Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior

High Speed Image Segmentation using a Binary Neural Network.

In the very near future large amounts of Remotely Sensed data will become available on a daily basis. Unfortunately, it is not clear if the processing methods are available to deal with this data in a timely fashion. This paper describes research towards an approach which will allow a user to perform a rapid pre-search of large amounts of image data for regions of interest based on texture. The method is based on a novel neural network architecture (ADAM) that is designed primarily for speed of operation by making use of computationally simple pre-processing and only uses Boolean operations in the weights of the network. To facilitate interactive use of the network, it is capable of rapid training. The paper outlines the neural network, its application to RS data in comparison with other methods, and briefly describes a fast hardware implementation of the network. 1 Introduction The advent of new satellites producing tens of megabytes of image-data per day has placed a challenge on th..

A Sabin 1 poliovirus-based vaccine vector transfects Vero cells with high efficiency

Over the past 40 years, live oral poliovirus (PV) vaccines have contributed to the eradication of wild PV in most countries. These live vaccine strains have a high safety record and can stimulate both cellular and humoral immune responses. As both of these factors are critical characteristics of a good vaccine, we aimed to modify the oral PV vaccines to create a powerful vaccine vector for extraneous antigen expression. In this study, we amplified three separate fragments from the Sabin 1 virus genome by RT-PCR and cloned them into the pGEM-TEasy vector. A cassette containing engineered protease cleavage sites and a polylinker was introduced into one of these fragments (f1) in front of the translation start site. This construction facilitated the insertion of foreign genes into the vector and the subsequent release of their co-translated antigens after digestion by endogenous protease. We also placed a ribozyme (Rz) sequence between the T7 promoter and viral genomic DNA so that in vitro transcription and Rz cleavage recreated the authentic 5′ end of the PV genome RNA. Poly(A)40 tails were added to the 3′ end of the genome to stabilize the transcribed RNA. The three PV genome fragments and their derivatives were cloned into various types of vectors that were transfected into Vero cells. Virus rescue experiments demonstrated that both the Rz and poly(A)40 elements were required for high transfection efficiency of the vector-derived RNAs