Shortened Basal Internodes Encodes a Gibberellin 2-Oxidase and Contributes to Lodging Resistance in Rice

Breeding semi-dwarf varieties to improve lodging resistance has been proven to be enormously successful in increasing grain yield since the advent of the ＂green revolution.＂ However, the breeding of the majority of semi-dwarf rice varieties in Asia has been dependent mainly on genetic introduction of the mutant alleles of SD1, which encodes a gibberellin （GA） 20-oxidase, OsGA20ox2, for catalyzing GA biosynthesis. Here, we report a new rice lodging-resistance gene, Shortened Basal lnternodes （SBI）, which encodes a gibberellin 2- oxidase and specifically controls the elongation of culm basal internodes through deactivating GA activity. SBI is predominantly expressed in culm basal internodes. Genetic analyses indicate that SBI is a semidominant gene affecting rice height and lodging resistance. SBI allelic variants display different activities and are associated with the height of rice varieties. Breeding with higher activity of the SBI allele generates new rice varieties with improved lodging resistance and increased yield. The discovery of the SB! provides a desirable gene resource for producing semi-dwarf rice phenotypes and offers an effective strategy for breeding rice varieties with enhanced lodging resistance and high yield.

Clinically applicable artificial intelligence algorithm for the diagnosis,evaluation,and monitoring of acute retinal necrosis

The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)algorithms in these areas of clinical research has not been reported previously.The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs.A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected,and the U-Net method was used to construct the AI algorithm.Thereby,a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time.This algorithm had an area under the receiver operating curve of 0.92,with 86%sensitivity and 88%specificity in the detection of retinal necrosis.For the purpose of retinal necrosis evaluation,necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444,P<0.0001)and therapeutic response of ARN(R2=0.999,P<0.0001).Therefore,our AI algorithm has a potential application in the clinical aided diagnosis of ARN,evaluation of ARN severity,and treatment response monitoring.

Sarcoid uveitis:A case report and systematic review of literature

1.Introduction Sarcoidosis is a disease that involves multiple systems and organs and is characterized by non-caseous necrotizing granulomas whose etiology is not fully understood.1 About 25%–80%of patients have eye and adnexal involvement.2 Uveitis,secondary glaucoma,cataracts,and macular edema are the most common complications of ocular sarcoidosis.3 Sarcoid uveitis refers to uveitis that occurs in patients with sarcoidosis and typically manifests as granulomatous anterior uveitis,posterior uveitis,or panuveitis.Symptoms of sarcoid uveitis include blurred vision,eye pain,photophobia,and tearing.In the anterior segment,iris nodules or mutton-fat keratic precipitates(KPs)are generally the most common clinical signs.In the vitreous cavity,the typical appearance is a snowball-like vitreous opacity.The fundus typically manifests as peripheral chorioretinal granulomas and peripheral phlebitis.Herein,we present a case of sarcoid uveitis with fungal endophthalmitis-like vitreitis.