Two studies explored the nature of phonological awareness (PA) in Chinese. In Study 1, involving 146 children, awareness of phoneme onset did not differ from chance levels at ages 3-5 years in preschool but increased to 70% correct in first grade, when children first received phonological coding (Pinyin) instruction. Similarly, tone awareness was at better than chance levels from second year kindergarten (age 4), but increased strongly and significantly in first grade to 74% accuracy. In contrast, syllable and rime awareness increased gradually and steadily across ages 3-6 years. Patterns suggest different influences of age and literacy instruction for different PA levels. In Study 2, involving 202 preschoolers, variance in Chinese character recognition was best explained by tasks of syllable awareness, tone awareness, and speeded naming. Findings underscore the unique importance of both tone and syllable for early character acquisition in Chinese children.

Tasks tapping visual skills, orthographic knowledge, phonological awareness, speeded naming, morphological awareness and Chinese character recognition were administered to 184 kindergarteners and 273 primary school students from Beijing. Regression analyses indicated that only syllable deletion, morphological construction and speeded number naming were unique correlates of Chinese character recognition in kindergarteners. Among primary school children, the independent correlates of character recognition were rime detection, homophone judgement, morpheme production, orthographic knowledge and speeded number naming. Results underscore the importance of some dimensions of both phonological processing and morphological awareness for both very early and intermediate Chinese reading acquisition. Although significantly correlated with character recognition in younger (but not older) children, visual skills were not uniquely associated with Chinese character reading at any grade level. However, orthographic skills were strongly associated with reading in primary school but not kindergarten, suggesting that orthographic skills are more important for literacy development as reading experience increases.

It has been suggested that Saturn's moon Enceladus possesses a subsurface ocean. The recent discovery of silica nanoparticles derived from Enceladus shows the presence of ongoing hydrothermal reactions in the interior. Here, we report results from detailed laboratory experiments to constrain the reaction conditions. To sustain the formation of silica nanoparticles, the composition of Enceladus' core needs to be similar to that of carbonaceous chondrites. We show that the presence of hydrothermal reactions would be consistent with NH3- and CO2-rich plume compositions. We suggest that high reaction temperatures (>50 °C) are required to form silica nanoparticles whether Enceladus' ocean is chemically open or closed to the icy crust. Such high temperatures imply either that Enceladus formed shortly after the formation of the solar system or that the current activity was triggered by a recent heating event. Under the required conditions, hydrogen production would proceed efficiently, which could provide chemical energy for chemoautotrophic life.

Purpose: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. Methods: In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. Results: The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. Conclusions: Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.