Putting it into perspective: Mathematics in the undergraduate science curriculum

Mathematics and science are tightly interwoven, yet they are often treated as distinct disciplines in the educational context. This study details the development, implementation and outcomes of a teaching intervention that highlights the links between mathematics and science, in the form of a first-year interdisciplinary course. A mixed method study using surveys and focus groups was employed to investigate undergraduate science students' perceptions of their experiences. Findings reveal that students bring strong beliefs about the nature of mathematics and science from secondary school, which can impact significanly on the success of interdisciplinary science-mathematics courese at the teritary level. Despite this, a range of beneficial outcomes can arise from such courses when they are delivered within a framework of analysing real-world issues. However, students with weak mathematical skills derived little benefit from an interdisciplinary approach and are likely to disengage from learning, in comparision with students who enter university with a solid foundation in mathematics

Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose to adapt the system to an on-line grading machine. In particular, this work was focused to study the feasibility of identifying organic stains on brown eggshells (dirty eggshell), caused by blood, feathers, feces, etc., from natural stains, caused by deposits of pigments on the outer layer of clean eggshells. During the analysis a total of 384 eggs were evaluated (clean: 148, dirty: 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the colour of the natural stains (clear or dark). For each sample digital images were acquired by employing a Charged Coupled Device (CCD) camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images, with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group on the basis of geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify near 98% of the samples with a very low processing time (0.05s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining similar percentage of correctly classified samples (97%)

Long-term outcomes of early childhood science education: insight from a cross-national comparative case study on conceptual understanding of science

The purpose of this research was to explore the long term outcomes of either participating or not participating in early childhood science education on Grade 6 students’ conceptual understanding of science. The research is situated in a conceptual framework that evokes Piagetian developmental levels as both potential curriculum constraints and potential models of efficacy. The research design was a multiple case study of Grade 6 children from three schools in China (n=140) who started formal science education in the third grade, and Grade 6 children from three matched schools in Australia (n=105) who started learning science in kindergarten. The students’ understanding was assessed by a science quiz and in-depth interview. The data showed that participating children from the high socio-economic schools in China and Australia had similar understandings of science. Divergence between the medium and low socio-economic schools, however, indicated that the grounding in early childhood science education in Australia may have placed these children at an advantage. Alternative explanations for the divergence including the nature of classroom instruction in the two countries are discussed

Challenging the Science Curriculum Paradigm: TeachingPrimary Children Atomic-Molecular Theory

Solutions to global issues demand the involvement of scientists, yet concern exists about retention rates in science as students pass through school into University. Young children are curious about science, yet are considered incapable of grappling with abstract and microscopic concepts such as atoms, sub-atomic particles, molecules and DNA. School curricula for primary (elementary) aged children reflect this by their limitation to examining only what phenomena are without providing any explanatory frameworks for how or why they occur. This research challenges the assumption that atomic-molecular theory is too difficult for young children, examining new ways of introducing atomic theory to 9 year olds and seeks to verify their efficacy in producing genuine learning in the participants. Early results in three cases in different schools indicate these novel methods fostered further interest in science, allowed diverse children to engage and learn aspects of atomic theory, and satisfied the children’s desire for intellectual challenge. Learning exceeded expectations as demonstrated in the post-interview findings. Learning was also remarkably robust, as demonstrated in two schools eight weeks after the intervention, and in one school, one year after their first exposure to ideas about atoms, elements and molecules

Human Cytomegalovirus IE1 Protein Elicits a Type II Interferon-Like Host Cell Response That Depends on Activated STAT1 but Not Interferon-γ

Human cytomegalovirus (hCMV) is a highly prevalent pathogen that, upon primary infection, establishes life-long persistence in all infected individuals. Acute hCMV infections cause a variety of diseases in humans with developmental or acquired immune deficits. In addition, persistent hCMV infection may contribute to various chronic disease conditions even in immunologically normal people. The pathogenesis of hCMV disease has been frequently linked to inflammatory host immune responses triggered by virus-infected cells. Moreover, hCMV infection activates numerous host genes many of which encode pro-inflammatory proteins. However, little is known about the relative contributions of individual viral gene products to these changes in cellular transcription. We systematically analyzed the effects of the hCMV 72-kDa immediate-early 1 (IE1) protein, a major transcriptional activator and antagonist of type I interferon (IFN) signaling, on the human transcriptome. Following expression under conditions closely mimicking the situation during productive infection, IE1 elicits a global type II IFN-like host cell response. This response is dominated by the selective up-regulation of immune stimulatory genes normally controlled by IFN-γ and includes the synthesis and secretion of pro-inflammatory chemokines. IE1-mediated induction of IFN-stimulated genes strictly depends on tyrosine-phosphorylated signal transducer and activator of transcription 1 (STAT1) and correlates with the nuclear accumulation and sequence-specific binding of STAT1 to IFN-γ-responsive promoters. However, neither synthesis nor secretion of IFN-γ or other IFNs seems to be required for the IE1-dependent effects on cellular gene expression. Our results demonstrate that a single hCMV protein can trigger a pro-inflammatory host transcriptional response via an unexpected STAT1-dependent but IFN-independent mechanism and identify IE1 as a candidate determinant of hCMV pathogenicity

Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs

A blood spot detection neural network was trained, tested, and evaluated entirely on eggs with blood spots and grade A eggs. The neural network could accurately distinguish between grade A eggs and blood spot eggs. However, when eggs with other defects were included in the sample, the accuracy of the neural network was reduced. The accuracy was also reduced when evaluating eggs from other poultry houses. To minimize these sensitivities, eggs with cracks and dirt stains were included in the training data as examples of eggs without blood spots. The training data also combined eggs from different sources. Similar inaccuracies were observed in neural networks for crack detection and dirt stain detection. New neural networks were developed for these defects using the method applied for the blood spot neural network development