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r reflect how segmentation quality affects dose distribution and related tumor control and toxicity.

This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity.In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.

In 2020 the coronavirus disease 19 (COVID-19) pandemic imposed a total and sudden lockdown. We aimed to investigate the consequences of the first COVID-19 lockdown (mid-March - mid-April 2020) on motor and non-motor symptoms (NMS) in a cohort of French people with Parkinson's disease (PwP).

PwP were enrolled either by an on-line survey sent from the national France Parkinson association (FP) to reach the French community of PwP or as part of outpatients' telemedicine visits followed by an hospital-based Parkinson Expert Center (PEC). All patients were evaluated using the same standardized questionnaire assessing motor and NMS (including a list of most disabling, new or worsened symptoms and Patient's Global Impression-Improvement scales [PGI-I]) psycho-social queries and quality of life.

2653 PwP were included 441 (16.6%) in the PEC group and 2122 (83.4%) in the community-based group. Physiotherapy was interrupted among 88.6% of the patients. 40.9% referred a clinical modification of their symptoms. Based on the questionnaire, pain (9.3%), rigidity (9.1%) and tremor (8.5%) were the three most frequently new or worsened reported symptoms. Based on the PGI-I, the motor symptoms were the most affected domain, followed by pain and psychic state. PwP in community-based group tended to have more frequent worsening for motor symptoms, motor complications, pain and confusion than those of the PEC group.

The first COVID-19 lockdown had a negative impact on motor and NMS of PwP. Efforts should be allocated to avoid interruption of care, including physiotherapy and physical activities and implement telemedicine. .

The first COVID-19 lockdown had a negative impact on motor and NMS of PwP. Efforts should be allocated to avoid interruption of care, including physiotherapy and physical activities and implement telemedicine. .

The previously observed effects of nonpolar additives on the scission energy and rheological properties of surfactant wormlike micelles can be explained in terms of the spatial distribution of the additive within the micelles. The dependence of the scission energy with the molecular organization of the system can be analyzed with a molecular theory capable of describing the thermodynamics and structure of the micelles.

A new theoretical method to determine the scission energy of surfactant wormlike micelles is introduced. This methodology is based on a molecular theory that explicitly considers molecular details of all components of the micelles, and their inter- and intramolecular interactions without the use of fitting and/or empirical macroscopic parameters.

The predicted effects of the concentration, molecular structure and hydrophobicity of the additive on the scission energy of cetyltrimethylammonium bromide (CTAB) wormlike micelles are found to be in qualitative agreement with previous experimental observations. In particular, our theory captures the decrease of micellar length with increasing content of highly hydrophobic additives and the non-monotonic dependence of the viscosity with additive hydrophobicity. The latter effect arises because highly and mildly hydrophobic additives affect the scission energy of wormlike micelles via markedly different molecular mechanisms.

The predicted effects of the concentration, molecular structure and hydrophobicity of the additive on the scission energy of cetyltrimethylammonium bromide (CTAB) wormlike micelles are found to be in qualitative agreement with previous experimental observations. In particular, our theory captures the decrease of micellar length with increasing content of highly hydrophobic additives and the non-monotonic dependence of the viscosity with additive hydrophobicity. click here The latter effect arises because highly and mildly hydrophobic additives affect the scission energy of wormlike micelles via markedly different molecular mechanisms.