Brain Functional Connectivity Is Modified By A Hypocaloric Mediterranean Diet And Physical Activity In Obese Women

Functional magnetic resonance imaging (fMRI) in the resting state has shown altered brain connectivity networks in obese individuals. However, the impact of a Mediterranean diet on cerebral connectivity in obese patients when losing weight has not been previously explored. The aim of this study was to examine the connectivity between brain structures before and six months after following a hypocaloric Mediterranean diet and physical activity program in a group of sixteen obese women aged 46.31 +/- 4.07 years. Before and after the intervention program, the body mass index (BMI) (kg/m(2)) was 38.15 +/- 4.7 vs. 34.18 +/- 4.5 (p < 0.02), and body weight (kg) was 98.5 +/- 13.1 vs. 88.28 +/- 12.2 (p < 0.03). All subjects underwent a pre- and post-intervention fMRI under fasting conditions. Functional connectivity was assessed using seed-based correlations. After the intervention, we found decreased connectivity between the left inferior parietal cortex and the right temporal cortex (p < 0.001), left posterior cingulate (p < 0.001), and right posterior cingulate (p < 0.03); decreased connectivity between the left superior frontal gyrus and the right temporal cortex (p < 0.01); decreased connectivity between the prefrontal cortex and the somatosensory cortex (p < 0.025); and decreased connectivity between the left and right posterior cingulate (p < 0.04). Results were considered significant at a voxel-wise threshold of p <= 0.05, and a cluster-level family-wise error correction for multiple comparisons of p <= 0.05. In conclusion, functional connectivity between brain structures involved in the pathophysiology of obesity ( the inferior parietal lobe, posterior cingulate, temporo-insular cortex, prefrontal cortex) may be modified by a weight loss program including a Mediterranean diet and physical exercise

Seed Legislation in Europe and Crop Genetic Diversity

publishedVersio

Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model

BACKGROUND AND OBJECTIVE: The fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions. METHODS: In this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images. RESULTS: The results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data). CONCLUSIONS: The proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology

Reconstruction of the fetus face from three-dimensional ultrasound using a newborn face statistical shape model

Background and objective: The fetal face is an essential source of information in the assessment of congenital malformations and neurological anomalies. Disturbance in early stages of development can lead to a wide range of effects, from subtle changes in facial and neurological features to characteristic facial shapes observed in craniofacial syndromes. Three-dimensional ultrasound (3D US) can provide more detailed information about the facial morphology of the fetus than the conventional 2D US, but its use for pre-natal diagnosis is challenging due to imaging noise, fetal movements, limited field-of-view, low soft-tissue contrast, and occlusions. Methods: In this paper, we propose the use of a novel statistical morphable model of newborn faces, the BabyFM, for fetal face reconstruction from 3D US images. We test the feasibility of using newborn statistics to accurately reconstruct fetal faces by fitting the regularized morphable model to the noisy 3D US images. Results: The results indicate that the reconstructions are quite accurate in the central-face and less reliable in the lateral regions (mean point-to-surface error of 2.35 mm vs 4.86 mm). The algorithm is able to reconstruct the whole facial morphology of babies from US scans while handle adverse conditions (e.g. missing parts, noisy data). Conclusions: The proposed algorithm has the potential to aid in-utero diagnosis for conditions that involve facial dysmorphology.This work is partly supported by the eSCANFace project (PID2020-114083GB-I00) funded by the Spanish Ministry of Science and Innovation, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant R42HD081712. A. Alomar was supported by AGAUR under the FI scholarship and G. Piella was supported by ICREA under the ICREA Academia programme

Quantitative Evaluation of Genetic Diversity in Wheat Germplasm Using Molecular Markers

Characterization of germplasm by means of DNA fingerprinting techniques provides a tool for precise germplasm identification and a quantitative estimate of genetic diversity. This estimate is important because a decrease in genetic variability might result in a reduction of the plasticity of the crops to respond to changes in climate, pathogen populations, or agricultural practices. In this study, 105 Argentine bread wheat (Triticum aestivum L.) cultivars released between 1932 and 1995 were characterized by simple sequence repeat (SSR) and amplified fragment length polymorphism (AFLP) markers. A selected subset of 10 highly informative SSR was used to construct an Identification Matrix that allowed the discrimination of the 105 cultivars. Data obtained from SSR markers were complemented by information derived from AFLPs. Molecular data were used to quantify genetic diversity across Argentine wheat breeding programs and to determine if modern wheat cultivars have a lower genetic diversity than earlier cultivars (genetic erosion). No significant differences in genetic diversity were found among the large private and public breeding programs, suggesting that each of them contains a representative sample of the complete diversity of the Argentine germplasm. Significant differences were found for both SSR and AFLP only between breeding programs with large differences in number of released cultivars. No significant differences in genetic diversity were found between the group of cultivars released before 1960 and those released in each of the following three decades. Average diversity values based on SSR markers were almost identical for the four analyzed periods. Genetic diversity estimates based on AFLP data confirmed the absence of a reduction of genetic diversity with time, but significant differences (P = 0.01) were found between bread wheat cultivars released in the 1970s (PIC = 0.28) and those released in the 1980s (PIC = 0.34). These results show that the Argentine bread wheat germplasm has maintained a relatively constant level of genetic diversity during the last half century