Light inputs to dopaminergic amacrine cells of the mammalian retina

Background: The retina responds to light over a wide range of operational conditions, surpassing 10 units in a logarithmic scale. Adaptation of the retina to the particular presenting light conditions relies considerably on modulation of retinal pathways by dopamine, which is released in response to light or circadian rhythms exclusively from dopaminergic amacrine cells. Rods, cones and intrinsically photoresponsive retinal ganglion cells (ipRGCs) have all been shown to input into dopaminergic amacrine cells. However, the pathways that these photoreceptors employ to ultimately trigger dopamine release in response to light remain unclear. Methods: Ultra-high performance liquid chromatography separation and tandem mass spectrometry detection was used to quantify dopamine, and its primary metabolite 3,4-dihidroxyphenylacetic acid (DOPAC). Retinal dopamine release was assessed under various conditions, in a variety of mouse models, using two complementary experimental designs: in vivo anaesthetised mice and ex vivo explanted retinae. Conclusions: This thesis provides novel evidence about dopamine dynamics in a variety of light conditions, transgenic mouse models and presence of pharmacological agents. Surprisingly, I found that rod input is both necessary and sufficient to evoke light-induced release dopamine across a wide range of light intensities, without quantifiable contribution from cones or ipRGCs, suggesting that electrophysiological inputs do not match dopamine release. Further, this data suggests that the main pathway that drives this increase in light-induced dopamine release at light intensities where rods should be saturated is the primary rod pathway (with smaller contributions from the secondary and tertiary pathways) and involves bleaching adaptation of rods

Entropy in Image Analysis III

Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

Symbiotic Effectiveness, Phylogeny and Genetic Stability of Biserrula pelecinus-nodulating Mesorhizobium sp. isolated from Eritrea and Ethiopia

Biserrula pelecinus is a productive pasture legume with potential for replenishing soil fertility and providing quality livestock feed in Southern Australia. The experience with growing B. pelecinus in Australia suggests an opportunity to evaluate this legume in Ethiopia, due to its relevance to low-input farming systems such as those practiced in Ethiopia. However, the success of B. pelecinus is dependent upon using effective, competitive, and genetically stable inoculum strains of root nodule bacteria (mesorhizobia). Mesorhizobium strains isolated from the Mediterranean region were previously reported to be effective on B. pelecinus in Australian soils. Subsequently, it was discovered that these strains transferred genes required for symbiosis with B. pelecinus (contained on a “symbiosis island’ in the chromosome) to non-symbiotic soil bacteria. This transfer converted the recipient soil bacteria into symbionts that were less effective in N2-fixation than the original inoculant. This study investigated selection of effective, stable inoculum strains for use with B. pelecinus in Ethiopian soils. Genetically diverse and effective mesorhizobial strains of B. pelecinus were shown to be present in Ethiopian and Eritrean soils. These strains were shown to belong to the genus Mesorhizobium and carry highly mobile symbiosis islands, with a novel integration hotspot (ser-tRNA). In vitro, the transfer of the symbiosis island from these strains to a non-symbiotic recipient strain resulted in novel bacteria with a poorly effective phenotype, except for one highly effective strain. By deleting a relaxase gene, which is involved in the conjugative transfer of the symbiosis island, a more stable strain was created containing an immobile symbiosis island. The study highlights the presence of taxonomically and symbiotically distinct B. pelecinus-nodulating Mesorhizobium strains in East African soils. In these Mesorhizobium strains, the rate of symbiosis island transfer was as high as 3x10-3 in vitro. It is suggested that island transfer has a significant role in the rapid evolution of poorly effective strains. Further, it is likely that this transfer contributes to one of the most intractable problems compromising N2-fixation in agricultural systems - that of poorly effective but competitive background rhizobia. In this study, the management of symbiosis island transfer through inactivation of the relaxase gene without affecting the symbiotic phenotype was found to be a viable approach for tackling this problem

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Common Metabolites, Distinct Pathways: The Use of High-field NMR Spectroscopy Metabolomics in Neurology and Immunology

