Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

Sintering of titanium with yttrium oxide additions for the scavenging of chlorine impurities

Chloride impurities in titanium powders are extremely difficult to remove and present a long-standing problem in titanium powder metallurgy. We show that the detrimental effects of chlorides on the sintering of titanium can be mitigated with trace additions of yttrium oxide, which has a high affinity for the normally volatile species and forms highly stable oxychloride reaction products. Compacts that would otherwise exhibit gross swelling and excessive porosity due to chloride impurities can be now sintered to near full density by liquid phase sintering. The potency of yttrium oxide additions is observable at levels as low as 500 ppm. The scavenging of chlorine by YO appears to be independent of alloy composition and sintering regime. It is effective when used with high-chloride powders such as Kroll sponge fines but ineffective when used with powders containing NaCl impurities or during solid-state sintering. The identification of highly potent chlorine scavengers may enable the future development of chloride-tolerant powder metallurgy (PM) alloys aimed at utilizing low-cost, high-chloride powder feedstocks

Can gender categorization influence the perception of animated virtual humans?

Animations have become increasingly realistic with the evolution of Computer Graphics (CG). In particular, human models and behaviors were represented through animated virtual humans, sometimes with a high level of realism. In particular, gender is a characteristic that is related to human identification, so that virtual humans assigned to a specific gender have, in general, stereotyped representations through movements, clothes, hair and colors, in order to be understood by users as desired by designers. An important area of study is finding out whether participants' perceptions change depending on how a virtual human is visually presented. Findings in this area can help the industry to guide the modeling and animation of virtual humans to deliver the expected impact to the audience. In this paper, we reproduce, through CG, a perceptual study that aims to assess gender bias in relation to a simulated baby. In the original study, two groups of people watched the same video of a baby reacting to the same stimuli, but one group was told the baby was female and the other group was told the same baby was male, producing different perceptions. The results of our study with virtual babies were similar to the findings with real babies. First, it shows that people's emotional response change depending on the character gender attribute, in this case the only difference was the baby's name. Our research indicates that by just informing the name of a virtual human can be enough to create a gender perception that impact the participant emotional answer.Comment: 8 pages, 1 figure, 2 table

Why Compact Tori For Fusion?

A compact torus (CT) has a toroidal magnetic and plasma geometry, but is contained within a simply-connected vacuum vessel such as a cylinder. Spheromaks and field-reversed configurations fall into this category. Compact tori are translatable and have a high engineering beta. The primary benefit of CTs for fusion is the absence of toroidal field and Ohmic Heating coils and the many problems brought on by them. Studying fusion-relevant plasma in simply-connected geometries affords the world fusion program both physics and technology opportunities not found in other configurations. This paper outlines the technology and physics opportunities of compact tori, and presents a cost model based on geometry for comparison with less compact configurations

Local stabilisation of polar order at charged antiphase boundaries in antiferroelectric (Bi_0.85Nd_0.15)(Ti_0.1Fe_0.9)O₃

Observation of an unusual, negatively-charged antiphase boundary in (Bi<sub>0.85</sub>Nd<sub>0.15</sub>)(Ti<sub>0.1</sub>Fe<sub>0.9</sub>)O<sub>3</sub> is reported. Aberration corrected scanning transmission electron microscopy is used to establish the full three dimensional structure of this boundary including O-ion positions to ~ ± 10 pm. The charged antiphase boundary stabilises tetragonally distorted regions with a strong polar ordering to either side of the boundary, with a characteristic length scale determined by the excess charge trapped at the boundary. Far away from the boundary the crystal relaxes into the well-known Nd-stabilised antiferroelectric phase

