CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

LIPID METABOLISM IN DIABETICS WITH AND WITHOUT BLOOD PRESSURE INCREASE

No abstrac

Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. We concentrated on the segmentation of calcium and soft plaque, because these structures predict the extension and the vulnerability of the atherosclerotic disease, respectively. The first step in the procedure was the extraction of texture features like local binary patterns, co-occurrence matrices and Gabor filter banks. After dimensionality reduction, the resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation,the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images

LIPID METABOLISM IN DIABETIC PATIENTS WITH AND WITHOUT BLOOD PRESSURE INCREASE

No abstrac

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes

Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants

Abstract Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ( $$\hbox {MAE}=5.23$$ MAE = 5.23 ), Association ( $$\hbox {MAE}=5.24$$ MAE = 5.24 ), and Projection ( $$\hbox {MAE}=5.28$$ MAE = 5.28 ) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value $$< 5\hbox {E}{-}8$$ < 5 E - 8 ) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes

High-Resolution Infrared Spectroscopic Measurements of Comet 2PlEncke: Unusual Organic Composition and Low Rotational Temperatures

We present high-resolution infrared spectroscopic measurements of the ecliptic comet 2P/Encke, observed on 4-6 Nov. 2003 during its close approach to the Earth, using the Near Infrared Echelle Spectrograph on the Keck II telescope. We present flux-calibrated spectra, production rates, and mixing ratios for H2O, CH3OH, HCN, H2CO, C2H2, C2H6, CH4 and CO. Comet 2P/Encke is a dynamical end-member among comets because of its short period of 3.3 years. Relative to "organics-normal" comets, we determined that 2PlEncke is depleted in HCN, H2CO, C2H2, C2H6, CH4 and CO, but it is enriched in CH3OH. We compared mixing ratios of these organic species measured on separate dates, and we see no evidence of macroscopic chemical heterogeneity in the nucleus of 2P/Encke, however, this conclusion is limited by sparse temporal sampling. The depleted abundances of most measured species suggest that 2P/Encke may have formed closer to the young Sun, before its insertion to the Kuiper belt, compared with "organics-normal" comets - as was previously suggested for other depleted comets (e.g. C/1999 S4 (LINEAR)). We measured very low rotational temperatures of 20 - 30 K for H2O, CH3OH and HCN in the near nucleus region of 2P/Encke, which correlate with one of the lowest cometary gas production rates (approx. 2.6 x 10(exp 27) molecules/s) measured thus far in the infrared. This suggests that we are seeing the effects of more efficient radiative cooling, insufficient collisional excitation, and/or inefficient heating by fast H-atoms (and icy grains) in the observed region of the coma. Its extremely short orbital period, very low gas production rate, and classification as an ecliptic comet, make 2PlEncke an important addition to our growing database, and contribute significantly to the establishment of a chemical taxonomy of comets