Oklahoma State University–Stillwater

About: Oklahoma State University–Stillwater is a education organization based out in Stillwater, Oklahoma, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 18267 authors who have published 36743 publications receiving 1107500 citations. The organization is also known as: Oklahoma State University & OKState.

Tensions in Learning to Teach Accommodation and the Development of a Teaching Identity

Abstract: This article analyzes how Sharon, a student teacher, negotiated the different conceptions of teaching that provided the expectations for good instruction in her university and the site of her student teaching and how her effort to reconcile the different belief systems affected her identity as a teacher. The key settings of Sharon’s experience were the university program, her third-grade class at Harding Elementary, and her first teaching job. During student teaching, Sharon experienced frustrating tensions because her cooperating teacher provided little room for experimentation, mentoring instead with a mimetic approach. When in her first job, Sharon had the opportunity to resolve instructional problems with greater authority. We see tensions that require a socially contextualized intellectual resolution rather than simply one of relational accommodation as potentially productive in creating environments conductive to the formation of a satisfying teaching identity.

Gene transfer from bacteria and archaea facilitated evolution of an extremophilic eukaryote

Abstract: Some microbial eukaryotes, such as the extremophilic red alga Galdieria sulphuraria, live in hot, toxic metal-rich, acidic environments. To elucidate the underlying molecular mechanisms of adaptation, we sequenced the 13.7-megabase genome of G. sulphuraria. This alga shows an enormous metabolic flexibility, growing either photoautotrophically or heterotrophically on more than 50 carbon sources. Environmental adaptation seems to have been facilitated by horizontal gene transfer from various bacteria and archaea, often followed by gene family expansion. At least 5% of protein-coding genes of G. sulphuraria were probably acquired horizontally. These proteins are involved in ecologically important processes ranging from heavy-metal detoxification to glycerol uptake and metabolism. Thus, our findings show that a pan-domain gene pool has facilitated environmental adaptation in this unicellular eukaryote.

The impact of genetic selection for increased milk yield on the welfare of dairy cows.

Abstract: Milk yield per cow has more than doubled in the previous 40 years and many cows now produce more than 20,000 kg of milk per lactation. The increase in production should be viewed with concern because: i) the increase in milk yield has been accompanied by declining fertility, increasing leg and metabolic problems and declining longevity; ii) there are unfavourable genetic correlations between milk yield and fertility, mastitis and other production diseases, indicating that deterioration in fertility and health is largely a consequence of selection for increased milk yield; and iii) high disease incidence, reduced fertility, decreased longevity and modification of normal behaviour are indicative of substantial decline in cow welfare. Improving welfare is important as good welfare is regarded by the public as indicative of sustainable systems and good product quality and may also be economically beneficial. Expansion of the Profitable Lifetime Index used in the UK to include mastitis resistance and fertility could increase economic response to selection by up to 80%, compared with selection for milk production alone. In the last 10 years, several breeding organisations in Europe and North America followed the example of Nordic Countries and have included improving fertility and reducing incidence of mastitis in their breeding objectives, but these efforts are still timid. A multi-trait selection programme in which improving health, fertility and other welfare traits are included in the breeding objective, and appropriately weighted relative to production traits, should be adopted by all breeding organisations motivated in their goal of improving welfare.

Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification

Abstract: Convolutional neural networks (CNNs) have gained remarkable success on many image classification tasks in recent years. However, the performance of CNNs highly relies upon their architectures. For the most state-of-the-art CNNs, their architectures are often manually designed with expertise in both CNNs and the investigated problems. Therefore, it is difficult for users, who have no extended expertise in CNNs, to design optimal CNN architectures for their own image classification problems of interest. In this article, we propose an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks. The most merit of the proposed algorithm remains in its “automatic” characteristic that users do not need domain knowledge of CNNs when using the proposed algorithm, while they can still obtain a promising CNN architecture for the given images. The proposed algorithm is validated on widely used benchmark image classification datasets, compared to the state-of-the-art peer competitors covering eight manually designed CNNs, seven automatic + manually tuning, and five automatic CNN architecture design algorithms. The experimental results indicate the proposed algorithm outperforms the existing automatic CNN architecture design algorithms in terms of classification accuracy, parameter numbers, and consumed computational resources. The proposed algorithm also shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.