flowCut: An R package for automated removal of outlier events and flagging of files based on time versus fluorescence analysis.

Technical artifacts such as clogging that occur during the data acquisition process of flow cytometry data can cause spurious events and fluorescence intensity shifting that impact the quality of the data and its analysis results. These events should be identified and potentially removed before being passed to the next stage of analysis. flowCut, an R package, automatically detects anomaly events in flow cytometry experiments and flags files for potential review. Its results are on par with manual analysis and it outperforms existing automated approaches.

Outcomes of hip fracture surgery during the COVID-19 pandemic.

PURPOSE: To investigate if changes to hospital operational models during the COVID-19 pandemic negatively impacted overall time to surgery (TtS) as well as morbidity and mortality rates of hip fractures (HFx). METHODS: 416 patients treated for OTA 31 fractures at a single institution between January 2019 and November 2020 were reviewed. TtS as well as morbidity and mortality rates were obtained from pre-pandemic and pandemic groups. RESULTS: 263 patients were treated pre-pandemic and 153 were treated during the pandemic. There were no significant differences in median TtS, readmission rates (p = 0.134), reoperation rates (p = 0.052), 30-day (p = 0.095) and 90-day (p = 0.22) mortality rates. CONCLUSION: Reallocation of hospital resources in response to the COVID-19 pandemic did not negatively impact surgical timing or complications. TtS for HFx remains a challenge and often requires multidisciplinary care, which is complicated by a pandemic. However, this study demonstrates HFx standard of care can be maintained despite COVID-19 obstacles to treatment efficiency and efficacy.

Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US.

Smoke from wildfires is a growing health risk across the US. Understanding the spatial and temporal patterns of such exposure and its population health impacts requires separating smoke-driven pollutants from non-smoke pollutants and a long time series to quantify patterns and measure health impacts. We develop a parsimonious and accurate machine learning model of daily wildfire-driven PM2.5 concentrations using a combination of ground, satellite, and reanalysis data sources that are easy to update. We apply our model across the contiguous US from 2006 to 2020, generating daily estimates of smoke PM2.5 over a 10 km-by-10 km grid and use these data to characterize levels and trends in smoke PM2.5. Smoke contributions to daily PM2.5 concentrations have increased by up to 5 µg/m3 in the Western US over the last decade, reversing decades of policy-driven improvements in overall air quality, with concentrations growing fastest for higher income populations and predominantly Hispanic populations. The number of people in locations with at least 1 day of smoke PM2.5 above 100 µg/m3 per year has increased 27-fold over the last decade, including nearly 25 million people in 2020 alone. Our data set can bolster efforts to comprehensively understand the drivers and societal impacts of trends and extremes in wildfire smoke.

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.

Pediatric Soft Tissue Perineurioma in the Head and Neck.

Soft tissue type perineuriomas (STP), or Extraneural perineuriomas, are typically found in the superficial extremities or trunk of adult patients. Their incidence in the head and neck is exceptionally rare, particularly amongst the pediatric population. Since 1978, only 19 cases of pediatric STP have been reported, with only one in the neck. This case report describes the second case of STP in the neck of a child as well as reviews the current literature on pediatric STP. The pattern of patient genetic anomalies associated with the few pediatric STP cases encountered suggests an association between genetic aberrations and STP. Clinicians should be aware of STP when formulating a differential diagnosis of pediatric soft tissue masses in the head and neck despite the rarity of this tumor.

Osteoradionecrosis of the hyoid presenting as acute airway compromise.

BACKGROUND: Osteoradionecrosis (ORN) of the mandible is a well-known complication of radiation therapy for head and neck cancer. However, few reports have described hyoid bone ORN and its clinical implications. METHODS: We describe a retrospective case series of previously irradiated patients who were seen with sudden airway compromise, found to have underlying pathological hyoid fractures secondary to osteoradionecrosis. RESULTS: Six patients within postchemoradiation period (3-9 months) for oropharyngeal squamous cell carcinoma were seen with acute-onset dyspnea. Computed topography (CT) imaging was remarkable for severe airway luminal narrowing and pathological hyoid fractures. All six patients required urgent intervention with direct laryngoscopy and tracheostomy. Intraoperatively, five patients were seen with exposed necrotic hyoid bones. CONCLUSION: The hyoid and its associated musculature strongly influence upper airway patency. ORN may compromise its physiological function and leads to acute airway compromise. Hyoid ORN may hold significant and imperative clinical implications in head and neck cancer post-treatment surveillance.

Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive.

Field-level monitoring of crop types in the United States via the Cropland Data Layer (CDL) has played an important role in improving production forecasts and enabling large-scale study of agricultural inputs and outcomes. Although CDL offers crop type maps across the conterminous US from 2008 onward, such maps are missing in many Midwestern states or are uneven in quality before 2008. To fill these data gaps, we used the now-public Landsat archive and cloud computing services to map corn and soybean at 30 m resolution across the US Midwest from 1999-2018. Our training data were CDL from 2008-2018, and we validated the predictions on CDL 1999-2007 where available, county-level crop acreage statistics, and state-level crop rotation statistics. The corn-soybean maps, which we call the Corn-Soy Data Layer (CSDL), are publicly hosted on Google Earth Engine and also available for download online.

Data-Driven Flow Cytometry Analysis.

The emergence of flow and mass cytometry technologies capable of generating 40-dimensional data has spurred research into automated methodologies that address bottlenecks across the entire analysis process from quality checking, data transformation, and cell population identification, to biomarker identification and visualizations. We review these approaches in the context of the stepwise progression through the different steps, including normalization, automated gating, outlier detection, and graphical presentation of results.

Personality Characteristics and Neurocognitive Functions in Parents of Children with Autism Spectrum Disorder.

BACKGROUND: The increasing prevalence of autism spectrum disorder in children and the huge family burdens have caused concern in the academic field as well as society. AIM: To study the personality characteristics and neurocognitive functions in the Chinese parents of children with autism spectrum disorder (ASD) and compare them with the parents of well-developed children. METHOD: This study recruited 41 Chinese children who met the diagnostic criteria of autism spectrum disorder according to Diagnostic and Statistical Manual of mental disorders-IV (DSM-IV). Their 79 biological parents were evaluated by means of Eysenck Personality Questionnaire (EPQ) and a set of neuropsychological tests. The normal control group consisted of 80 parents of well-developed children, and they were matched by age and gender. RESULTS: We found that the EPQ scores in the P scale for parents of children with ASD were significantly higher than the parents of well-developed children (t=1.68, p=0.039), while their scores in E scale and L scale were significantly lower (t=1.84, p=0.035; t=2.07, p=0.023). We also identified that the parents of children with ASD took significantly longer time than the normal control to complete Trail Making Test (TMT) Part A and Part B-M (t=1.57, p=0.013; t=0.83, p=0.019). CONCLUSION: Compared to the parents of well-developed children, the parents of children with ASD were more likely to be unconcerned, rigid, stubborn, introverted, and reticent. They displayed less novelty and thrill-seeking behaviors, and had limited social skills and maturity. Although the general cognitive functions including IQ were relatively intact in the parents of children with ASD, there were impairments in their planning, flexibility and visual processing functions.