Did the public attribute the Flint Water Crisis to racism as it was happening? Text analysis of Twitter data to examine causal attributions to racism during a public health crisis.

The Flint Water Crisis (FWC) was an avoidable public health disaster that has profoundly affected the city's residents, a majority of whom are Black. Although many scholars and journalists have called attention to the role of racism in the water crisis, little is known about the extent to which the public attributed the FWC to racism as it was unfolding. In this study, we used natural language processing to analyze nearly six million Flint-related tweets posted between April 1, 2014, and June 1, 2016. We found that key developments in the FWC corresponded to increases in the number and percentage of tweets that mentioned terms related to race and racism. Similar patterns were found for other topics hypothesized to be related to the water crisis, including water and politics. Using sentiment analysis, we found that tweets with a negative polarity score were more common in the subset of tweets that mentioned terms related to race and racism when compared to the full set of tweets. Next, we found that word pairs that included terms related to race and racism first appeared after the January 2016 state and federal emergency declarations and a corresponding increase in media coverage of the FWC. We conclude that many Twitter users connected the events of the water crisis to race and racism in real-time. Given growing evidence of negative health effects of second-hand exposure to racism, this may have implications for understanding minority health and health disparities in the US.

Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning.

Food samples are routinely screened for food-contaminating beetles (i.e., pantry beetles) due to their adverse impact on the economy, environment, public health and safety. If found, their remains are subsequently analyzed to identify the species responsible for the contamination; each species poses different levels of risk, requiring different regulatory and management steps. At present, this identification is done through manual microscopic examination since each species of beetle has a unique pattern on its elytra (hardened forewing). Our study sought to automate the pattern recognition process through machine learning. Such automation will enable more efficient identification of pantry beetle species and could potentially be scaled up and implemented across various analysis centers in a consistent manner. In our earlier studies, we demonstrated that automated species identification of pantry beetles is feasible through elytral pattern recognition. Due to poor image quality, however, we failed to achieve prediction accuracies of more than 80%. Subsequently, we modified the traditional imaging technique, allowing us to acquire high-quality elytral images. In this study, we explored whether high-quality elytral images can truly achieve near-perfect prediction accuracies for 27 different species of pantry beetles. To test this hypothesis, we developed a convolutional neural network (CNN) model and compared performance between two different image sets for various pantry beetles. Our study indicates improved image quality indeed leads to better prediction accuracy; however, it was not the only requirement for achieving good accuracy. Also required are many high-quality images, especially for species with a high number of variations in their elytral patterns. The current study provided a direction toward achieving our ultimate goal of automated species identification through elytral pattern recognition.

Verification of fetal evoked response by magnetic dipole fitting.

BACKGROUND: Investigation of fetal evoked response to auditory or visual stimuli is an important means of understanding the developmental stages and potential problems in prenatal life. It is, however, not without certain imperfections. The biggest challenge with fetal evoked response is its low signal to noise ratio. Under noisy conditions, the detected fetal evoked response should, therefore, be further investigated to confirm that the source of the signal is from fetal brain and is not related to random noise. Existing methods for verification are: (1) visual inspection of magnetic field maps, which requires user intervention and expert knowledge which can be highly subjective; (2) simultaneous ultrasound measurement, which is expensive and technically difficult to manage; and (3) equivalent current dipole fitting, which requires knowledge of the orientation of fetal head and its dimensions that may not be available at all times. OBJECTIVE: To verify that the detected fetal evoked response signal is originating from the fetal head by using an objective and feasible method that employs magnetic dipole fitting to fetal evoked response. STUDY DESIGN: From raw fetal magnetoencephalography data, the cardiac interference was removed by frequency dependent subtraction. After averaging over stimulus triggers, the resulting signal was taken as the candidate fetal evoked response. The fetal evoked response was investigated for the highest peak in between 0.2-0.5 s, which is the expected latency of the response to the stimulus. The magnetic field at this highest peak was used for magnetic dipole fitting. The validation of peak was based on the closeness of the magnetic dipole fit to vicinity of fetal head location determined by ultrasound and the anatomically reasonable distance from the fetal heart. The methodology was first tested on a sample neonatal data before application to fetal data. RESULTS: The results of neonatal application confirmed that the source localization by magnetic dipole fitting for the brain produced meaningful results. When applied to fetal data, auditory and visual evoked response was detected in 27 of the 38 recordings. This implied that with our verification method, fetal evoked responses were detected in 71% of fetuses. CONCLUSION: Detection rate of the evoked responses were similar to earlier reports where subjective visual inspection or simultaneous ultrasound measurement were used. Our method using magnetic dipole fitting for verification is more feasible and objective compared to the earlier methods.

An enhanced frequency dependent subtraction algorithm for removal of cardiac interference from fMEG by using minimum norm projection operator.

BACKGROUND: A frequency dependent subtraction method, SUBTR, is developed to remove maternal and fetal magnetocardiography (mMCG and fMCG) interference from fetal magnetoencephalography (fMEG). But channels close to fetal head cannot be used as references for SUBTR in order to protect fMEG from subtraction and this results in cardiac residual when these channels have important fMCG frequency components. Cardiac residual creates noise in evoked response (ER) which results in poor ER detection. NEW METHOD: We developed an enhanced SUBTR algorithm, which we call SUBTR with minimum norm projection operator (SUBTRwMNPO), by employing covariance based minimum norm projection operators (MNPO). mMCG and fMCG signals are extracted from the raw data using MNPO and they are subtracted in the frequency domain from raw data to extract fetal Evoked Response (fER). RESULTS: When tested on 87 datasets, SUBTRwMNPO is shown to attenuate cardiac interference almost totally resulting in a clean fER signal. COMPARISON WITH EXISTING METHODS: Cardiac attenuation with SUBTRwMNPO is either as good as or better than SUBTR. SUBTRwMNPO has higher attenuation rate for the datasets where SUBTR leaves cardiac residual. CONCLUSIONS: SUBTRwMNPO is successful in removing cardiac interference regardless of the orientation of fMCG and fMEG signal spaces. It can also be used to remove cardiac interference when there is no prior knowledge of fetal head location.