SabR enhances nikkomycin production via regulating the transcriptional level of sanG, a pathway-specific regulatory gene in Streptomyces ansochromogenes

<p>Abstract</p> <p>Background</p> <p><it>sabR </it>is a pleiotropic regulatory gene which has been shown to positively regulate the nikkomycin biosynthesis and negatively affect the sporulation of <it>Streptomyces ansochromogenes</it>. In this study, we investigate the mechanism of SabR on modulating nikkomycin production in <it>Streptomyces ansochromogenes</it>.</p> <p>Results</p> <p>The transcription start point of <it>sabR </it>was determined by high-resolution S1 nuclease mapping and localized at the nucleotide T at position 37 bp upstream of the potential <it>sabR </it>translation start codon (GTG). Disruption of <it>sabR </it>enhanced its own transcription, but retarded the nikkomycin production. Over-expression of <it>sabR </it>enhanced nikkomycin biosynthesis in <it>Streptomyces ansochromogenes</it>. EMSA analysis showed that SabR bound to the upstream region of <it>sanG</it>, but it did not bind to the upstream region of its encoding gene (<it>sabR</it>), <it>sanF </it>and the intergenic region between <it>sanN </it>and <it>sanO</it>. DNase 1 footprinting assays showed that the SabR-binding site upstream of <it>sanG </it>was 5'-CTTTAAGTCACCTGGCTCATTCGCGTTCGCCCAGCT-3' which was designated as SARE. Deletion of SARE resulted in the delay of nikkomycin production that was similar to that of <it>sabR </it>disruption mutant.</p> <p>Conclusions</p> <p>These results indicated that SabR modulated nikkomycin biosynthesis as an enhancer via interaction with the promoter region of <it>sanG</it>, and expanded our understanding about regulatory cascade in nikkomycin biosynthesis.</p

Oscillation theorems for certain third order nonlinear delay dynamic equations on time scales

In this paper, we establish some new oscillation criteria for the third order nonlinear delay dynamic equations $$\left(b(t)\left([a(t)(x^\Delta(t))^{\alpha_1}]^\Delta\right)^{\alpha_2}\right)^\Delta+q(t)x^{\alpha_3}(\tau(t))=0$$ on a time scale $\mathbb{T}$, where $\alpha_i$ are ratios of positive odd integers, $i=1,\ 2,\ 3,$ $b,\ a$ and $q$ are positive real-valued rd-continuous functions defined on $\mathbb{T}$, and the so-called delay function $\tau:\mathbb{T}\rightarrow \mathbb{T}$ is a strictly increasing function such that $\tau(t)\leq t$ for $t\in \mathbb{T}$ and $\tau(t)\rightarrow\infty$ as $t\rightarrow\infty.$ By using the Riccati transformation technique and integral averaging technique, some new sufficient conditions which insure that every solution oscillates or tends to zero are established. Our results are new for third order nonlinear delay dynamic equations and complement the results established by Yu and Wang in J. Comput. Appl. Math., 2009, and Erbe, Peterson and Saker in J. Comput. Appl. Math., 2005. Some examples are given here to illustrate our main results

Examining the Relationship between Corporate Governance Characteristics and Firm Financial Performance: Evidence from UK FTSE 250

This dissertation examines the relationship between corporate governance characteristics and firm financial performance. The data was collected from the UK FTSE 250, including 17 significant sectors and crossing nine fiscal years from 2011 to 2019. And the number of sample firms is 122, so spanning nine fiscal years, there are 122*9=1098 observations. The board of directors' characteristics is employed to represent corporate governance, including the board size, the directors' remuneration, gender diversity and the proportion of non-executive directors. At the same time, the firm's profitability ratios serve as the financial indicators, such as Return on Assets (ROA) and Return on Capital Employed (ROCE). The main statistical technique is the fixed-effects regression model, and the ordinary least squares (OLS) regression model is used for robustness tests. The findings show that the relationship between the directors' remuneration and firm financial performance is significantly positive. Furthermore, the proportion of non-executive directors also has a positive influence on the financial performance of a firm. However, the association between board size and corporate performance is significantly negative. And the impact of the presence of female directors on the board on the firm financial performance is negative

Content-Based Hyperspectral Image Compression Using a Multi-Depth Weighted Map With Dynamic Receptive Field Convolution

In content-based image compression, the importance map guides the bit allocation based on its ability to represent the importance of image contents. In this paper, we improve the representational power of importance map using Squeeze-and-Excitation (SE) block, and propose multi-depth structure to reconstruct non-important channel information at low bit rates. Furthermore, Dynamic Receptive Field convolution (DRFc) is introduced to improve the ability of normal convolution to extract edge information, so as to increase the weight of edge content in the importance map and improve the reconstruction quality of edge regions. Results indicate that our proposed method can extract an importance map with clear edges and fewer artifacts so as to provide obvious advantages for bit rate allocation in content-based image compression. Compared with typical compression methods, our proposed method can greatly improve the performance of Peak Signal-to-Noise Ratio (PSNR), structural similarity (SSIM) and spectral angle (SAM) on three public datasets, and can produce a much better visual result with sharp edges and fewer artifacts. As a result, our proposed method reduces the SAM by 42.8% compared to the recently SOTA method to achieve the same low bpp (0.25) on the KAIST dataset

