Radar signal categorization using a neural network

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications

Stochastic modeling and financial viability of mollusk aquaculture

Compared to finfish and crustaceans, limited attention has been given to the economic modeling and production risk analysis of mollusk aquaculture. Given mollusk aquaculture's sensitivity to environmental factors, understanding production risk and its relationship to production technology and location is critical to firm viability. We modeled production as a function of random elements and performed stochastic risk analysis utilizing Monte Carlo simulation in conjunction with sensitivity analysis and scenario comparison. We applied these methods to compare different equipment systems and production strategies. This paper provides a framework for shellfish risk research that can be applied to various regions and species.publishedVersio

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations

MitoNeoD:a mitochondria-targeted superoxide probe

Mitochondrial superoxide (O2⋅−) underlies much oxidative damage and redox signaling. Fluorescent probes can detect O2⋅−, but are of limited applicability in vivo, while in cells their usefulness is constrained by side reactions and DNA intercalation. To overcome these limitations, we developed a dual-purpose mitochondrial O2⋅− probe, MitoNeoD, which can assess O2⋅− changes in vivo by mass spectrometry and in vitro by fluorescence. MitoNeoD comprises a O2⋅−-sensitive reduced phenanthridinium moiety modified to prevent DNA intercalation, as well as a carbon-deuterium bond to enhance its selectivity for O2⋅− over non-specific oxidation, and a triphenylphosphonium lipophilic cation moiety leading to the rapid accumulation within mitochondria. We demonstrated that MitoNeoD was a versatile and robust probe to assess changes in mitochondrial O2⋅− from isolated mitochondria to animal models, thus offering a way to examine the many roles of mitochondrial O2⋅−production in health and disease

Electroplasma technologies: automation of the project life cycle

Efficiency of the automated methods of plasmatrons designing can be raised due to integration of designing and manufacture technologies. The basic directions of automation for plasmotron designing are considered. Models and forms of algorithmization for the automated designing in electroplasma technologies are presentedЭффективность автоматизированных методов проектирования плазмотронов можно повысить за счет интеграции технологий проектирования и производства. Рассмотрены основные направления автоматизации процедур проектирования плазмотронов. Представлены модели и формы алгоритмизации автоматизированного проектирования в электроплазменных технология

Lactation failure in Src knockout mice is due to impaired secretory activation

<p>Abstract</p> <p>Background</p> <p>Mammary gland development culminates in lactation and is orchestrated by numerous stimuli and signaling pathways. The Src family of nonreceptor tyrosine kinases plays a pivotal role in cell signaling. In order to determine if Src plays a role in mammary gland development we have examined mammary gland development and function during pregnancy and lactation in mice in which expression of Src has been eliminated.</p> <p>Results</p> <p>We have characterized a lactation defect in the Src-/- mice which results in the death of over 80% of the litters nursed by Src-/- dams. Mammary gland development during pregnancy appears normal in these mice; however secretory activation does not seem to occur. Serum prolactin levels are normal in Src-/- mice compared to wildtype controls. Expression of the prolactin receptor at both the RNA and protein level was decreased in Src-/- mice following the transition from pregnancy to lactation, as was phosphorylation of STAT5 and expression of milk protein genes. These results suggest that secretory activation, which occurs following parturition, does not occur completely in Src-/- mice. Failed secretory activation results in precocious involution in the mammary glands of Src-/- even when pups were suckling. Involution was accelerated following pup withdrawal perhaps as a result of incomplete secretory activation. In vitro differentiation of mammary epithelial cells from Src-/- mice resulted in diminished production of milk proteins compared to the amount of milk proteins produced by Src+/+ cells, indicating a direct role for Src in regulating the transcription/translation of milk protein genes in mammary epithelial cells.</p> <p>Conclusion</p> <p>Src is an essential signaling modulator in mammary gland development as Src-/- mice exhibit a block in secretory activation that results in lactation failure and precocious involution. Src appears to be required for increased expression of the prolactin receptor and successful downstream signaling, and alveolar cell organization.</p