Most of the Shewanella species contain two periplasmic nitrate reductases (NAP-a and NAP-b), which is a unique feature of this genus. In the present study, the physiological function and evolutionary relationship of the two NAP systems were studied in the deep-sea bacterium Shewanella piezotolerans WP3. Both of the WP3 nap gene clusters: nap-a (napD1A1B1C) and nap-b (napD2A2B2) were shown to be involved in nitrate respiration. Phylogenetic analyses suggest that NAP-b originated earlier than NAP-a. Tetraheme cytochromes NapC and CymA were found to be the major electron deliver proteins, and CymA also served as a sole electron transporter towards nitrite reductase. Interestingly, a DnapA2 mutant with the single functional NAP-a system showed better growth than the wild-type strain, when grown in nitrate medium, and it had a selective advantage to the wild-type strain. On the basis of these results, we proposed the evolution direction of nitrate respiration system in Shewanella: from a single NAP-b to NAP-b and NAP-a both, followed by the evolution to a single NAP-a. Moreover, the data presented here will be very useful for the designed engineering of Shewanella for more efficient respiring capabilities for environmental bioremediation.

To find out which of the following parameters-serum levels of insulin-like growth factor 1 (IGF-1), osteoprotegerin (OPG), leptin, osteocalcin (OC), and urinary excretion of N-terminal telopeptide of type I collagen (NTx), can be used as an early marker for osteopenia/osteoporosis in women diagnosed by dual-energy X-ray absorptiometry (DXA), 282 premenopausal and 222 postmenopausal women aged 20-75 years were investigated by the measurement of bone mineral densities (BMDs) at lumbar spine (LS) and femoral neck (FN) by DXA, together with serum concentrations of IGF-1, OPG, leptin, OC, and urinary NTx. The characteristics of the earliest marker(s) were tested with the receiver operating characteristic (ROC) analysis. The area under the curve (AUC), sensitivity, and specificity parameters were determined. It was revealed that serum levels of IGF-1 and leptin changed the earliest, with both markers significantly decreasing (P < 0.0001) or increasing (P = 0.020), respectively, at age 30. However, in ROC analysis, IGF-1 was the only early parameter that had the capacity to differentiate the low bone mass/osteoporosis women from the normal ones (P < 0.0001). If the serum level of IGF-1 at 1.5 SD below its peak was adopted as a cutoff point, it could identify women with low bone mass/osteoporosis with a sensitivity of 73% and specificity of 67%. In the premenopausal women subgroup analysis, the low bone mass women (30/282, 10.6%) were older (38.2 +/- 1.7 vs. 34.5 +/- 0.5 years; P = 0.026), with lower serum levels of IGF-1 (215.1 +/- 22.4 vs. 278.8 +/- 9.4 ng/ml; P = 0.02) and less lean mass (33.1 +/- 0.6 vs. 34.8 +/- 0.2 kg; P = 0.010) than the normal ones. After controlling for age, the serum level of IGF-1 had a weak, but still significant, positive correlation with lean mass (r = 0.17, P < 0.001). In conclusion, measurement of serum IGF-1 in young women may help in the early identification of those at risk for developing low bone mass and osteoporosis.

No abstract

Objective: This study focused on the ADHD classification through functional connectivity (FC) analysis. Method: An ADHD classification method was proposed with subspace clustering and binary hypothesis testing, wherein partial information of test data was adopted for training. By hypothesizing the binary label (ADHD or control) for the test data, two feature sets of training FC data were generated during the feature selection procedure that employed both training and test data. Then, a multi-affinity subspace clustering approach was performed to obtain the corresponding subspace-projected feature sets. With the energy comparison of projected feature sets, we finally identified ADHD individuals for the test data. Results: Our method outperformed several state-of-the-art methods with the above 90% average identification accuracy. By the discriminative FC contribution analysis, it also proved the reliability of our method. Conclusion: Results demonstrate the remarkable classification performance of our method and reveal some useful brain circuits to identify ADHD.

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g., the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitraryshaped text spotting and can be trained end-to-end with only coarse position information (e.g., rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-ofthe-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.

Background: As an advanced surgical technique to reduce trauma to the inner ear, robot-assisted electrode array (EA) insertion has been applied in adult cochlear implantation (CI) and was approved as a safe surgical procedure that could result in better outcomes. As the mastoid and temporal bones are generally smaller in children, which would increase the difficulty for robot-assisted manipulation, the clinical application of these systems for CI in children has not been reported. Given that the pediatric candidate is the main population, we aim to investigate the safety and reliability of robot-assisted techniques in pediatric cochlear implantation.Methods: Retrospective cohort study at a referral center in Shanghai including all patients of simultaneous bilateral CI with robotic assistance on one side (RobOtol® system, Collin ORL, Bagneux, France), and manual insertion on the other (same brand of EA and CI in both side), from December 2019 to June 2020. The surgical outcomes, radiological measurements (EA positioning, EA insertion depth, mastoidectomy size), and audiological outcomes (Behavior pure-tone audiometry) were evaluated.Results: Five infants (17.8 ± 13.5 months, ranging from 10 to 42 months) and an adult (39 years old) were enrolled in this study. Both perimodiolar and lateral wall EAs were included. The robot-assisted EA insertion was successfully performed in all cases, although the surgical zone in infants was about half the size in adults, and no difference was observed in mastoidectomy size between robot-assisted and manual insertion sides (p = 0.219). The insertion depths of EA with two techniques were similar (P = 0.583). The robot-assisted technique showed no scalar deviation, but scalar deviation occurred for one manually inserted pre-curved EA (16%). Early auditory performance was similar to both techniques.Conclusion: Robot-assisted technique for EA insertion is approved to be used safely and reliably in children, which is possible and potential for better scalar positioning and might improve long-term auditory outcome. Standard mastoidectomy size was enough for robot-assisted technique. This first study marks the arrival of the era of robotic CI for all ages.

In this paper, we present

Iris segmentation algorithms are of great significance in complete iris recognition systems, and directly affect the iris verification and recognition results. However, the conventional iris segmentation algorithms have poor adaptability and are not sufficiently robust when applied to noisy iris databases captured under unconstrained conditions. In addition, there are currently no large iris databases; thus, the iris segmentation algorithms cannot maximize the benefits of convolutional neural networks (CNNs). The main work of this paper is as follows: first, we propose an architecture based on CNNs combined with dense blocks for iris segmentation, referred to as a dense-fully convolutional network (DFCN), and adopt some popular optimizer methods, such as batch normalization (BN) and dropout. Second, because the public ground-truth masks of the CASIA-Interval-v4 and IITD iris databases do not include the labeled eyelash regions, we label these regions that occlude the iris regions using the Labelme software package. Finally, the promising results of experiments based on the CASIA-Interval-v4, IITD, and UBIRIS.V2 iris databases captured under different conditions reveal that the iris segmentation network proposed in this paper outperforms all of the conventional and most of the CNN-based iris segmentation algorithms with which we compared our algorithm's results in terms of various metrics, including the accuracy, precision, recall, f1 score, and nice1 and nice2 error scores, reflecting the robustness of our proposed network. INDEX TERMS CNNs, dense block, dense-fully convolutional network, iris segmentation.