New MR sequences in daily practice: susceptibility weighted imaging. A pictorial essay

Background Susceptibility-weighted imaging (SWI) is a relatively new magnetic resonance (MR) technique that exploits the magnetic susceptibility differences of various tissues, such as blood, iron and calcification, as a new source of contrast enhancement. This pictorial review is aimed at illustrating and discussing its main clinical applications. Methods SWI is based on high-resolution, threedimensional (3D), fully velocity-compensated gradientecho sequences using both magnitude and phase images. A phase mask obtained from the MR phase images is multiplied with magnitude images in order to increase the visualisation of the smaller veins and other sources of susceptibility effects, which are displayed at best after postprocessing of the 3D dataset with the minimal intensity projection (minIP) algorithm. Results SWI is very useful in detecting cerebral microbleeds in ageing and occult low-flow vascular malformations, in characterising brain tumours and degenerative diseases of the brain, and in recognizing calcifications in various pathological conditions. The phase images are especially useful in differentiating between paramagnetic susceptibility effects of blood and diamagnetic effects of calcium. SWI can also be used to evaluate changes in iron content in different neurodegenerative disorders. Conclusion SWI is useful in differentiating and characterising diverse brain disorders

Altered cardiac autonomic nervous function in depression

Background:Depression is an independent risk factor for coronary artery disease. Autonomic instability may play a mediating or moderating role in this relationship; however this is not well understood. The objective of this study was to explore cardiac autonomic function and cardiac arrhythmia in depression, the correlation between depression severity and Heart Rate Variability (HRV) related indices, and the prevalence of arrhythmia.Methods:Individuals (n = 53) with major depression as assessed by the Diagnostic and Statistical Manual of Mental Disorders, who had a Hamilton Rating Scale for Depression (HAMD) score ≥20 and a Zung Self-Rating Depression Scale score > 53 were compared to 53 healthy individuals, matched for age and gender. Multichannel Electrocardiograph ECG-92C data were collected over 24 hours. Long-term changes in HRV were used to assess the following vagally mediated changes in autonomic tone, expressed as time domain indices: Standard deviation of the NN intervals (SDNN), standard deviation of 5 min averaged NN intervals (SDANN), Root Mean Square of the Successive Differences (RMSSD) and percentage of NN intervals > 50 ms different from preceding interval (pNN50). Pearson’s correlations were conducted to explore the strength of the association between depression severity (using the SDS and HRV related indices, specifically SDNN and low frequency domain / high frequency domain (LF/HF)).Results:The values of SDNN, SDANN, RMSSD, PNN50 and HF were lower in the depression group compared to the control group (P<.05). The mean value of the LF in the depression group was higher than the in control group (P<.05). Furthermore the ratio of LF/HF was higher among the depression group than the control group (P<.05). A linear relationship was shown to exist between the severity of the depression and HRV indices. In the depression group, the prevalence of arrhythmia was significantly higher than in the control group (P<.05), particularly supraventricular arrhythmias.Conclusions:Our findings suggest that depression is accompanied by dysfunction of the cardiac autonomic nervous system, and further, that depression severity is linked to severity of this dysfunction. Individuals with depression appear to be susceptible to premature atrial and/or ventricular disease

Coping with Temperature at the Warm Edge – Patterns of Thermal Adaptation in the Microbial Eukaryote Paramecium caudatum

Ectothermic organisms are thought to be severely affected by global warming since their physiological performance is directly dependent on temperature. Latitudinal and temporal variations in mean temperatures force ectotherms to adapt to these complex environmental conditions. Studies investigating current patterns of thermal adaptation among populations of different latitudes allow a prediction of the potential impact of prospective increases in environmental temperatures on their fitness.In this study, temperature reaction norms were ascertained among 18 genetically defined, natural clones of the microbial eukaryote Paramecium caudatum. These different clones have been isolated from 12 freshwater habitats along a latitudinal transect in Europe and from 3 tropical habitats (Indonesia). The sensitivity to increasing temperatures was estimated through the analysis of clone specific thermal tolerances and by relating those to current and predicted temperature data of their natural habitats. All investigated European clones seem to be thermal generalists with a broad thermal tolerance and similar optimum temperatures. The weak or missing co-variation of thermal tolerance with latitude does not imply local adaptation to thermal gradients; it rather suggests adaptive phenotypic plasticity among the whole European subpopulation. The tested Indonesian clones appear to be locally adapted to the less variable, tropical temperature regime and show higher tolerance limits, but lower tolerance breadths.Due to the lack of local temperature adaptation within the European subpopulation, P. caudatum genotypes at the most southern edge of their geographic range seem to suffer from the predicted increase in magnitude and frequency of summer heat waves caused by climate change

A Deep Neural Network for Simultaneous Estimation of b Jet Energy and Resolution

We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of s = 13 TeV at the CERN LHC. The algorithm is trained on a large sample of simulated b jets and validated on data recorded by the CMS detector in 2017 corresponding to an integrated luminosity of 41 fb - 1 . A multivariate regression algorithm based on a deep feed-forward neural network employs jet composition and shape information, and the properties of reconstructed secondary vertices associated with the jet. The results of the algorithm are used to improve the sensitivity of analyses that make use of b jets in the final state, such as the observation of Higgs boson decay to b b ¯