Trial Outcome and Associative Learning Signals in the Monkey Hippocampus

In tasks of associative learning, animals establish new links between unrelated items by using information about trial outcome to strengthen correct/rewarded associations and modify incorrect/unrewarded ones. To study how hippocampal neurons convey information about reward and trial outcome during new associative learning, we recorded hippocampal neurons as monkeys learned novel object-place associations. A large population of hippocampal neurons (50%) signaled trial outcome by differentiating between correct and error trials during the period after the behavioral response. About half these cells increased their activity following correct trials (correct up cells) while the remaining half fired more following error trials (error up cells). Moreover, correct up cells, but not error up cells, conveyed information about learning by increasing their stimulus-selective response properties with behavioral learning. These findings suggest that information about successful trial outcome conveyed by correct up cells may influence new associative learning through changes in the cell's stimulus-selective response properties.National Institutes of Health (U.S.) (NIH grant MH48847)National Institutes of Health (U.S.) (NIH Award DA015644)National Institutes of Health (U.S.) (NIH Award MH59733)National Institutes of Health (U.S.) (NIH grant MH071847)National Institutes of Health (U.S.) (NIH grant DP1 OD003646)Fondation pour la recherche médical

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown

Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained

Prothrombotic gene mutations and Crohn's disease; is there any association?

Background/Aims: Patients with inflammatory bowel disease have an increased tendency for thromboembolism. In this study we aimed to determine the frequency of FV gene and Prothrombin G20210A gene mutations in a group of patients with Crohn's Disease (CD) and estimate its correlation with disease activity and clinical subtype

Ganciclovir-resistant cytomegalovirus encephalitis in a hematopoietic stem cell transplant recipient

We describe a 41-year-old patient, who upon receiving a bone marrow transplant in order to treat chronic myeloid leukemia, developed cytomegalovirus (CNV) retinitis and encephalitis under the ganciclovir maintenance treatment. Analysis of sequential viral isolates recovered from the patient's cerebrospinal fluid and blood showed CMV DNA with a UL97 mutation (M460V) known to confer ganciclovir resistance. Foscarnet resistance mutations were not found. Although therapy was switched to foscarnet when ganciclovir resistance was suspected, the patient was lost on posttransplant day 200

Prevalence of hepatic granulomas in chronic hepatitis B

An increasing frequency of hepatic granulomas, up to 10%, in chronic hepatitis C patients is reported, and their presence is considered to be a predictor of treatment success. However, there is only one prevalence study on granuloma in chronic hepatitis B, and its significance for treatment outcome is unknown. We aimed to determine the prevalence of hepatic granulomas in a larger group of chronic hepatitis B patients and to compare their presence with the response to interferon therapy. Biopsy specimens of chronic hepatitis B patients were reevaluated for the presence of hepatic granulomas. All patients with hepatic granuloma were screened for other granulomatous diseases by tuberculin skin test, chest X-ray and computed tomography, venereal disease research laboratory, Brucella agglutination tests, and exposure to hepatotoxic agents. We screened 663 cases of chronic hepatitis B. Hepatic granulomas were found in 10 cases (1.5%). The granulomas could not be ascribed to any other reason. Of the 10 patients with hepatic granulomas, 4 responded to interferon therapy, 2 dropped out, and 4 were nonresponders. We conclude that hepatic granuloma is a rare finding in chronic hepatitis B and its presence does not seem to predict the response to interferon therapy

The HSP90 inhibitor NVP-AUY922 potently inhibits non-small cell lung cancer growth

Heat shock protein 90 (HSP90) is involved in protein folding and functions as a chaperone for client proteins, many of which are important in non-small cell lung cancer (NSCLC) pathogenesis. We sought to define effects of the HSP90 inhibitor NVP-AUY922 in NSCLC cell lines, and identify predictors of response by in vitro and in vivo evaluation. NVP-AUY922 potently inhibited growth in all 41 NSCLC cell lines evaluated in vitro with IC50 < 100 nM. In 36 lines, IC100 (complete inhibition of proliferation) was below 40 nM. Greatest sensitivity was in lines with low baseline HSP70 protein levels. In vitro comparison of gene expression before and after NVP-AUY922 exposure demonstrated consistent changes in expression of genes involved in a wide range of cellular functions, including consistently decreased expression of dihydrofolate reductase after exposure. Expression of the co-chaperone AHA1 increased in response to exposure, and this effect was disproportionately seen in less sensitive lines. NVP-AUY922 slowed growth of A549 (KRAS mutant) xenografts, and achieved tumor stability and decreased epidermal growth factor receptor (EGFR) protein expression in H1975 xenografts, a model harboring a sensitizing and a resistance mutation for EGFR tyrosine kinase inhibitors in the EGFR gene. This impressive preclinical efficacy in a broad range of NSCLC cell lines led to clinical evaluation of NVP-AUY922 in NSCLC patients with tumors bearing known driver mutations as well as tumors without such abnormalities. An ongoing phase II NSCLC trial will incorporate correlative data to confirm our observations and guide further development of NVP-AUY922 in NSCLC. Word Count: 249 of (max 250