APLIKASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA KULIAH

Making a class schedule becomes problem and takes a long time because of several obstacles such as the lack of lecture rooms, the lack of teaching staff, and the high of courses available in one semester. This study aims to apply genetic algorithms in making class schedules to facilitate the process of making class schedules. The method used is the waterfall method with the stages of the Software Development Life Cycle. The results of the application of genetics application show that the process of making course schedules can overcome the constraints of 1) space and time clashes, 2) lecturer conflicts, 3) Friday prayer times clashing, 4) there is a time when the lecturer wants for certain reasons, and 5) practicum in the laboratory room. By passing these constraints, the application of genetic algorithms in course scheduling is categorized as effective. Based on the results of running on 51 lecturers (51 chromosomes), the average running time 30 times in a row is 25.86 minutes so that the use of genetic algorithm applications in scheduling courses is efficient.

493 Awardee Talk: Filling Knowledge Gaps in Quantitative Genetics Through Online Education

The genomic revolution has been compared to the industrial revolution, with caveats that it has happened faster and will have a far greater impact on our lives. Interpreting and using knowledge emanating from this revolution requires unique skills. Providing education in quantitative genetics that keeps pace with that need, particularly where expertise and funds are limited, remains challenging. One solution is sharing resources and capacities across-institutions to deliver high-quality instruction online. Beginning with 4 universities in 2007, expanding to 7 in 2012, a multi-state U.S. consortium built an online Masters-level curriculum in quantitative genetics and genomics. Sixteen courses were developed, each revised based on review by 2 academic peers and an instructional designer. Over 330 students from 34 U.S. and 5 international institutions have completed over 1,200 credit hours. Anonymous student feedback has been overwhelmingly positive. The curriculum was established with funding from two USDA-NIFA Higher Education Challenge grants. In 2015 it was integrated into AG*IDEA, a national consortium offering online courses in agriculture. A permanent infrastructure was thereby established with students earning formal academic credit. Only students matriculated at one of 19 AG*IDEA member universities can enroll directly, sadly limiting access, especially to international students. A potential constraint of online instruction is a disconnect with students. In some courses, a blended-learning format has been introduced with a weekly virtual recitation session. To increase engagement, an experiential learning opportunity also is offered. This entails a web-based simulation game—CyberSheep—where students apply genetic principles to a virtual breeding cooperative. Additionally, CyberSheep is typically played by 400 undergraduate students at 5 U.S. universities each academic term, contributing to their learning of animal genetics. Outcomes of these initiatives demonstrate that online training can be an effective tool to fill knowledge gaps in quantitative genetics, with opportunity to reach a wider audience.

Veterinarians’ Competence in Applying Basic Genetic Principles and Daily Implementation of Clinical Genetics: A Study in a University Environment

Veterinarian competency in genetics is vital for a meaningful application of the rapidly growing number of genetic tests available for animals. We evaluated the use of genetic tests in the daily veterinary practice and the competency of university-employed veterinarians in applying basic principles of genetics in a clinical setting through an electronic survey with 14 cases and 7 statements on genetics. Ninety-one non-geneticist veterinarians from two veterinary faculties in two different countries responded. Almost half of the participants apply genetic tests during their daily work, with frequencies varying between weekly and once a year. The most common indication to request a genetic test was diagnostic testing of clinically ill patients. Although 80% of the veterinarians communicated the result of a genetic test themselves, only 56% of them found it “very to rather easy” to find the correct test, and only 32% of them always felt competent to interpret the result of the test. The number of correctly answered questions varied widely, with median scores of 9/14 (range 0–14) and 5/7 (range 0–7) for the cases and statements, respectively. Most difficulties were seen with recognition of pedigree inheritance patterns, while veterinarians scored better in breeding advice and probability of disease estimations. Veterinarians scored best on questions related to autosomal recessive inheritance, followed by complex, autosomal dominant, X-linked recessive, and X-linked dominant inheritance. This study exposed pain points in veterinarians’ knowledge and has led to the formulation of recommendations for future education and communication between laboratories, geneticists, and veterinarians.

Mini-Review: Genetic Literacy and Engagement With Genetic Testing for Autism Spectrum Disorder

As genomic and personalized medicine is integrated into healthcare, the need for patients to understand and make decisions about their own genetic makeup increases. Genetic literacy, or one’s knowledge of genetic principles and their applications, measures an individual’s ability to apply genetic information to their own treatment. Increased genetic literacy can improve comprehension of genetic tests and therefore increase participation in testing to detect and treat genetic disorders. It can also help providers understand and explain genetic information to their patients. However, current research indicates that the population’s genetic literacy is generally low. Because many medical students, providers, and patients cannot adequately apply genetic information to their health, new and beneficial genetic technologies can be underused. More specifically, though genetic testing is recommended at the time of diagnosis for those affected by autism spectrum disorder (ASD), as few as 22% of families undergo genetic testing after diagnosis. While ASD, a neurodevelopmental condition characterized by impaired social communication and restricted interests, has both genetic and environmental risk, genetic testing can give clinicians useful information and help families avoid potentially painful and costly tests, even when many families do not receive a “positive” genetic result through microarrays or gene panels. Improving genetic literacy in populations affected by ASD can also improve attitudes toward genetic testing, thereby ensuring access to genetic health risk information. In this mini review, we discuss the current literature describing genetic literacy and genetic testing rates for ASD.

Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers

The preservation of relevant mutual information under compression is the fundamental challenge of the information bottleneck method. It has many applications in machine learning and in communications. The recent literature describes successful applications of this concept in quantized detection and channel decoding schemes. The focal idea is to build receiver algorithms intended to preserve the maximum possible amount of relevant information, despite very coarse quantization. The existent literature shows that the resulting quantized receiver algorithms can achieve performance very close to that of conventional high-precision systems. Moreover, all demanding signal processing operations get replaced with lookup operations in the considered system design. In this paper, we develop the idea of maximizing the preserved relevant information in communication receivers further by considering parametrized systems. Such systems can help overcome the need of lookup tables in cases where their huge sizes make them impractical. We propose to apply genetic algorithms which are inspired from the natural evolution of the species for the problem of parameter optimization. We exemplarily investigate receiver-sided channel output quantization and demodulation to illustrate the notable performance and the flexibility of the proposed concept.

Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes’ mobility. In these contexts, routing is NP-hard and is usually solved by heuristic “store and forward” replication-based approaches, where multiple copies of the same message are moved and stored across nodes in the hope that at least one will reach its destination. Still, the existing routing protocols produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely, Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes and find that GI produces consistent gains in delivery probability in four cases. We then verify if this improvement entails a worsening of other relevant network metrics, such as latency and buffer time. Finally, we compare the logics of the best evolved protocols with those of the baseline protocols, and we discuss the generalizability of the results across test cases.

On the Issue of Discrimination Based on Genetic Status: Legal Approaches in the Judicial Practice of Foreign States

This paper is devoted to the consideration of legal approaches to discrimination based on genetic status, formulated by the judicial practice of a number of foreign countries: Australia, the United States of America and Canada. The author notes that the regulatory framework for combating discriminatory practices based on genetic status has developed at the level of international law with the adoption of key documents in the relevant area. The author makes a conclusion about the ways to apply genetic information, which often acts as a “tool” for the implementation of discriminatory practices. As genetic testing becomes more widespread, the challenge will inevitably arise to determine what role genetic information should play in human and social life.

Absent expansion of pericentral hepatocytes and altered physiology in Axin2CreERT2 mice

Abstract/IntroductionUnderstanding how the liver regenerates is a key biological question. Hepatocytes are the principle regenerative population in the liver. Recently, numerous lineage tracing studies (which apply genetic tagging to a restricted population and track its descendants over time) have reported conflicting results using a variety of hepatocyte based reporting systems in mice1,2. The first significant lineage tracing from a distinct subpopulation of hepatocytes in homeostasis reported hyper-proliferation of self-renewing pericentral hepatocytes with their subsequent expansion across the liver lobule3. This study used a CreERT2 construct knocked into the endogenous Axin2 locus; here termed Axin2CreERT2. Subsequent studies, using either a different pericentral marker (Lgr54) or a different AxinCreERT2 transgene5, did not show lineage tracing. Here we aim to reconcile these discrepancies by re-evaluating lineage tracing in the Axin2CreERT2 knock-in model and explore the physiological consequences of this mutant allele. We were unable to find evidence of expansion of an Axin2CreERT2 labelled population and show that this population, whilst zonated, is spread throughout the lobule rather than being zonally restricted. Finally, we report that this allele results in profound perturbation of the Wnt pathway and physiology in the mouse.

Nkx2-5 defines distinct scaffold and recruitment phases during formation of the murine cardiac Purkinje fiber network

The ventricular conduction system coordinates heartbeats by rapid propagation of electrical activity through the Purkinje fiber (PF) network. PFs share common progenitors with contractile cardiomyocytes, yet the mechanisms of segregation and network morphogenesis are poorly understood. Here, we apply genetic fate mapping and temporal clonal analysis to identify murine cardiomyocytes committed to the PF lineage as early as E7.5. We find that a polyclonal PF network emerges by progressive recruitment of conductive precursors to this scaffold from a pool of bipotent progenitors. At late fetal stages, the segregation of conductive cells increases during a phase of rapid recruitment to build the definitive PF network through a non-cell autonomous mechanism. We also show that PF differentiation is impaired in Nkx2-5 haploinsufficient embryos leading to failure to extend the scaffold. In particular, late fetal recruitment fails, resulting in PF hypoplasia and persistence of bipotent progenitors. Our results identify how transcription factor dosage regulates cell fate divergence during distinct phases of PF network morphogenesis.