While current metabolic imaging approaches, such as positron emission tomography, hyperpolarized magnetic resonance (MR) imaging and localized MR spectroscopy, provide information on localization and visualization of metabolically-active tissues and metabolites in vivo, additional ex vivo validation and investigations are used for a deeper molecular elucidation of biological events. Metabolomics provides an insight into ongoing cellular processes in a living organism by analyzing and investigating alterations of small molecule polar compounds and lipids. Many of these alterations are directly affected by environmental and genetic factors, such as RNA expression. In recent years, besides mass spectrometry-based approaches, nuclear magnetic resonance (NMR) spectroscopy has been successfully established as an important analytical technique in metabolomics due to its non-destructive nature, high reproducibility and generation of absolute concentration values. NMR, furthermore, is one of the closest approaches to established in vivo MR technologies allowing a direct link from in vivo MR spectra to ex vivo NMR. NMR spectroscopy is, therefore, a perfect tool to quantitatively analyze small polar molecules, such as amino acids, short-chain fatty acids, energy, growth and redox metabolism-related compounds as well as metabolites of gut microbiota and lipids. The broad variety of NMR applications has led to the development of increased sensitivity probes and fully-automated commercial assays. Inventions, such as the ultrasensitive 1.7 mm microprobe, have made it possible to analyze smaller tissue or biofluid amounts of precious samples, including small preclinical organs and tissues or patient tumor biopsies, with appropriate precision and minimal tissue metabolite dilution. In this thesis project, we aimed to investigate and characterize specific metabolic alterations to clinically-relevant immunological and neurological conditions by employing NMR spectroscopy-based metabolomics. In line with the previous preclinical imaging work, two preclinical models – i) acute and chronic inflammation progression and ii) neurological gut-brain axis – were selected as core examples for a comprehensive ex vivo metabolome characterization. In the first project i), the delayed-type hypersensitivity reaction (DTHR) mouse model was subjected to a dynamic immunometabolism and inflammation characterization with the hypothesizing that the inflammation progression is related to dynamic systemic alterations for which metabolomics readout can elucidate the ongoing bio-molecular events. The inflammation was induced by repeated contact tissue challenges on mouse ear tissue. Different metabolic patterns were identified arising from either acute or chronic DTHR that correlated with the resident immune cell response and further active cell infiltration to the inflamed location. Distinct metabolic events, including switches between the scavenging of reactive oxygen and nitrogen species, facilitated the detailed characterization of the detrimental effect of prolonged inflammation and the emergency state of the system. Continuous inflammation led to limited access to substrates for energy metabolism. Chronic DTHR further required alternative anabolic pathways to sustain the cellular growth and repair process. In the second project ii), a gastric bypass surgery rat model was used for the metabolomic characterization of gut microbiota metabolites and potential gut-brain axis communication. We hypothesized that gut-brain axis communication could be responsible for the beneficial lasting effects after surgical intervention. Rats were fed a liquid sucrose diet to induce obesity since high-sugar beverages and liquid caloric consumption have become a pandemic in the adolescent population hindered by the general concept of the Western diet. Plasma and feces were studied as gut metabolism readout and further analyzed in the context of fecal microbiome, hepatic lipid profiles, and brain activity imaging to obtain a holistic overview of the systemic effects of the Roux-En-Y gastric bypass (RYGB) surgery. The gut metabolite γ-aminobutyrate (GABA) was increased in surgery animal feces together with GABA-producing microbiota species abundance compared to sham controls. RYGB surgery animals showed greater neuronal activation in midbrain regions that are known to be rich in GABAergic cells, pointing towards an activated gut-brain communication resulting from the surgery. Two main projects demonstrate how the usage of optimized preanalytical procedures, together with harmonized analytical NMR workflows provide reproducible data with comprehensive insight into the metabolism of an organism ex vivo. Further outlook includes metabolomics result integration in the context of in vivo imaging data, as the combined result evaluation can facilitate the understanding of health and disease progression, and help to streamline diagnostic, such as novel radiotracer development, and therapeutic approaches

2018 FSDG Combined Abstracts

https://scholarworks.gvsu.edu/fsdg_abstracts/1000/thumbnail.jp

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research