Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.

A joint network optimization framework to predict clinical severity from resting state functional MRI data.

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.

Effects of autonomic balance and fluid and electrolyte changes on cardiac function in infarcted rats: A serial study of sexual dimorphism.

Premenopausal women are known to show lower incidence of cardiovascular disease than men. During myocardial infarction (MI), homeostatic responses are activated, including the sympathetic autonomic nervous system and the rennin-angiotensin-aldosterone system, which is related to the fluid and electrolyte balance, both aiming to maintain cardiac output. This study sought to perform a serial evaluation of sexual dimorphism in cardiac autonomic control and fluid and electrolyte balance during the development of MI-induced heart failure in rats. Experimental MI was induced in male (M) and female (F) adult (7-9 weeks of age) Wistar rats. The animals were placed in metabolic cages to assess fluid intake and urine volume 1 and 4 weeks after inducing MI (male myocardial infarction (MMI) and female myocardial infarction (FMI) groups). They subsequently underwent echocardiographic evaluation and spectral analysis of heart rate variability. After completing each protocol, the animals were killed for postmortem evaluation and histology. The MMI group showed earlier and more intense cardiac morphological and functional changes than the FMI group, although the extent of MI did not differ between groups (P > 0.05). The MMI group showed higher sympathetic modulation and sodium and water retention than the FMI group (P < 0.05), which may partly explain both the echocardiographic and pathological findings. Females subjected to infarction seem to show attenuation of sympathetic modulation, more favourable fluid and electrolyte balances, and better preserved cardiac function compared to males subjected to the same infarction model.

Temperature oscillations of magnetization observed in nanofluid ferromagnetic graphite.

We report on unusual magnetic properties observed for nanofluid room temperature ferromagnetic graphite (with an average particle size of [Formula: see text] nm). More precisely, the measured magnetization exhibits a low temperature anomaly (attributed to the manifestation of finite size effects below the quantum temperature [Formula: see text]) as well as pronounced temperature oscillations above T = 50 K (attributed to manifestation of the hard-sphere type of pair correlations between ferromagnetic particles in the nanofluid).

[Antenatal diagnosis of a choledocal cyst]. / Diagnostic anténatal du kyste du cholédoque.

Choledocal cyst is a rare anomaly that usually becomes symptomatic during childhood. Forty to sixty percent of choledocal cysts are diagnosed before 10 years old, usually when complications occur. Today, because of advances in sonographic imaging, some cysts can de diagnosed before birth. The major prognostic factor is the development of complications such as hepatic fibrosis. Early treatment, after postnatal ultrasonography assessment, can reduce the incidence of serious complications. The authors report a case of a choledocal cyst diagnosed at 23 weeks gestation.

[Arterial hypertension among oil-drilling workers exposed to noise]. / Hipertensão arterial entre trabalhadores de petróleo expostos a ruído.

A cross-sectional study with a retrospective component was conducted to evaluate occupational noise exposure as a potential risk factor for arterial hypertension among 775 workers from an oil-drilling industry. Hypertension was defined as >/= 140/90mmHg. Occupational noise exposure was measured as: (1) exposure to sound pressure levels >/= 85dbA for 10 years or more and (2) moderate-to-severe noise-induced hearing loss (NIHL). The effects of age, education, shift work, and obesity were evaluated by stratification and logistic regression analysis. A positive association between occupational noise exposure and hypertension was found, using both the level/duration of noise exposure (RP = 1.8; 95% CI: 1.3-2.4) and NIHL (RP = 1.5; 95% CI: 1.1-2.0) as exposure indicators. Considering the study limits, long-term occupational noise exposure thus appears to be a risk factor for arterial hypertension.