An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits

Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.

FINTECH TOOLS TO REGULATE GRAY EXPORTS OF MILITARY AND DUAL USE GOODS AND TECHNOLOGIES

This article reviews challenges of monitoring and regulation of military and dual use goods and technologies in Ukraine. These challenges are not new; their different aspects have been analyzed previously by many Ukrainian researchers, such as G. Androshchuk, O. Fradynskyi, I. Anokhin, V. Davydovskyy and more, but all earlier analyses, while looking into theoretical and practical aspects of military and dual use goods and technologies export per se, left aside economic and financial aspects of this problem, which are in the focus of our investigation. The object of the study is the export of military and dual use goods and technologies. The subject of the study is the FinTech tools that can be applied to analysis of export of military and dual use goods and technologies. The aim of the research is to analyze the current situation in export of military and dual use goods and technologies and based on results of analysis to outline the FinTech tools that will be useful to evaluate and regulate gray exports of military and dual use goods and technologies. The methodology of research is based on economic analysis in which we have applied an alternative approach to assessing key indicators. Firstly, we determined government budget military expenditure and then compared it with the scope of relevant exports. Further, we analyzed the black market of military and dual use goods and technologies based on the data obtained from the Ministry of Internal Affairs. This analysis demonstrated that official numbers represent only 10% of the total expected amount of military and dual use goods and technologies export; the balance is shared between the domestic black market and gray exports. As result of the research we propose modern FinTech tools, including financial markers and the BlockChain technology, as instruments to detect such gray exports. Financial markers are specific FinTech indicators making banks aware that a transaction involves transfer of military or dual use goods or technologies and therefore requires special attention (to verify whether the company has an appropriate license or whether a license is needed for the transaction etc.) BlockChain is the best solution for tracking the financial marker information since it supports storage of information about the whole transaction chain and analysis of this information on any transaction stage. BlockChain technology can generate information on possible gray exports automatically and chain breaks (where the end user does not typically use or sell military or dual use goods or technologies but is a vendor of conventional goods or technologies).

Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model

Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included.

Aplikasi Augmented Reality Untuk Informasi Pemakaian Ruang Kelas Pada Kampus Fasilkom Unsri Berbasis Android

Campus is a place of learning with many buildings and rooms. Due to the number of rooms available, it is not uncommon for students and lecturers to enter the wrong room where learning and teaching are provided so that often interfere with the activities of the users of the room at that time. This application is used as a means of information to find out who and whenever study space on campus fasilkom unsri used. This application applies Augmented Reality technology that combines virtual objects into the real world so that the information can only be viewed through the intermediary of mobile phone camera or computer. With this application is expected to facilitate users in knowing information about the classroom that will be used so as not to interfere with the activities of other users of the room.Keywords – Augmented Reality, Marker, Information, AndroidAbstrak - Kampus merupakan tempat belajar yang terdiri dari banyak gedung dan ruangan. Karena banyaknya ruangan yang tersedia, maka tidak jarang para mahasiswa dan dosen salah dalam memasuki ruangan tempat belajar dan mengajar disediakan sehingga seringkali mengganggu kegiatan pengguna ruangan pada saat itu. Aplikasi ini digunakan sebagai sarana informasi untuk mengetahui siapa dan kapan saja ruang belajar di kampus fasilkom unsri dipakai. Aplikasi ini menerapkan teknologi Augmented Reality yang menggabungkan objek virtual ke dunia nyata sehingga informasinya hanya dapat dilihat melalui perantara kamera handphone ataupun komputer. Dengan adanya aplikasi ini diharapkan dapat mempermudah penggunanya dalam mengetahui informasi mengenai ruang kelas yang akan digunakannya sehingga tidak mengganggu kegiatan pengguna ruangan lain.Kata Kunci – Augmented Reality, Marker, Informasi, Android

Extensions of BLUP models for genomic prediction in heterogeneous populations: Application in a diverse switchgrass sample

ABSTRACTGenomic prediction is a useful tool to accelerate genetic gain in selection using DNA marker information. However, this technology usually relies on models that are not designed to accommodate population heterogeneity, which results from differences in marker effects across genetic backgrounds. Previous studies have proposed to cope with population heterogeneity using diverse approaches: (i) either ignoring it, therefore relying on the robustness of standard approaches; (ii) reducing it, by selecting homogenous subsets of individuals in the sample; or (iii) modelling it by using interactive models. In this study we assessed all three possible approaches, applying existing and novel procedures for each of them. All procedures developed are based on deterministic optimizations, can account for heteroscedasticity, and are applicable in contexts of admixed populations. In a case study on a diverse switchgrass sample, we compared the procedures to a control where predictions rely on homogeneous subsamples. Ignoring heterogeneity was often not detrimental, and sometimes beneficial, to prediction accuracy, compared to the control. Reducing heterogeneity did not result in further increases in accuracy. However, in scenarios of limited subsample sizes, a novel procedure, which accounted for redundancy within subsamples, outperformed the existing procedure, which only considered relationships to selection candidates. Modelling heterogeneity resulted in substantial increases in accuracy, in the cases where accounting for population heterogeneity yielded a highly significant improvement in fit. Our study exemplifies advantages and limits of the various approaches that are promising in various contexts of population heterogeneity, e.g. prediction based on historical datasets or dynamic breeding.