For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. The COVID-19 pandemic has rapidly propagated due to widespread person-to-person transmission 1-6. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific RT-PCR, but the test can take up to 2 d to complete. Chest CT is a valuable component of evaluation and diagnosis in symptomatic patients with

MYB transcription factors (TFs) play pivotal roles in the abiotic stress response in plants, but their characteristics and functions in wheat (Triticum aestivum L.) have not been fully investigated. A novel wheat MYB TF gene, TaMYB73, is reported here based on the observation that its targeting probe showed the highest salinity-inducibility level among all probes annotated as MYB TFs in the cDNA microarray. TaMYB73 is a R2R3 type MYB protein with transactivation activity, and binds with types I, II, and IIG MYB binding motifs. The gene was induced by NaCl, dehydration, and several phytohormones, as well as some stress-, ABA-, and GA-responsive cis-elements present in its promoter region. Its over-expression in Arabidopsis enhanced the tolerance to NaCl as well as to LiCl and KCl, whereas it had no contribution to mannitol tolerance. The over-expression lines had superior germination ability under NaCl and ABA treatments. The expression of many stress signalling genes such as AtCBF3 and AtABF3, as well as downstream responsive genes such as AtRD29A and AtRD29B, was improved in these over-expression lines, and TaMYB73 can bind with promoter sequences of AtCBF3 and AtABF3. Taken together, it is suggested that TaMYB73, a novel MYB transcription factor gene, participates in salinity tolerance based on improved ionic resistance partly via the regulation of stress-responsive genes.

PURPOSE: To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS: Fifty-nine patients (31 males and 28 females ranging from 25 to 70 years old) with confirmed COVID-19 infection performed follow-up thin-section thorax CT. After 31.5±7.9 days (range, 24 to 39 days) of hospital admission, the results of CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups namely, groups A and B (with and without CT evidence of fibrosis), respectively. Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS: Among the 59 patients, 89.8% (53/59) had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Also, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4±16.9 vs. 33.8±10.2 years) ( P = 0.001), with longer LOS (19.1±5.2 vs. 15.0±2.5 days) ( P = 0.001), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)) ( P = 0.061), higher peak C-reactive protein level (30.7±26.4 vs. 18.1±17.9 mg/L) ( P = 0.041), and higher maximal CT score (5.2±4.3 vs. 4.0±2.2) ( P = 0.06) than those in group B. CONCLUSIONS: Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.

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It is known that with a proper fuzzy membership function, a fuzzy support vector machine can effectively reduce the effects of outliers when solving the classification problem. In this paper, a new fuzzy membership function is proposed to the nonlinear fuzzy support vector machine. The fuzzy membership is calculated in the feature space and is represented by kernels. This method gives good performance on reducing the effects of outliers and significantly improves the classification accuracy and generalization.

As a widely used semiconductor material, silicon has been extensively used in many areas, such as photodiode, photodetector, and photovoltaic devices. However, the high surface reflectance and large bandgap of traditional bulk silicon restrict the full use of the spectrum. To solve this problem, many methods have been developed. Among them, the surface nanostructured silicon, namely black silicon, is the most efficient and widely used. Due to its high absorption in the wide range from UV-visible to infrared, black silicon is very attractive for using as sensitive layer of photodiodes, photodetector, solar cells, field emission, luminescence, and other photoelectric devices. Intensive study has been performed to understand the enhanced absorption of black silicon as well as the response extended to infrared spectrum range. In this paper, the application of black silicon is systematically reviewed. The limitations and challenges of black silicon material are also discussed. This article will provide a meaningful introduction to black silicon and its unique properties.

PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

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