Commercialization of genetically modified plants: progress towards the marketplace

Agricultural productivity increases over the last 40 years were driven by significant advances in several areas: plant breeding, farm mechanization, the use of crop chemicals, irrigation systems and modern farm management practices. Adding agricultural biotechnology to this set of tools promises unprecedented improvements not just in productivity but also food quality, even the use of plants as production facilities for chemicals and a reduction of our dependence on petroleum imports

Attentional breadth and proximity seeking in romantic attachment relationships

The present study provides first evidence that attentional breadth responses can be influenced by proximity-distance goals in adult attachment relationships. In a sample of young couples, we measured attachment differences in the breadth of attentional focus in response to attachment-related cues. Results showed that priming with a negative attachment scenario broadens attention when confronted with pictures of the attachment figure in highly avoidant men. In women, we found that attachment anxiety was associated with a more narrow attentional focus on the attachment figure, yet only at an early stage of information processing. We also found that women showed a broader attentional focus around the attachment figure when their partner was more avoidantly attached. This pattern of results reflects the underlying action of attachment strategies and provides insight into the complex and dynamic influence of attachment on attentional processing in a dyadic context

Evasive maneuver subsequent to CSM/LM ejection from the S-4B in earth orbit - Project Apollo

Apollo 9 evasive maneuver after command service module/lunar module ejection from Saturn S-4B stage in earth orbi

Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.

High-Throughput 3-D Cellular Assays Using Destabilized Green Fluorescence Protein

Cell assays for high-throughput screening (HTS) of potential drug candidates are important tools in the process of drug discovery. Most cellular assays are currently based on 2-D monolayer cell cultures, but 3-D cell cultures could better mimic the in vivo characteristics of actual organism tissues. Unfortunately, assays using 3-D culture models usually require significant manual manipulation and are therefore not suitable for HTS. Research under Dr. Shang-Tian Yang has resulted in a functioning system for high-throughput 3-D cellular assays using engineered cells to express enhanced green fluorescence protein (EGFP) quantifiable through fluorometry. System improvement to allow rapid assessment of cellular events, such as specific gene expression or cell cycle progress may be limited by the relatively long persistence of the currently used reporter protein in the cells. In this study a new fluorescence reporting cell line was established using a destabilized EGFP (d4EGFP) expressed in Chinese hamster ovary (CHO) cells. Correlating the fluorescence response with cell number for the d4EGFP expressing cell line in 2-D assays indicated that the fluorescence expression of d4EGFP may be too low for use in reporting cell number in a high-throughput manner. The fluorescence and cell number correlation in 3-D assays indicated that slightly better performance could be achieved with the d4EGFP reporter in 3-D but further testing is needed to demonstrate whether this improvement would be sufficient. Future work investigating growth and environmental conditions or further genetic modification of the cell line is recommended to possibly improve the fluorescence expression.The College of Engineering at The Ohio State UniversityA one-year embargo was granted for this item

Inferring meta-covariates in classification

This paper develops an alternative method for gene selection that combines model based clustering and binary classification. By averaging the covariates within the clusters obtained from model based clustering, we define “meta-covariates” and use them to build a probit regression model, thereby selecting clusters of similarly behaving genes, aiding interpretation. This simultaneous learning task is accomplished by an EM algorithm that optimises a single likelihood function which rewards good performance at both classification and clustering. We explore the performance of our methodology on a well known leukaemia dataset and use the Gene Ontology to interpret our results

Examining the Reggio Emilia approach: keys to understanding why it motivates students

Because of the success of the Reggio Emilia Approach in early childhood education, it could be useful to researchers and practitioners to identify and explicate components of the approach that make it effective in motivating students. In this paper, we examine the Reggio Emilia Approach through the lens of the MUSIC® Model of Motivation, a model based on key motivation components (i.e., empowerment, usefulness, success, interest, and caring) derived from current research and theory. We explain the connections between the Reggio approach and the MUSIC model using theoretical and practical examples to demonstrate that the success of the Reggio approach is in part due to the manner in which it is consistent with key motivation principles. We believe that educators could assess their own programs to determine whether they could do more to incorporate these motivational components into their educational environment.</jats:p

Re-examining Acts of God

Applying recent critiques of the construction of nature as distinct from and excluding all that is human, this article examines the concept of an act of God, which exculpates defendants when a disaster is “solely caused” by a “natural” event. The doctrine incorporates a classical separation of the human from the natural—a separation that is now refuted in geography and philosophy. While other areas of law such as patents, food and drug law, and land use policy have begun to acknowledge our changing understandings of human and nature, we have yet to re-examine the acts of god doctrine, which is foundationally built on this classically constructed separation. The neglect is particularly significant in light of the developing modern cultural understanding of climate change and even individual weather events as human-generated. This article suggests that by claiming a pure separation of human and natural, the acts of god doctrine embraces a fiction without indeed admitting it and thereby does damage to public confidence in the law

Comment on "Deuterium--tritium fusion reactors without external fusion breeding" by Eliezer et al

Inclusion of inverse Compton effects in the calculation of deuterium-deuterium burn under the extreme conditions considered by Eliezer et al. [Phys. Lett. A 243 (1998) 298] are shown to decrease the maximum burn temperature from about 300 keV to only 100--150 keV. This decrease is such that tritium breeding by the DD --> T + p reaction is not sufficient to replace the small amount of tritium that is initially added to the deuterium plasma in order to trigger ignition at less than 10 keV.Comment: 6 pages, 1 tabl

Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of Next-Generation Sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a `zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent Conditional Auto-Regressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.Comment: 25 pages; 3 tables, 6 figure