497 research outputs found

    Tolerance and safety evaluation of N, N-dimethylglycine, a naturally occurring organic compound, as a feed additive in broiler diets

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    N, N-dimethylglycine (DMG) is a tertiary amino acid that naturally occurs as an intermediate metabolite in choline-to-glycine metabolism. The objective of the present trial was to evaluate tolerance, safety and bioaccumulation of dietary DMG in broilers when supplemented at 1 g and 10 g Na-DMG/kg. A feeding trial was conducted using 480 1-d-old broiler chicks that were randomly allocated to twenty-four pens and fed one of three test diets added with 0, 1 or 10 g Na-DMG/kg during a 39 d growth period. Production performance was recorded to assess tolerance and efficacy of the supplement. At the end of the trial, toxicity was evaluated by means of haematology, plasma biochemistry and histopathology of liver, kidney and heart (n 12), whereas bioaccumulation was assessed on breast meat, liver, blood, kidney and adipose tissue (n 8). Carcass traits were similar between the control and 1 g Na-DMG/kg feed groups (P>0·05), but the feed:gain ratio was significantly improved at 1 g Na-DMG/kg feed compared with the control or the 10-fold dose (P = 0·008). Histological examinations showed no pathological effects and results of haematology and plasma biochemistry revealed similar values between the test groups (P>0·05). Bioaccumulation occurred at the 10-fold dose, but the resulting DMG content in breast meat was comparable with, for instance, wheat bran and much lower than uncooked spinach. In conclusion, DMG at 1 g Na-DMG/kg improved the feed:gain ratio in broilers without DMG being accumulated in consumer parts. Furthermore, dietary supplementation with DMG up to 10 g Na-DMG/kg did not induce toxicity or impaired performance in broilers

    Analysis of cranial neural crest migratory pathways in axolotl using cell markers and transplantation

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    We have examined the ability of normal and heterotopically transplanted neural crest cells to migrate along cranial neural crest pathways in the axolotl using focal DiI injections and in situ hybridization with the neural crest marker, AP-2. DiI labeling demonstrates that cranial neural crest cells migrate as distinct streams along prescribed pathways to populate the maxillary and mandibular processes of the first branchial arch, the hyoid arch and gill arches 1-4, following migratory pathways similar to those observed in other vertebrates. Another neural crest marker, the transcription factor AP-2, is expressed by premigratory neural crest cells within the neural folds and migrating neural crest cells en route to and within the branchial arches. Rotations of the cranial neural folds suggest that premigratory neural crest cells are not committed to a specific branchial arch fate, but can compensate when displaced short distances from their targets by migrating to a new target arch. In contrast, when cells are displaced far from their original location, they appear unable to respond appropriately to their new milieu such that they fail to migrate or appear to migrate randomly. When trunk neural folds are grafted heterotopically into the head, trunk neural crest cells migrate in a highly disorganized fashion and fail to follow normal cranial neural crest pathways. Importantly, we find incorporation of some trunk cells into branchial arch cartilage despite the random nature of their migration. This is the first demonstration that trunk neural crest cells can form cartilage when transplanted to the head. Our results indicate that, although cranial and trunk neural crest cells have inherent differences in ability to recognize migratory pathways, trunk neural crest can differentiate into cranial cartilage when given proper instructive cues

    A framework for algorithm stability

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    We say that an algorithm is stable if small changes in the input result in small changes in the output. Algorithm stability plays an important role when analyzing and visualizing time-varying data. However, so far, there are only few theoretical results on the stability of algorithms, possibly due to a lack of theoretical analysis tools. In this paper we present a framework for analyzing the stability of algorithms. We focus in particular on the tradeoff between the stability of an algorithm and the quality of the solution it computes. Our framework allows for three types of stability analysis with increasing degrees of complexity: event stability, topological stability, and Lipschitz stability. We demonstrate the use of our stability framework by applying it to kinetic Euclidean minimum spanning trees

    Insights from Amphioxus into the Evolution of Vertebrate Cartilage

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    Central to the story of vertebrate evolution is the origin of the vertebrate head, a problem difficult to approach using paleontology and comparative morphology due to a lack of unambiguous intermediate forms. Embryologically, much of the vertebrate head is derived from two ectodermal tissues, the neural crest and cranial placodes. Recent work in protochordates suggests the first chordates possessed migratory neural tube cells with some features of neural crest cells. However, it is unclear how and when these cells acquired the ability to form cellular cartilage, a cell type unique to vertebrates. It has been variously proposed that the neural crest acquired chondrogenic ability by recruiting proto-chondrogenic gene programs deployed in the neural tube, pharynx, and notochord. To test these hypotheses we examined the expression of 11 amphioxus orthologs of genes involved in neural crest chondrogenesis. Consistent with cellular cartilage as a vertebrate novelty, we find that no single amphioxus tissue co-expresses all or most of these genes. However, most are variously co-expressed in mesodermal derivatives. Our results suggest that neural crest-derived cartilage evolved by serial cooption of genes which functioned primitively in mesoderm

    Understanding the Neural Bases of Implicit and Statistical Learning

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    © 2019 Cognitive Science Society, Inc. Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of “implicit statistical learning.” However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms. In this review, we describe current knowledge about neural mechanisms underlying both implicit learning and statistical learning, highlighting converging findings between these two literatures. A common thread across all paradigms is that learning is supported by interactions between the declarative and nondeclarative memory systems of the brain. We conclude by discussing several outstanding research questions and future directions for each of these two research fields. Moving forward, we suggest that the two literatures may interface by defining learning according to experimental paradigm, with “implicit learning” reserved as a specific term to denote learning without awareness, which may potentially occur across all paradigms. By continuing to align these two strands of research, we will be in a better position to characterize the neural bases of both implicit and statistical learning, ultimately improving our understanding of core mechanisms that underlie a wide variety of human cognitive abilities
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