Abstract:Oil-polluted bilge water is difficult to treat, especially in large scale, due to its high salinity, complicated composition and toxicity. To improve the efficacy of ballast bilge water treatment, an artificial bacterial consortium including nine petroleum degrading strains previously screened from petroleumpolluted ocean waters was applied as bioaugmentation agent. A trial run was firstly completed in a 500 L tank, following which a second run was carried out in a specially designed 600 L bioreactor. The total petroleum hydrocarbons were analyzed to evaluate the efficacy of treatment. Moreover, potential changes in the structure of bacterial consortia during treatment were monitored with denaturing gradient gel electrophoresis and Illumina HiSeq 2000 sequencing. In the first trial run, the oil-removal efficiency was approximately 70%, while in the second run performed in specially designed bioreactor, the efficiency of oil degradation was increased to over 90%. In both runs, the artificial bacterial consortium was reconstituted during the acclimation process immediately before the treatment, and then stabilized and predominated by three strains, Marinobacter hydrocarbonoclasticus, Acinetobacter venetianus, and Alcanivorax dieselolei, throughout the following several months of treatment. Overall, this work suggests that bioaugmentation with the artificial bacterial consortium composed of hydrocarbon-degrading strains could provide a promising solution to the treatment of oil-polluted ballast bilge water in large scale.

Abstract:Otoacoustic emissions (OAE) is a type of elastic wave energy produced in the cochlea, can be recorded and used to detect the health of the outer hair cells of the cochlea. At present, the transient evoked otoacoustic emissions (TEOAE) and the distortion product otoacoustic emissions (DPOAE) have their own advantages and disadvantages. The subject of this study is a much widely used stimulus frequency otoacoustic emissions (SFOAE) than other OAE. This paper presents a swept-based SFOAE measurement method. The so-called swept-tone is a kind of stimulus whose frequency changes linearly with time. The SFOAE frequency measured has a much wider range, and the resolution depends on the sweep frequency. The threeinterval paradigm was used to generate SFOAE, and the tracking filter can extract the swept SFOAE from the background noise. In this paper, the reliability, compatibility and effectiveness of the swept-tone based SFOAE are verified by comparing the results measured by the same subject at different times, comparing with the traditional methods and comparing the results measured at different scanning times. Experimental results show that the proposed SFOAE can be used to improve the detection methods clinically available for hearing loss.

Abstract:Tumour cells undergoing immunogenic cell death (ICD) can elicit a specific anti-tumour immune response by releasing some specific signalling molecules, which determines the long-term success of cancer therapeutic strategies. In this study, human serum albumin (HSA) was hybridized with hemoglobin (Hb) by intermolecular disulfide bonds to develop a Ce6-loaded hybrid protein oxygen carrier (C@Hb/HSA). Then, the efficacy of C@Hb/HSA-mediated photodynamic therapy (PDT) in CT26.WT cells was assessed. Whether C@Hb/HSA-mediated PDT can induce the ICD was also studied. The results show that under laser irradiation, low-dose C@Hb/HSA exhibits enhanced reactive oxygen species generation in CT26.WT cells, leading to a low cell viability of (17.8±5.5)%. Meanwhile, C@Hb/HSA-mediated PDT dramatically increases the surface-exposure of calreticulin, thereby effectively enhancing the immunogenicity of CT26.WT cells and significantly promoting the maturation of dendritic cells.

Abstract:Microglia is one of the important non-neoplastic elements of cerebral glioma. The interaction of glioma and microglia promotes tumor progression. However, the spatiotemporal heterogeneity of the microglia in the context of glioma remains uncertain. In this study, cerebral glioma model was built with C6 cell implantation in 28 SD rats to investigate the distribution of microglia during the tumor progression. T2 weighted magnetic resonance imaging (T2WI) was conducted at the post-operative day 7, 9, 12, 14, 16, 18, 22, 23 and 24 after the model built-up, after which the Hematoxylin-Eosin (HE) and immunofluorescent staining of the brain tissue were prepared. Nine regions of interest (ROI) were defined within the tumor, as the peritumoral and the contralesional areas on the HE sections with the largest tumor expansion. Scale-invariant feature transform (SIFT) algorithm was used to register and fuse the HE-immunofluorescent image pairs. ROIs defined on the HE sections were then translated to the immunofluorescence images. The averaged signal intensity was measured on the T2WI image with the largest tumor diameter. Mean density (MD) of the microglia in the ROIs were measured for each ROI and plotted with the time after C6 cell implantation. It can be observed that MD in the tumor ROI was significantly larger than that of the rest ROIs (P＜0.001). MD increased with time and diameter that best fit to binomial and linear functions, respectively, for all the ROIs with a more precipitous inclination in the tumor MD. The average signal intensity of the ROIs on T2WI were also found positively correlated with the tumor MD. These findings indicate that tumor formation of C6 glioma triggers extensive microglia activation in the tumor and the non-neoplastic brain tissue, necessitating the assessment of microglia in both the local and global scales in performing the aggressiveness characterization and treatment trials targeting microenvironment of cerebral glioma.

Abstract:Ultrasound is a mechanical vibration wave with frequencies above 20 kHz, which has mechanical effect, heating effect and cavitations effect. The ultrasound with different intensity has been applied for organisms tissues and cells with different biological effects. With the development of engineering technology, ultrasound has been widely applied to medical imaging, solid tumor treatment and other directions in biomedicine. Low intensity ultrasound has gradually been considered as an important engineering tool to assist the stem cell therapy of various diseases. The detailed understanding of ultrasound for stem cell therapy will promote further application of ultrasonic technology and the development of clinic applications. The progress of ultrasound for stem cell therapy is introduced in this review, including promoting stem cell proliferation, differentiation and migration, tracking stem cell by ultrasound image and target drug delivery. Finally, ideas about improving ultrasound for stem cell therapy in future are discussed.

Abstract:Modern processors typically have only 4-8 performance counters which can be programmed to measure up to thousands of cycle-level performance events. These events can easily generate large amount of data, which is called central processing unit (CPU) big performance data. However, how to extract value from the big performance data faces many challenges. This paper presents a performance data analysis approach, which builds a performance model by iteratively using the gradient boosting regression tree algorithm and quantifies the importance of the performance events of workloads in cloud to guide their performance optimization.

Abstract:In this paper, a deep learning-based image semantic segmentation method was studied. A neural network trained by point pair annotations of relative depth was used to predict depth images from common color images. By feeding the color and depth images into a fully convolutional networks with atrous convolution, accurate segmentation of the images could be obtained. As different representations of object properties, concatenate operation on the feature maps instead of traditional adding operation was used to fuse them. The differences between these two representations could be preserved when they were feed into the next convolutional layers. Experimental results on two different datasets show that, performance of semantic segmentation can be improved by the proposed method.

Abstract:Line segment is the essential element of geometry objects, which contains very rich geometric information. Extracting complete and continuous line segments with semantic information from an image is of great significance for restoring the geometry structure of a scene, yet challenging. This paper proposes a multiresolution segment extraction approach, which performs semantic analysis on the line segments to distinguish the contour and the texture line segments. This approach first extracts line segments with multi-resolution thought, then combines the deep neural network technology to perform semantic analysis on line segments, and finally clusters the line segments to get the final result. In terms of line segment continuity and integrity, the proposed approach has obvious advantages compared with the commonly used line segment extraction methods. In terms of semantic analysis accuracy, the pixel accuracy of the proposed approach on the test set is achieves 97.82%.