Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We propose a method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset with OCT images from 20 subjects diagnosed with PED. The experimental results showed the following. 1) The overall mean unsigned border positioning error for layer segmentation is 7.87±3.36 μm , and is comparable to the mean inter-observer variability ( 7.81±2.56 μm). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%, respectively. 3) The average running time is 220 s for OCT data of 512 × 64 × 480 voxels.

The symbiosis of the highly metal-resistant Sinorhizobium meliloti CCNWSX0020 and Medicago lupulina has been considered an efficient tool for bioremediation of heavy metal-polluted soils. However, the metal resistance mechanisms of S. meliloti CCNWSX00200 have not been elucidated in detail. Here we employed a comparative transcriptome approach to analyze the defense mechanisms of S. meliloti CCNWSX00200 against Cu or Zn exposure. Six highly upregulated transcripts involved in Cu and Zn resistance were identified through deletion mutagenesis, including genes encoding a multicopper oxidase (CueO), an outer membrane protein (Omp), sulfite oxidoreductases (YedYZ), and three hypothetical proteins (a CusA-like protein, a FixH-like protein, and an unknown protein), and the corresponding mutant strains showed various degrees of sensitivity to multiple metals. The Cu-sensitive mutant (ΔcueO) and three mutants that were both Cu and Zn sensitive (ΔyedYZ, ΔcusA-like, and ΔfixH-like) were selected for further study of the effects of these metal resistance determinants on bioremediation. The results showed that inoculation with the ΔcueO mutant severely inhibited infection establishment and nodulation of M. lupulina under Cu stress, while inoculation with the ΔyedYZ and ΔfixH-like mutants decreased just the early infection frequency and nodulation under Cu and Zn stresses. In contrast, inoculation with the ΔcusA-like mutant almost led to loss of the symbiotic capacity of M. lupulina to even grow in uncontaminated soil. Moreover, the antioxidant enzyme activity and metal accumulation in roots of M. lupulina inoculated with all mutants were lower than those with the wildtype strain. These results suggest that heavy metal resistance determinants may promote bioremediation by directly or indirectly influencing formation of the rhizobiumlegume symbiosis.IMPORTANCE Rhizobium-legume symbiosis has been promoted as an appropriate tool for bioremediation of heavy metal-contaminated soils. Considering the plantgrowth-promoting traits and survival advantage of metal-resistant rhizobia in contaminated environments, more heavy metal-resistant rhizobia and genetically manipulated strains were investigated. In view of the genetic diversity of metal resistance determinants in rhizobia, their effects on phytoremediation by the rhizobium-legume symbiosis must be different and depend on their specific assigned functions. Our work provides a better understanding of the mechanism of heavy metal resistance determinants involved in the rhizobium-legume symbiosis, and in further studies, genetically Citation Lu M, Jiao S, Gao E, Song X, Li Z, Hao X, Rensing C, Wei G. 2017. Transcriptome response to heavy metals in Sinorhizobium meliloti CCNWSX0020 reveals new metal resistance determinants that also promote bioremediation by Medicago lupulina in metalcontaminated soil. Appl Environ Microbiol

PurposeTo assess the correlation and agreement between the Topcon built-in algorithm and our graph-based algorithm in measuring the total and regional macular thickness for normal and glaucoma subjects.MethodsA total of 228 normal eyes and 93 glaucomatous eyes were enrolled in our study. All patients underwent comprehensive ophthalmic examination and Topcon 3D-OCT 2000 scan. One eye was randomly selected for each subject. The thickness of each layer and the total and regional macular thickness on an Early Treatment of Diabetic Retinopathy Study (ETDRS) chart were measured using the Topcon algorithm and our three-dimensional graph-based algorithm. Correlation and agreement analyses between these two algorithms were performed.ResultsOur graph search algorithm exhibited a strong correlation with Topcon algorithm. The macular GCC thickness values for normal and glaucoma subjects ranged from 0.86 to 0.91 and from 0.78 to 0.90, and the regional macular thickness values ranged from 0.79 to 0.96 and 0.70 to 0.95, respectively. Small differences were observed between the Topcon algorithm and our graph-based algorithm. The span of 95% limits of agreement of macular GCC thickness was less than 28 μm in both normal and glaucoma subjects, respectively. These limits of total and regional macular thickness were 15.5 μm and 23.1 μm for normal subjects and 29.1 μm and 46.4 μm for glaucoma subjects, respectively.ConclusionOur graph-based algorithm exhibited a high degree of agreement with the Topcon algorithm with respect to thickness measurements in normal and glaucoma subjects. Moreover, our graph-based algorithm can segment the retina into more layers than the Topcon algorithm does.

In this paper, a fully automatic method is proposed to segment the kidney into multiple components: renal cortex, renal column, renal medulla and renal pelvis, in clinical 3D CT abdominal images. The proposed fast automatic segmentation method of kidney consists of two main parts: localization of renal cortex and segmentation of kidney components. In the localization of renal cortex phase, a method which fully combines 3D Generalized Hough Transform (GHT) and 3D Active Appearance Models (AAM) is applied to localize the renal cortex. In the segmentation of kidney components phase, a modified Random Forests (RF) method is proposed to segment the kidney into four components based on the result from localization phase. During the implementation, a multithreading technology is applied to speed up the segmentation process. The proposed method was evaluated on a clinical abdomen CT data set, including 37 contrast-enhanced volume data using leave-one-out strategy. The overall true-positive volume fraction and false-positive volume fraction were 93.15%, 0.37% for renal cortex segmentation; 83.09%, 0.97% for renal column segmentation; 81.92%, 0.55% for renal medulla segmentation; and 80.28%, 0.30% for renal pelvis segmentation, respectively. The average computational time of segmenting kidney into four components took 20 seconds.