Introgression of bacterial leaf blight (BLB) resistant gene, Xa7 into MARDI elite variety, MR219 by marker assisted backcrossing (MABC) approach

Bacterial leaf blight (BLB) is one of the major rice diseases in Malaysia. This disease causes substantial yield loss as high as 70%. Development of rice varieties which inherited BLB resistant traits is a crucial approach to promote and sustain rice industry in Malaysia. Hence, this study aims were to enhance BLB disease resistant characters of high yielding commercial variety MR219 through backcross breeding approach with supporting tool of marker-assisted selection (MAS). Broad spectrum BLB resistance gene, Xa7 from donor parent IRBB7 were introgressed into the susceptible MR219 (recurrent parent) using two flanking markers ID7 and ID15. At BC3F4, we managed to generate 19 introgressed lines with homozygous Xa7 gene and showed resistant characteristics as donor parent when it was challenged with Xanthomonas oryzae pv. oryzae through artificial inoculation. Recurrent parent MR219 and control variety, MR263 were found to be severely infected by the disease. The improved lines exhibited similar morphological and yield performance characters as to the elite variety, MR219. Two lines, PB-2-107 and PB-2-34 were chosen to be potential lines because of their outstanding performances compared to parent, MR219. This study demonstrates a success story of MAS application in development of improved disease resistance lines of rice against BLB disease.

“Liner shipping”

The aim of the study was to analyze the scientific views on the definition of liner shipping at various stages of its development. For this, literary sources for more than a hundred years of research on this issue were analyzed. The analysis emphasizes the lack of systemic studies on this issue and the scientific approach to the definition of the concept in question. As a result of the study, the stages of development of liner shipping are presented and the main characteristic features for the concept are given, its boundaries are established. The resulting research also synthesizes the scientific papers in the field of liner shipping, which represents an important supporting tool for future theoretical and applied studies. Taking into account the exceptional role of liner shipping in intermodal delivery systems, the presented analysis is useful for all involved transport modes.

Brain intrinsic connection patterns underlying tool processing in human adults are present in neonates and not in macaques

Tool understanding and use are supported by a dedicated left-lateralized, intrinsically connected network in the human adult brain. To examine this network's phylogenic and ontogenetic origins, we compared resting-state functional connectivity (rsFC) among regions subserving tool processing in human adults to rsFC among homologous regions in human neonates and macaque monkeys (adolescent and mature). These homologous regions formed an intrinsic network in human neonates, but not in macaques. Network topological patterns were highly similar between human adults and neonates, and significantly less so between humans and macaques. The premotor-parietal rsFC had most significant contribution to the formation of the neonate tool network. These results suggest that an intrinsic brain network potentially supporting tool processing exists in the human brain prior to individual tool use experiences, and that the premotor-parietal functional connection in particular offers a brain basis for complex tool behaviors specific to humans.

Towards a systematic approach to manual annotation of code smells

<div>Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells.</div><div>A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction.</div><div>Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.</div>

Application of Deep Learning in Petrographic Coal Images Segmentation

The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work.

Additive Manufacturing for Neurosurgery: Digital Light Processing of Individualized Patient-Specific Cerebral Aneurysms

This study aims at demonstrating the feasibility of reproducing individualized patient-specific three-dimensional models of cerebral aneurysms by using the direct light processing (DLP) 3D printing technique in a low-time and inexpensive way. Such models were used to help neurosurgeons understand the anatomy of the aneurysms together with the surrounding vessels and their relationships, providing, therefore, a tangible supporting tool with which to train and plan surgical operations. The starting 3D models were obtained by processing the computed tomography angiographies and the digital subtraction angiographies of three patients. Then, a 3D DLP printer was used to print the models, and, if acceptable, on the basis of the neurosurgeon’s opinion, they were used for the planning of the neurosurgery operation and patient information. All the models were printed within three hours, providing a comprehensive representation of the cerebral aneurysms and the surrounding structures and improving the understanding of their anatomy and simplifying the planning of the surgical operation.

SMARTPHONE-BASED REALITY CAPTURE FOR SUBSURFACE UTILITIES: EXPERIENCES FROM WATER UTILITY COMPANIES IN DENMARK

Inaccurate and inconsistent documentation of subsurface utilities is a reoccurring problem in the construction industry affecting not only the end-users, but all actors involved in designing, constructing, and maintaining pipes, cables and other utilities hidden underground. In this study, a new method for 3D capturing of subsurface utilities, based on a newly developed Smartphone-based Reality Capture (RC) solution is explored. The research was divided into two parts. Firstly a testing of the method accuracy and secondly, an investigation of the usability of the method. The research results firstly showed that the RC solution is a feasible surveying method, that facilitate capturing of as-built utility assets, which can be used as a supporting tool to conventional surveying methods or alone, as the testing showed an accuracy of ±5 cm for the generated point clouds. Secondly the usability testing revealed that the RC solution benefited the utility owners by allowing time-savings on construction projects, as well as generating visual-realistic 3D models of exposed subsurface utilities to be used for quality assurance and planning of future utility work.