| 1. |
- Ranhem, Cecilia, et al.
(författare)
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Expression of LRIG proteins as possible prognostic factors in primary vaginal carcinoma
- 2017
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- Background: Primary vaginal carcinoma (PVC) is a rare malignancy. Established prognostic factors include tumour stage and age at diagnosis. The leucine-rich repeats and immunoglobuline-like domains (LRIG)-1 protein functions as a tumour suppressor, but less is known about the functions of LRIG2 and LRIG3. The present study aimed to evaluate the expression of LRIG proteins and analyse their possible associations with clinical characteristics and survival in a cohort of PVC patients.Methods: We used immunohistochemistry to investigate LRIG1, LRIG2, and LRIG3 expression in tumour samples from a consecutive cohort of 70 PVC patients. The association between LRIG protein expression and clinical characteristics and cancer-specific survival was investigated using univariate and multivariate analyses.Results: The majority of PVC patients (72%) had > 50% LRIG1-and LRIG2-positive cells, and no or low LRIG3-positive cells. HPV status was significantly correlated with LRIG1 expression (p = 0.0047). Having high LRIG1 expression was significantly correlated with superior cancer-specific survival in univariate and multivariate analyses. LRIG2 and LRIG3 expression did not significantly correlate with clinical characteristics or survival.Conclusion: LRIG1 expression might be of interest as a prognostic marker in PVC patients, whereas the role of LRIG2 and LRIG3 expression remains to be clarified.
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| 2. |
- Sikström, Sverker, et al.
(författare)
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The Role of Sparsely Distributed Representations in Familiarity Recognition of Verbal and Olfactory Materials
- 2018
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Ingår i: Cognitive Processing. - : Springer Science and Business Media LLC. - 1612-4782 .- 1612-4790. ; 19:4, s. 481-494
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Tidskriftsartikel (refereegranskat)abstract
- We present the generalized signal detection theory (GSDT), where familiarity is described by a sparse binomial distribution of binary node activity rather than by normal distribution of familiarity. Items are presented in a distributed representation, where each node receives either noise only, or signal and noise. An old response (i.e., a ‘yes’ response) is made if at least one node receives signal plus noise that is larger than the activation threshold, and item variability is determined by the distribution of activated nodes as the threshold is varied. A distinct representation leads to better performance and a lower ratio of new to old item variability, than a more distributed and less distinct representations. Here we apply the GSDT to empirical data on verbal and olfactory memory and suggest that verbal memory relies on a distinct neural item representation whereas olfactory memory has a fuzzy neural representation leading to poorer memory and inducing a larger ratio of new to old item variability.
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