| 1. |
- Abbafati, Cristiana, et al.
(författare)
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- 2020
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Ahmad, Tauseef, 1986, et al.
(författare)
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Methodology for Power-Aware Coherent Receiver Design
- 2013
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Ingår i: Optics InfoBase Conference Papers. - 2162-2701. ; , s. SPT4D.4-
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Konferensbidrag (refereegranskat)abstract
- We describe a methodology to design and evaluate DSP hardware for a coherent receiver. Important parameters that can be assessed include DSP power consumption and chip area.
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| 3. |
- Caruso, Vanni, et al.
(författare)
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The G protein-coupled receptor GPR162 is widely distributed in the CNS and highly expressed in the hypothalamus and in hedonic feeding areas
- 2014
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Ingår i: Gene. - : Elsevier BV. - 0378-1119 .- 1879-0038. ; 553:1, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- The Rhodopsin family is a class of integral membrane proteins belonging to G protein-coupled receptors (GPCRs). To date, several orphan GPCRs are still uncharacterized and in this study we present an anatomical characterization of the GPR162 protein and an attempt to describe its functional role. Our results show that GPR162 is widely expressed in GABAergic as well as other neurons within the mouse hippocampus, whereas extensive expression is observed in areas related to energy homeostasis and hedonic feeding such as hypothalamus, amygdala and ventral tegmental area, regions known to be involved in the regulation of palatable food consumption.
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| 4. |
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| 5. |
- Hellsten, Sofie V., et al.
(författare)
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The gene expression of numerous SLC transporters is altered in the immortalized hypothalamic cell line N25/2 following amino acid starvation
- 2017
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Ingår i: FEBS Open Bio. - : Wiley. - 2211-5463. ; 7:2, s. 249-264
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Tidskriftsartikel (refereegranskat)abstract
- Amino acids are known to play a key role in gene expression regulation,and in mammalian cells, amino acid signaling is mainly mediated via twopathways, the mammalian target of rapamycin complex 1 (mTORC1) pathwayand the amino acid responsive (AAR) pathway. It is vital for cells tohave a system to sense amino acid levels, in order to control protein andamino acid synthesis and catabolism. Amino acid transporters are crucialin these pathways, due to both their sensing and transport functions. Inthis large-scale study, an immortalized mouse hypothalamic cell line (N25/2)was used to study the gene expression changes following 1, 2, 3, 5 or 16 hof amino acid starvation. We focused on genes encoding solute carriers(SLCs) and putative SLCs, more specifically on amino acid transporters.The microarray contained 28 270 genes and 86.2% of the genes wereexpressed in the cell line. At 5 h of starvation, 1001 genes were upregulatedand 848 genes were downregulated, and among these, 47 genes from theSLC superfamily or atypical SLCs were found. Of these, 15 were genesencoding amino acid transporters and 32 were genes encoding other SLCsor atypical SLCs. Increased expression was detected for genes encodingamino acid transporters from system A, ASC, L, N, T, xc-, and y+. UsingGO annotations, genes involved in amino acid transport and amino acidtransmembrane transporter activity were found to be most upregulated at3 h and 5 h of starvation.
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| 6. |
- Sohaib, Ahmad Tauseef, et al.
(författare)
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Evaluating classifiers for emotion recognition using EEG
- 2013
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Ingår i: Foundations of Augmented Cognition. - Las Vegas : Springer. - 9783642394539 - 9783642394546 ; , s. 492-501
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Konferensbidrag (refereegranskat)abstract
- There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%.
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