Infants’ Developing Understanding of Social Gaze

Young infants are sensitive to self-directed social actions, but do they appreciate the intentional, target-directed nature of such behaviors? The authors addressed this question by investigating infants’ understanding of social gaze in third-party interactions (N = 104). Ten-month-old infants discriminated between 2 people in mutual versus averted gaze, and expected a person to look at her social partner during conversation. In contrast, 9-month-old infants showed neither ability, even when provided with information that highlighted the gazer’s social goals. These results indicate considerable improvement in infants’ abilities to analyze the social gaze of others toward the end of their 1st year, which may relate to their appreciation of gaze as both a social and goal-directed action.Psycholog

Transient wall shear stress estimation in coronary bifurcations using convolutional neural networks

Background and Objective: Haemodynamic metrics, such as blood flow induced shear stresses at the inner vessel lumen, are associated with the development and progression of coronary artery disease. Understanding these metrics may therefore improve the assessment of an individual's coronary disease risk. However, the calculation of such luminal Wall Shear Stress (WSS) using traditional Computational Fluid Dynamics (CFD) methods is relatively slow and computationally expensive. As a result, CFD based haemodynamic computation is not suitable for integrated and large-scale use in clinical settings. Methods: In this work, deep learning techniques are proposed as an alternative method to CFD, whereby luminal WSS magnitude can be predicted in coronary bifurcations throughout the cardiac cycle based on the steady state solution (which takes <120 seconds to calculate including preprocessing), vessel geometry and additional global features. The deep learning model is trained on a dataset of 101 patient-specific and 2626 synthetic left main bifurcation models with 26 separate patient-specific cases used as the test set. Results: The model showed high fidelity predictions with <5% (normalised against mean WSS magnitude) deviation to CFD derived values as the gold-standard method, while being orders of magnitude faster with on average <2 minutes versus 3 hours computation for transient CFD. Conclusions: This method therefore offers a new approach to substantially reduce the computational cost involved in, for example, large-scale population studies of coronary haemodynamic metrics, and may therefore open the pathway for future clinical integration

Learning Left Main Bifurcation Shape Features with an Autoencoder

Geometric characteristics of the coronary arteries have been suggested as potential markers for disease risk. However, evaluation of such characteristics rely on judgement by human experts, and are thus variable and may lack sophistication. Here we apply recent advances in 3D deep learning to automatically obtain shape representation of the Left Main Bifurcation (LMB) of the coronary artery. We train a Variational Auto-Encoder based on the FoldingNet architecture to encode LMB shape features in a 450-dimension feature vector. The geometric features of patient-specific LMBs can then be manipulated by modifying, combining or interpolating the feature vectors before decoding. We also show that these vectors, on average, perform better than hand-crafted features in predicting measures of adverse blood flow (oscillating shear index or 'OSI', relative residence time 'RRT' and time averaged wall shear stress 'TAWSS') with a R2 goodness of fit value of 84.1% compared to 79.7%. These learned representations can also be used in other downstream predictive modelling tasks where an encoded version of a LMB is needed

Helical Flow in Healthy and Diseased Patient-specific Coronary Bifurcations

Helical flow (HF) exists in healthy and diseased coronary bifurcations and was found to have a protective atherosclerotic vascular effect in other vessels. However, the role of HF in patient-specific human coronary arteries still needs further study, and is therefore the objective of this study in both healthy and diseased bifurcations. Computational studies were conducted on 16 patient-specific coronary bifurcations, including eight healthy and eight identical cases with idealized narrowing to represent disease. In general, higher HF intensity may have a favorable effect as it corelated to the reduction of the percentage vessel area exposed to adverse time averaged wall shear stress (TAWSS%) in both healthy and diseased models. The HF intensity and distribution of each model varies due to the complex shape of patient-specific models. The presence of disease appears to have an important impact on the downstream HF patterns and the TAWSS distributions. Clinical Relevance - By understanding the relationship between HF and hemodynamics, HF may be used as a predictor for the formation and progression of atherosclerotic plaque in coronary arteries instead of near-wall WSS measures, which can be determined with higher accuracy in vivo

Smoothed Complexity Theory

Smoothed analysis is a new way of analyzing algorithms introduced by Spielman and Teng (J. ACM, 2004). Classical methods like worst-case or average-case analysis have accompanying complexity classes, like P and AvgP, respectively. While worst-case or average-case analysis give us a means to talk about the running time of a particular algorithm, complexity classes allows us to talk about the inherent difficulty of problems. Smoothed analysis is a hybrid of worst-case and average-case analysis and compensates some of their drawbacks. Despite its success for the analysis of single algorithms and problems, there is no embedding of smoothed analysis into computational complexity theory, which is necessary to classify problems according to their intrinsic difficulty. We propose a framework for smoothed complexity theory, define the relevant classes, and prove some first hardness results (of bounded halting and tiling) and tractability results (binary optimization problems, graph coloring, satisfiability). Furthermore, we discuss extensions and shortcomings of our model and relate it to semi-random models.Comment: to be presented at MFCS 201

Editorial: Advancements in the Understanding of Anthropogenic Impacts on the Microbial Ecology and Function of Aquatic Environments

Aquatic environments are important ecosystems providing multiple services to humankind and directly affecting economic income worldwide. These ecosystems have increasingly been threatened by changes in global climate and diverse anthropogenic activities—from agriculture to industry (Häder et al., 2020). In general, the continuous exploitation of aquatic ecosystems has caused severe impacts on biological diversity despite some efforts to control habitat exploitation via local legislation (Popper et al., 2020). For instance, the leaching of pollutants and chemical hazards, the eutrophisation caused by extensive use of chemicals in agriculture and aquaculture, changes in land use, and the disposal of urban wastes; are the major factors responsible for most of the anthropogenic impacts on these ecosystems worldwide (Cotta et al., 2019). As such, properly monitoring the effects of these human activities is critical to aid the early detection of potential chemicals and activities with large impacts in aquatic ecosystems. Besides, advances in ecological research can provide the basis for developing new strategies of remediation and recovery of impacted systems (Taketani et al., 2010)

TECPR1 promotes aggrephagy by direct recruitment of LC3C autophagosomes to lysosomes

The accumulation of protein aggregates is involved in the onset of many neurodegenerative diseases. Aggrephagy is a selective type of autophagy that counteracts neurodegeneration by degrading such aggregates. In this study, we found that LC3C cooperates with lysosomal TECPR1 to promote the degradation of disease-related protein aggregates in neural stem cells. The N-terminal WD-repeat domain of TECPR1 selectively binds LC3C which decorates matured autophagosomes. The interaction of LC3C and TECPR1 promotes the recruitment of autophagosomes to lysosomes for degradation. Augmented expression of TECPR1 in neural stem cells reduces the number of protein aggregates by promoting their autophagic clearance, whereas knockdown of LC3C inhibits aggrephagy. The PH domain of TECPR1 selectively interacts with PtdIns(4)P to target TECPR1 to PtdIns(4)P containing lysosomes. Exchanging the PH against a tandem-FYVE domain targets TECPR1 ectopically to endosomes. This leads to an accumulation of LC3C autophagosomes at endosomes and prevents their delivery to lysosomes. Many neurodegenerative disorders are characterised by the accumulation of protein aggregates in neurons. Here, the authors show that the lysosomal protein TECPR1 selectively recruits mature autophagosomes via an interaction with LC3C to break down protein aggregates in neural stem cells