A high resolution atlas of gene expression in the domestic sheep (Ovis aries)

Sheep are a key source of meat, milk and fibre for the global livestock sector, and an important biomedical model. Global analysis of gene expression across multiple tissues has aided genome annotation and supported functional annotation of mammalian genes. We present a large-scale RNA-Seq dataset representing all the major organ systems from adult sheep and from several juvenile, neonatal and prenatal developmental time points. The Ovis aries reference genome (Oar v3.1) includes 27,504 genes (20,921 protein coding), of which 25,350 (19,921 protein coding) had detectable expression in at least one tissue in the sheep gene expression atlas dataset. Network-based cluster analysis of this dataset grouped genes according to their expression pattern. The principle of 'guilt by association' was used to infer the function of uncharacterised genes from their co-expression with genes of known function. We describe the overall transcriptional signatures present in the sheep gene expression atlas and assign those signatures, where possible, to specific cell populations or pathways. The findings are related to innate immunity by focusing on clusters with an immune signature, and to the advantages of cross-breeding by examining the patterns of genes exhibiting the greatest expression differences between purebred and crossbred animals. This high-resolution gene expression atlas for sheep is, to our knowledge, the largest transcriptomic dataset from any livestock species to date. It provides a resource to improve the annotation of the current reference genome for sheep, presenting a model transcriptome for ruminants and insight into gene, cell and tissue function at multiple developmental stages

Clinical challenges in the management of vaginal prolapse

Nazema Y Siddiqui, Autumn L EdenfieldDivision of Urogynecology and Reconstructive Surgery, Duke University Medical Center, Durham, NC, USAAbstract: Pelvic organ prolapse is highly prevalent, and negatively affects a woman’s quality of life. Women with bothersome prolapse may be offered pessary management or may choose to undergo corrective surgery. In choosing the most appropriate surgical procedure, there are many factors to consider. These may include the location(s) of anatomic defects, the severity of prolapse symptoms, the activity level of the woman, and concerns regarding the durability of the repair. In many instances, women and their surgeons are challenged to weigh the risks and benefits of native tissue versus mesh-augmented repairs. Though mesh-augmented repairs may offer better durability, they are also associated with unique complications, such as mesh erosion. Furthermore, newer surgical techniques of mesh placement via abdominal or vaginal routes may result in different outcomes compared to traditional techniques. Biologic grafts may also be considered to improve durability of a surgical repair, while avoiding potential complications of synthetic mesh. In this article, we review many of the clinical challenges that gynecologic surgeons face in the surgical management of vaginal prolapse. Furthermore, we review data that can help guide decision making when treating women with pelvic organ prolapse.Keywords: pelvic organ prolapse, vaginal prolapse, surgery, sacrocolpopexy, sacrospinous ligament fixation, transvaginal mesh, uterosacral ligament suspensio

Weed Detection Using Deep Learning: A Systematic Literature Review

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area