Providing flow based performance guarantees for buffered crossbar switches

Buffered crossbar switches are a special type of com-bined input-output queued switches with each crosspoint of the crossbar having small on-chip buffers. The introduc-tion of crosspoint buffers greatly simplifies the scheduling process of buffered crossbar switches, and furthermore en-ables buffered crossbar switches with speedup of two to eas-ily provide port based performance guarantees. However, recent research results have indicated that, in order to pro-vide flow based performance guarantees, buffered crossbar switches have to either increase the speedup of the cross-bar to three or greatly increase the total number of cross-point buffers, both adding significant hardware complexity. In this paper, we present scheduling algorithms for buffered crossbar switches to achieve flow based performance guar-antees with speedup of two and with only one or two buffers at each crosspoint. When there is no crosspoint blocking in a specific time slot, only the simple and distributed in-put scheduling and output scheduling are necessary. Other-wise, the special urgent matching is introduced to guarantee the on-time delivery of crosspoint blocked cells. With the proposed algorithms, buffered crossbar switches can pro-vide flow based performance guarantees by emulating push-in-first-out output queued switches, and we use the counting method to formally prove the perfect emulation. For the special urgent matching, we present sequential and paral-lel matching algorithms. Both algorithms converge with N iterations in the worst case, and the latter needs less itera-tions in the average case. Finally, we discuss an alternative backup-buffer implementation scheme to the bypass path, and compare our algorithms with existing algorithms in the literature

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an important role in the diagnosis and evaluation of human diseases, yet it is a challenging uncertainty in the complex segmentation tasks due to different issues such as various image appearances, low contrast between curvilinear objects and their surrounding backgrounds, thin and uneven curvilinear structures, and improper background illumination conditions. To overcome these challenges, we present a unique curvilinear structure segmentation framework based on an oriented derivative of stick (ODoS) filter and a deep learning network for curvilinear object segmentation in medical images. Currently, a large number of deep learning models emphasize developing deep architectures and ignore capturing the structural features of curvilinear objects, which may lead to unsatisfactory results. Consequently, a new approach that incorporates an ODoS filter as part of a deep learning network is presented to improve the spatial attention of curvilinear objects. Specifically, the input image is transfered into four-channel image constructed by the ODoS filter. In which, the original image is considered the principal part to describe various image appearance and complex background illumination conditions, a multi-step strategy is used to enhance the contrast between curvilinear objects and their surrounding backgrounds, and a vector field is applied to discriminate thin and uneven curvilinear structures. Subsequently, a deep learning framework is employed to extract various structural features for curvilinear object segmentation in medical images. The performance of the computational model is validated in experiments conducted on the publicly available DRIVE, STARE and CHASEDB1 datasets. The experimental results indicate that the presented model yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure

Interfacial Properties of Monolayer and Bilayer MoS2 Contacts with Metals: Beyond the Energy Band Calculations

Although many prototype devices based on two-dimensional (2D) MoS2 have been fabricated and wafer scale growth of 2D MoS2 has been realized, the fundamental nature of 2D MoS2-metal contacts has not been well understood yet. We provide a comprehensive ab initio study of the interfacial properties of a series of monolayer (ML) and bilayer (BL) MoS2-metal contacts (metal = Sc, Ti, Ag, Pt, Ni, and Au). A comparison between the calculated and observed Schottky barrier heights (SBHs) suggests that many-electron effects are strongly suppressed in channel 2D MoS2 due to a charge transfer. The extensively adopted energy band calculation scheme fails to reproduce the observed SBHs in 2D MoS2-Sc interface. By contrast, an ab initio quantum transport device simulation better reproduces the observed SBH in the two types of contacts and highlights the importance of a higher level theoretical approach beyond the energy band calculation in the interface study. BL MoS2-metal contacts have a reduced SBH than ML MoS2-metal contacts due to the interlayer coupling and thus have a higher electron injection efficiency.Comment: 36 pages, 13 figures, 3 table

MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images

The Coronavirus Disease 2019 (COVID-19) pandemic has increased the public health burden and brought profound disaster to humans. For the particularity of the COVID-19 medical images with blurred boundaries, low contrast and different sizes of infection sites, some researchers have improved the segmentation accuracy by adding model complexity. However, this approach has severe limitations. Increasing the computational complexity and the number of parameters is unfavorable for model transfer from laboratory to clinic. Meanwhile, the current COVID-19 infections segmentation DCNN-based methods only apply to a single modality. To solve the above issues, this paper proposes a symmetric Encoder-Decoder segmentation framework named MS-DCANet. We introduce Tokenized MLP block, a novel attention scheme that uses a shift-window mechanism similar to the Transformer to acquire self-attention and achieve local-to-global semantic dependency. MS-DCANet also uses several Dual Channel blocks and a Res-ASPP block to expand the receptive field and extract multi-scale features. On multi-modality COVID-19 tasks, MS-DCANet achieved state-of-the-art performance compared with other U-shape models. It can well trade off the accuracy and complexity. To prove the strong generalization ability of our proposed model, we apply it to other tasks (ISIC 2018 and BAA) and achieve satisfactory results

Machine Fault Classification Based on Local Discriminant Bases and Locality Preserving Projections

Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities