Being Surveyed Can Change Later Behavior and Related Parameter Estimates

Does completing a household survey change the later behavior of those surveyed? In three field studies of health and two of microlending, we randomly assigned subjects to be surveyed about health and/or household finances and then measured subsequent use of a related product with data that does not rely on subjects' self-reports. In the three health experiments, we find that being surveyed increases use of water treatment products and take-up of medical insurance. Frequent surveys on reported diarrhea also led to biased estimates of the impact of improved source water quality. In two microlending studies, we do not find an effect of being surveyed on borrowing behavior. The results suggest that limited attention could play an important but context-dependent role in consumer choice, with the implication that researchers should reconsider whether, how, and how much to survey their subjectsEconomic

Testing a New Protocol for Extracting Microbial DNA from Urine Samples

The urinary microbiome is one of the least studied sites in humans. In order to study and characterize its inhabitants, new techniques for specimen isolation and DNA extraction must be developed. Here, a new protocol is proposed that is designed to collect both bacteria and viruses from the same urine sample. This removes the need for dedicated concentration and amplification steps, saving time and money. It also allows for a more complete catalogue of a sample\u27s inhabitants. DNA extraction and Viral concentration efficiency were tested using bacteriophages obtained from our lab, combined with lab strains of Streptococcus and E. coli

Continuous Integration of Skill, Attitude, and Knowledge Assessment across the Curriculum

The objective of this curriculum assessment process was to develop a seamless integration of skill, attitude, and knowledge assessment throughout the curriculum, and to insure that the Pharmaceutical Sciences and Pharmacy Practice departments had an ongoing awareness of what assessments were being conducted in the other area. For uniformity across the departments, definitions were agreed upon for four assessment categories: Skill, Attitude, New Knowledge, and Integrated Knowledge, which are referred to as S, A, NK, & IK. A one- to three-plus weighted scale to indicate the magnitude of assessment in a category was created. To discern an assessment profile of the entire curriculum, faculty were surveyed, and a table constructed containing all the courses in sequence, the weights for each of the four categories, and the methods used to assess the categories, such as new knowledge assessed by objective tests and case studies. Specialized assessment forms, when used, were collected as well. The Curriculum Committee then critically examined the table and conducted a follow-up to verify some data and obtain a more complete profile of others. The table produced a complete assessment profile which now allows a more direct comparison between the school’s mission and competency objectives and where these objectives are taught and how assessed, and the table allows individual faculty to see how their assessments compare to companion course

Role of sureness in evaluating AI/CADx: Lesion-based repeatability of machine learning classification performance on breast MRI

Background: Artificial intelligence/computer-aided diagnosis (AI/CADx) and its use of radiomics have shown potential in diagnosis and prognosis of breast cancer. Performance metrics such as the area under the receiver operating characteristic (ROC) curve (AUC) are frequently used as figures of merit for the evaluation of CADx. Methods for evaluating lesion-based measures of performance may enhance the assessment of AI/CADx pipelines, particularly in the situation of comparing performances by classifier. Purpose: The purpose of this study was to investigate the use case of two standard classifiers to (1) compare overall classification performance of the classifiers in the task of distinguishing between benign and malignant breast lesions using radiomic features extracted from dynamic contrast-enhanced magnetic resonance (DCE-MR) images, (2) define a new repeatability metric (termed sureness), and (3) use sureness to examine if one classifier provides an advantage in AI diagnostic performance by lesion when using radiomic features. Methods: Images of 1052 breast lesions (201 benign, 851 cancers) had been retrospectively collected under HIPAA/IRB compliance. The lesions had been segmented automatically using a fuzzy c-means method and thirty-two radiomic features had been extracted. Classification was investigated for the task of malignant lesions (81% of the dataset) versus benign lesions (19%). Two classifiers (linear discriminant analysis, LDA and support vector machines, SVM) were trained and tested within 0.632 bootstrap analyses (2000 iterations). Whole-set classification performance was evaluated at two levels: (1) the 0.632+ bias-corrected area under the ROC curve (AUC) and (2) performance metric curves which give variability in operating sensitivity and specificity at a target operating point (95% target sensitivity). Sureness was defined as 1-95% confidence interval of the classifier output for each lesion for each classifier. Lesion-based repeatability was evaluated at two levels: (1) repeatability profiles, which represent the distribution of sureness across the decision threshold and (2) sureness of each lesion. The latter was used to identify lesions with better sureness with one classifier over another while maintaining lesion-based performance across the bootstrap iterations. Results: In classification performance assessment, the median and 95% CI of difference in AUC between the two classifiers did not show evidence of difference (ΔAUC = -0.003 [-0.031, 0.018]). Both classifiers achieved the target sensitivity. Sureness was more consistent across the classifier output range for the SVM classifier than the LDA classifier. The SVM resulted in a net gain of 33 benign lesions and 307 cancers with higher sureness and maintained lesion-based performance. However, with the LDA there was a notable percentage of benign lesions (42%) with better sureness but lower lesion-based performance. Conclusions: When there is no evidence for difference in performance between classifiers using AUC or other performance summary measures, a lesion-based sureness metric may provide additional insight into AI pipeline design. These findings present and emphasize the utility of lesion-based repeatability via sureness in AI/CADx as a complementary enhancement to other evaluation measures.</p

Being surveyed can change later behavior and related parameter estimates

Does completing a household survey change the later behavior of those surveyed? In three field studies of health and two of microlending, we randomly assigned subjects to be surveyed about health and/or household finances and then measured subsequent use of a related product with data that does not rely on subjects' self-reports. In the three health experiments, we find that being surveyed increases use of water treatment products and take-up of medical insurance. Frequent surveys on reported diarrhea also led to biased estimates of the impact of improved source water quality. In two microlending studies, we do not find an effect of being surveyed on borrowing behavior. The results suggest that limited attention could play an important but context-dependent role in consumer choice, with the implication that researchers should reconsider whether, how, and how much to survey their subjects

Identification of SARS-CoV-2 variants in indoor dust.

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings

Being surveyed can change later behavior and related parameter estimates

Does completing a household survey change the later behavior of those surveyed? In three field studies of health and two of microlending, we randomly assigned subjects to be surveyed about health and/or household finances and then measured subsequent use of a related product with data that does not rely on subjects' self-reports. In the three health experiments, we find that being surveyed increases use of water treatment products and take-up of medical insurance. Frequent surveys on reported diarrhea also led to biased estimates of the impact of improved source water quality. In two microlending studies, we do not find an effect of being surveyed on borrowing behavior. The results suggest that limited attention could play an important but context-dependent role in consumer choice, with the implication that researchers should reconsider whether, how, and how much to survey their subjects.National Science Foundation (U.S.) (Grant SES-0547898)United States. Agency for International DevelopmentBill & Melinda Gates FoundationGoogle (Firm)World Ban

Mapping Flow‐Obstructing Structures on Global Rivers

To help store water, facilitate navigation, generate energy, mitigate floods, and support industrial and agricultural production, people have built and continue to build obstructions to natural flow in rivers. However, due to the long and complex history of constructing and removing such obstructions, we lack a globally consistent record of their locations and types. Here, we used a consistent method to visually locate and classify obstructions on 2.1 million km of large rivers (width ≥30 m) globally. We based our mapping on Google Earth Engine’s high resolution images, which for many places have meter-scale resolution. The resulting Global River Obstruction Database (GROD) consists of 30,549 unique obstructions, covering six different obstruction types: dam, lock, low head dam, channel dam, and two types of partial dams. By classifying a subset of the obstructions multiple times, we are able to show high classification consistency (87% mean balanced accuracy) for the three types of obstructions that fully intersect rivers: dams, low head dams, and locks. The classification of the three types of partial obstructions are somewhat less consistent (61% mean balanced accuracy). Overall, by comparing GROD to similar datasets, we estimate GROD likely captured >90% of the obstructions on large rivers. We anticipate that GROD will be of wide interest to the hydrological modeling, aquatic ecology, geomorphology, and water resource management communities

Tracking COVID-19 lineages in a single building.

Samples were collected over a 5-month period with a 1-month gap due to a notable reduced campus residential population during the holidays. Each line represents one sample, and the color of the line indicates the estimated frequency of the lineage present in that sample based on measured mutations. There is a gap from November 2021 to January 2022 due to holiday break and a reduced campus residential population.</p