Lindgren Dillon (drakeattack75)

agricultural disaster prevention, mitigation and agricultural irrigation in Sichuan Province.Post operative ventral hernias are common following Hartmann's procedure. There is a debate whether hernia repair is safe when performed concomitantly with colostomy closure. In this study we aimed to evaluate the outcomes of synchronous Hartmann reversal (HR) with a hernia repair, compared to a staged procedure. A retrospective multi-center study was conducted, including all patients who underwent Hartmann's procedure from January 2004 to July 2017 in 5 medical centers. Patient data included demographics, surgical data and post-operative outcome. Two hundred and seventy-four patients underwent colostomy reversal following Hartmann's procedure. In 107 patients (39%) a concomitant ventral hernia was reported during the Hartmann's reversal. Out of this cohort, 62 patients (58%) underwent hernia repair during follow-up. Thirty two patients (52%) underwent a synchronous hernia repair and 30 patients (48%) underwent hernia repair as a separate procedure. Post operative complication rate was significantly higher in the colostomy reversal with synchronous hernia repair group when compared to HR alone group (53% vs. 20%; p less then 0.01; OR 4.5). In addition, severe complication rate (Clavien-Dindo score ≥ 3) was higher in the synchronous hernia repair group (25% vs. 7%). A tendency for higher hernia recurrence rate was noted in the synchronous group (56% vs. 40%). Median follow up time was 2.53 years (range 1-13.3 years). Synchronous colostomy closure and ventral hernia repair following Hartmann's procedure carries a significant risk for post operative complications, indicating that a staged procedure might be preferable.Most speech separation studies in monaural channel use only a single type of network, and the separation effect is typically not satisfactory, posing difficulties for high quality speech separation. In this study, we propose a convolutional recurrent neural network with an attention (CRNN-A) framework for speech separation, fusing advantages of two networks together. The proposed separation framework uses a convolutional neural network (CNN) as the front-end of a recurrent neural network (RNN), alleviating the problem that a sole RNN cannot effectively learn the necessary features. This framework makes use of the translation invariance provided by CNN to extract information without modifying the original signals. Within the supplemented CNN, two different convolution kernels are designed to capture information in both the time and frequency domains of the input spectrogram. After concatenating the time-domain and the frequency-domain feature maps, the feature information of speech is exploited through consecutive convolutional layers. Finally, the feature map learned from the front-end CNN is combined with the original spectrogram and is sent to the back-end RNN. Further, the attention mechanism is further incorporated, focusing on the relationship among different feature maps. The effectiveness of the proposed method is evaluated on the standard dataset MIR-1K and the results prove that the proposed method outperforms the baseline RNN and other popular speech separation methods, in terms of GNSDR (gloabl normalised source-to-distortion ratio), GSIR (global source-to-interferences ratio), and GSAR (gloabl source-to-artifacts ratio). In summary, the proposed CRNN-A framework can effectively combine the advantages of CNN and RNN, and further optimise the separation performance via the attention mechanism. The proposed framework can shed a new light on speech separation, speech enhancement, and other related fields.Toll-like receptor 9 (TLR9) is crucial to the host immune response against fungi, such as Candida albicans, Aspergillus fumigatus and Cryptococcus neoformans, but its importance in Cryptococcus gattii infection is unknown. Our study aimed to understand the role of TLR9 during the course of experimental C. gattii