The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

Background: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).

Objective: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.

Milk proteomic analysis reveals differentially expressed proteins in high-yielding and low-yielding Guanzhong dairy goats at peak lactation.

The aim of this experiment was to investigate the differential proteomic characteristics of milk from high- and low-yielding Guanzhong dairy goats during the peak lactation period under the same feeding conditions. Nine Guanzhong dairy goats with high yield (H: 3.5 ± 0.

Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity.

Prior work has shown that analyzing the use of first-person singular pronouns can provide insight into individuals' mental status, especially depression symptom severity. These findings were generated by counting frequencies of first-person singular pronouns in text data. However, counting doesn't capture how these pronouns are used.

Comparative analysis of different metabolites in semen of Guanzhong dairy goats with different motility rates.

In order to explore the different metabolites of buck semen with different motility stored at 4 °C, the semen of bucks was collected by artificial vagina. The collected semen was divided into high motility group and low motility group after treatment, with 6 replicates set for each group. The semen metabolites of high motility group and low motility group were detected by Liquid Chromatography-Mass Spectrometry (LC-MS).

Data Augmentation in Defect Detection of Sanitary Ceramics in Small and Non-i.i.d Datasets.

In this study, a data-augmentation method is proposed to narrow the significant difference between the distribution of training and test sets when small sample sizes are concerned. Two major obstacles exist in the process of defect detection on sanitary ceramics. The first results from the high cost of sample collection, namely, the difficulty in obtaining a large number of training images required by deep-learning algorithms, which limits the application of existing algorithms in sanitary-ceramic defect detection.