in the range of 1∼1.5 kPa, at the metering device rotation speed of 10∼60 rpm, with diameters of inlet holes being 2 mm (chamfer: 45°), 2, and 1.5 mm, respectively. The number of seeds was counted by a sucking hole under different factor combinations. The multi-factor test was carried out by rotation rate, negative pressure, and types of sucking holes. A rate of 2 ± 1 rice seed per sucking hole is regarded as the qualified standard. It shows that the qualification rate (2 ± 1 rice seed per sucking hole) of seed suction can reach 97.4% under a combination of metering device rotation speed of 30 r/min, negative pressure of 1.0 kPa, and suction hole diameters of 1.5 mm. High-speed photography was used to study the trajectory of seed-metering at different rotation rates, a locomotive axis was applied to fit the motion curve, and 3D-printing was used to make the seed-metering tube so that the seed collisions could be reduced. This study provides evidence for further optimizing the performance of the vacuum central drum direct-seeding machine for hybrid rice

Optimizing Hill Seeding Density for High-Yielding Hybrid Rice in a Single Rice Cropping System in South China

Mechanical hill direct seeding of hybrid rice could be the way to solve the problems of high seeding rates and uneven plant establishment now faced in direct seeded rice; however, it is not clear what the optimum hill seeding density should be for high-yielding hybrid rice in the single-season rice production system. Experiments were conducted in 2010 and 2011 to determine the effects of hill seeding density (25 cm 615 cm, 25 cm 617 cm, 25 cm 619 cm, 25 cm 621 cm, and 25 cm 623 cm; three to five seeds per hill) on plant growth and grain yield of a hybrid variety, Nei2you6, in two fields with different fertility (soil fertility 1 and 2). In addition, in 2012 and 2013, comparisons among mechanical hill seeding, broadcasting, and transplanting were conducted with three hybrid varieties to evaluate the optimum seeding density. With increases in seeding spacing from 25 cm615 cm to 25 cm623 cm, productive tillers per hill increased by 34.2% and 50.0% in soil fertility 1 and 2. Panicles per m2 declined with increases in seeding spacing in soil fertility 1. In soil fertility 2, no difference in panicles per m2 was found at spacing ranging from 25 cm617 cm to 25 cm623 cm, while decreases in the area of the top three leaves and aboveground dry weight per shoot at flowering were observed. Grain yield was the maximum at 25 cm 617 cm spacing in both soil fertility fields. Our results suggest that a seeding density of 25 cm617 cm was suitable for high-yielding hybrid rice. These results were verified through on-farm demonstration experiments, in which mechanical hill-seeded rice at this density had equal or higher grain yield than transplanted ric

Optimizing hill seeding density for high-yielding hybrid rice in a single rice cropping system in South China

Mechanical hill direct seeding of hybrid rice could be the way to solve the problems of high seeding rates and uneven plant establishment now faced in direct seeded rice; however, it is not clear what the optimum hill seeding density should be for high-yielding hybrid rice in the single-season rice production system. Experiments were conducted in 2010 and 2011 to determine the effects of hill seeding density (25 cm ×15 cm, 25 cm×17 cm, 25 cm×19 cm, 25 cm×21 cm, and 25 cm×23 cm; three to five seeds per hill) on plant growth and grain yield of a hybrid variety, Nei2you6, in two fields with different fertility (soil fertility 1 and 2). In addition, in 2012 and 2013, comparisons among mechanical hill seeding, broadcasting, and transplanting were conducted with three hybrid varieties to evaluate the optimum seeding density. With increases in seeding spacing from 25 cm×15 cm to 25 cm623 cm, productive tillers per hill increased by 34.2% and 50.0% in soil fertility 1 and 2. Panicles per m declined with increases in seeding spacing in soil fertility 1. In soil fertility 2, no difference in panicles per m was found at spacing ranging from 25 cm×17 cm to 25 cm×23 cm, while decreases in the area of the top three leaves and aboveground dry weight per shoot at flowering were observed. Grain yield was the maximum at 25 cm×17 cm spacing in both soil fertility fields. Our results suggest that a seeding density of 25 cm×17 cm was suitable for high-yielding hybrid rice. These results were verified through on-farm demonstration experiments, in which mechanical hill-seeded rice at this density had equal or higher grain yield than transplanted rice

Design of and Experiment on a Cleaning Mechanism of the Pneumatic Single Seed Metering Device for Coated Hybrid Rice

In order to improve the single-grain seeding rate of the pneumatic single seed metering device, an airflow seed cleaning device was designed in combination with positive pressure airflow. The influence of the position of the seed cleaning mechanism on the seed cleaning effect is theoretically analyzed and a flow field simulation test analysis of different nozzle structures was carried out by using Fluent software (ANSYS, Inc., Canonsburg, PA, USA). The results of this test show that a nozzle with a Witoszynski curve has good airflow concentration and uniform air pressure distribution. In order to verify the performance of the seed cleaning mechanism, a 0.7 times coated seed (hybrid rice Wuyou 1179) was used as the test material and a quadratic regression test with three levels was carried out with the rotation speed of the seed plate, the negative pressure of the suction chamber, and the positive pressure of the seed cleaning as the test factors. The results showed that when the speed of sucking plate was 30 r/min, the negative pressure of the suction chamber was 1.8 kPa and the positive pressure of the seed cleaning was 0.2 kPa; the seeding effect was at its best and the qualified rate of the seed metering device was the highest at 86.43%, the minimum leakage rate was 3.81%, and the multiple rate was 9.76%. The proposed seed cleaning mechanism effectively improves the accuracy of seeding and provides a certain theoretical basis for the single-grain sowing of hybrid rice