Modeling soil water dynamics considering measurement uncertainty

In shallow water table controlled environments, surface water management impacts groundwater table levels and soil water dynamics. The study goal was to simulate soil water dynamics in response to canal stage raises considering uncertainty in measured soil water content. WAVE (Water and Agrochemicals in the soil, crop and Vadose Environment) was applied to simulate unsaturated flow above a shallow aquifer. Global sensitivity analysis was performed to identify model input factors with greatest influence on predicted soil water content. Nash-Sutcliffe increased and Root Mean Square Error reduced when uncertainties in measured data were considered in goodness-of-fit calculations using measurement probability distributions and probable asymmetric error boundaries; implying that appropriate model performance evaluation should be done using uncertainty ranges instead of single values. Although uncertainty in the experimental measured data limited evaluation of the absolute predictions by the model, WAVE was found a useful exploratory tool for estimating temporal variation in soil water content. Visual analysis of soil water content time series under proposed changes in canal stage management indicated that sites with land surface elevation of less than 2.0 m NGVD29 were predicted to periodically experience saturated conditions in the root zone and shortening of the growing season if canal stage is raised more than 9 cm and maintained at this level. The models developed could be combined with high resolution digital elevation models in future studies to identify areas with the greatest risk of experiencing saturated root zone. The study also highlighted the need to incorporate measurement uncertainty when evaluating performance of unsaturated flow models

Smart automotive technology adherence to the law: (de)constructing road rules for autonomous system development, verification and safety

Driving is an intuitive task that requires skill, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans, focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users including wild animals. Modern motor vehicles include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer’s in-depth knowledge of traffic legislation as well. This article presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. Our approach (de)constructs road rules in legal terminology and specifies them in ‘structured English logic’ that is expressed as ‘Boolean logic’ for automation and ‘Lawmaps’ for visualization. We demonstrate an example using these tools leading to the construction and validation of a ‘Bayesian Network model’. We strongly believe these tools to be approachable by programmers and the general public, useful in development of Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.fals

Dynamic factor analysis of surface water management impacts on soil and bedrock water contents in Southern Florida Lowlands

As part of the C111 spreader canal project, structural and operational modifications involving incremental raises in canal stage are planned along one of the major canals (i.e., C111) separating Everglades National Park and agricultural production areas to the east of the park. This study used Dynamic Factor Analysis (DFA) as an alternative tool to physically based models to explore the relationship between different hydrologic variables and the effect of proposed changes in surface water management on soil and bedrock water contents in south Florida. To achieve the goal, objectives were to: (1) use DFA to identify the most important factors affecting temporal variation in soil and bedrock water contents, (2) develop a simplified DFA based regression model for predicting soil and bedrock water contents as a function of canal stage and (3) assess the effect of the proposed incremental raises in canal stage on soil and bedrock water contents. DFA revealed that 5 common trends were the minimum required to describe unexplained variation in the 11 time series studied. Introducing canal stage, water table evaporation and net recharge resulted in lower Akaike information criterion (AIC) and higher Nash-Sutcliffe (C[subscript eff]) values. Results indicated that canal stage significantly (t > 2) drives temporal variation in soil and bedrock water contents, which was represented as scaled frequency while net surface recharge was significant in 7 out of the 11 time series analyzed. The effect of water table evaporation was not significant at all sites. Results also indicated that the most important factor influencing temporal variation in soil and bedrock water contents in terms of regression coefficient magnitude was canal stage. Based on DFA results, a simple regression model was developed to predict soil and bedrock water contents at various elevations as a function of canal stage and net recharge. The performance of the simple model ranged from good (C[subscript eff] ranging from 0.56 to 0.74) to poor (C[subscript eff] ranging from 0.10 to 0.15), performance was better at sites with smaller depths to water table (< 1 m) highlighting the effect of micro-topography on soil and bedrock water content dynamics. Assessment of the effect of 6, 9 and 12 cm increases in canal stage using the simple regression model indicated that changes in temporal variation in soil and bedrock water contents were negligible (average<1.0% average change) at 500 to 2000 m from C111 (or low elevations) which may be attributed to the near saturation conditions already occurring at these sites. This study used DFA to explore the relationship between soil and bedrock water dynamics and surface water stage in shallow water table environments. This approach can be applied to any system in which detailed physical modeling would be limited by inadequate information on parameters or processes governing the physical system

Degree of explanation

Partial explanations are everywhere. That is, explanations citing causes that explain some but not all of an effect are ubiquitous across science, and these in turn rely on the notion of degree of explanation. I argue that current accounts are seriously deficient. In particular, they do not incorporate adequately the way in which a cause’s explanatory importance varies with choice of explanandum. Using influential recent contrastive theories, I develop quantitative definitions that remedy this lacuna, and relate it to existing measures of degree of causation. Among other things, this reveals the precise role here of chance, as well as bearing on the relation between causal explanation and causation itself