Adult neural stem cells and multiciliated ependymal cells share a common lineage regulated by the Geminin family members

Adult neural stem cells and multiciliated ependymalcells are glial cells essential for neurological func-tions. Together, they make up the adult neurogenicniche. Using both high-throughput clonal analysisand single-cell resolution of progenitor division pat-terns and fate, we show that these two componentsof the neurogenic niche are lineally related: adult neu-ral stem cells are sister cells to ependymal cells,whereas most ependymal cells arise from the termi-nal symmetric divisions of the lineage. Unexpectedly,we found that the antagonist regulators of DNA repli-cation, GemC1 and Geminin, can tune the proportionof neural stem cells and ependymal cells. Our find-ings reveal the controlled dynamic of the neurogenicniche ontogeny and identify the Geminin familymembers as key regulators of the initial pool of adultneural stem cells

Characterizing the Aging Process of the Human Eye: Tear Evaporation, Fluid Dynamics, Blood Flow, and Metabolism-Based Comparative Study

Eye temperature and intraocular pressure are two measurable parameters that can be monitored as a health index with aging. Deviations from the normal range of intraocular pressure and temperature lead to the formation of many diseases. This study has been carried out to evaluate the relations between the physiological and anatomical changes of the eye with aging using mathematical modeling. 2D computer-aided design of the human eye has been developed for two major groups: 21 to 30 years and 41 to 50 years. The computer simulation has been carried out to determine the effects of physiological changes of tear evaporation, fluid dynamics, blood flow, and metabolism of eye tissues with aging. The simulation has been carried out in the standing and the supine position of a human body. The rate of temperature change is – 0.0075 K per year in the standing position and – 0.007 K per year in the supine position because of the modeled anatomical and physiological effects. All the three simulation parameters of this study, the temperature of the human eye, the intraocular pressure, and the aqueous humor flow velocity, have been compared with the recent practical and simulation-based experiments to validate our results

Lightweight and Parameter-Optimized Real-Time Food Calorie Estimation from Images Using CNN-Based Approach

Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. With the help of a deep learning-based automated object identification system, it is possible to introduce accurate and intelligent solutions in the form of a mobile app. However, for these kind of applications, processing speed is an important concern as the images should be processed in real time. Although plenty of studies have been conducted that focus on food image detection-based calorie estimation, there is still a lack of an image-driven, lightweight, fast, and reliable food calorie estimation system. In this paper, we propose a method based on the parameter-optimized Convolution Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once identification process of the food items are complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process

Geostatistics for Context-Aware Image Classification

Context information is fundamental for image understanding. Many algorithms add context information by including semantic relations among objects such as neighboring tendencies, relative sizes and positions. To achieve context inclusion, popular context-aware classification methods rely on probabilistic graphical models such as Markov Random Fields (MRF) or Conditional Random Fields (CRF). However, recent studies showed that MRF/CRF approaches do not perform better than a simple smoothing on the labeling results. The need for more context awareness has motivated the use of different methods where the semantic relations between objects are further enforced. With this, we found that on particular application scenarios where some specific assumptions can be made, the use of context relationships is greatly more effective. We propose a new method, called GeoSim, to compute the labels of mosaic images with context label agreement. Our method trains a transition probability model to enforce properties such as class size and proportions. The method draws inspiration from Geostatistics, usually used to model spatial uncertainties. We tested the proposed method in two different ocean seabed classification context, obtaining state-of-art results

Lightweight and Parameter-Optimized Real-Time Food Calorie Estimation from Images Using CNN-Based Approach

Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. With the help of a deep learning-based automated object identification system, it is possible to introduce accurate and intelligent solutions in the form of a mobile app. However, for these kind of applications, processing speed is an important concern as the images should be processed in real time. Although plenty of studies have been conducted that focus on food image detection-based calorie estimation, there is still a lack of an image-driven, lightweight, fast, and reliable food calorie estimation system. In this paper, we propose a method based on the parameter-optimized Convolution Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once identification process of the food items are complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process

Automated Detection of Underwater Military Munitions Using Fusion of 2D and 2.5D Features From Optical Imagery

Technologies that can efficiently and objectively detect, identify, and map underwater military munitions are needed. The knowledge of benthic environments adjacent to underwater military munitions is crucial for remediation decisions. When attempting to identify munitions from optical imagery, tridimensional structure information obtained from the surveyed area can complement the texture information that is available in the images. In this work, we use a fusion of two-dimensional (2D) and two-and-a-half-dimensional (2.5D) features to classify munitions on the seabed from a sequence of images of an optical survey of the seabed. The 2D features respond to texture, whereas the 2.5D features respond to geometry. The 2.5D features used were coefficients of polynomial surface fitting, standard deviation, skewness, and kurtosis of the elevation, slope of principal plane, mean and standard deviation of the distance of 2.5D points to the principal plane, surface normal, curvatures, rugosity and symmetry measures. Adding the 2.5D features increased classification accuracy relative to using only 2D features when detecting discarded military munitions