Diamonds are Forever

We defend the thesis that every necessarily true proposition is always true. Since not every proposition that is always true is necessarily true, our thesis is at odds with theories of modality and time, such as those of Kit Fine and David Kaplan, which posit a fundamental symmetry between modal and tense operators. According to such theories, just as it is a contingent matter what is true at a given time, it is likewise a temporary matter what is true at a given possible world; so a proposition that is now true at all worlds, and thus necessarily true, may yet at some past or future time be false in the actual world, and thus not always true. We reconstruct and criticize several lines of argument in favor of this picture, and then argue against the picture on the grounds that it is inconsistent with certain sorts of contingency in the structure of time

Predator telemetry informs temporal and spatial overlap with stocked salmonids in Lake Huron

Double-Crested Cormorants (Phalacrocorax auratus), Walleyes (Sander vitreus), and Lake Trout (Salvelinus namaycush) are migratory predators that undergo extensive movements in Lake Huron. Stocking of juvenile salmonid fish (Oncorhynchus and Salmo sp.) is an important component of fishery management in Lake Huron and assessing the spatial and temporal extent of predator movements is a useful consideration for determining when and where to stock juvenile fish to reduce predation and maximize survival. Previous investigation indicated that some Walleyes migrate to the main basin of Lake Huron in spring from Saginaw Bay. Similarly, telemetry studies of Lake Trout movement in Lake Huron have indicated an onshore movement in the spring. We used detection histories of Walleyes implanted with acoustic transmitters tagged in Saginaw Bay and Lake Trout implanted in northern Lake Huron to estimate the arrival date of migrating adults at eight ports in Lake Huron, where hatchery reared juvenile salmonids are stocked. Satellite telemetry of Cormorants that return to nesting grounds in northern Lake Huron were used to estimate their arrival dates at the same Lake Huron ports. Arrival of Walleye at Lake Huron ports ranged from April 10th to May 7th. Cormorants arrived earlier than Walleye at most Lake Huron ports (April 11th–April 18th). Lake Trout were more variable with a range of onshore movement from March 28th to May 16th. Our results suggested stocking efforts at these ports should generally occur before April 14th to decrease predatory impact from Cormorants, Walleyes, and Lake Trout

Using Healthcare Data in Embedded Pragmatic Clinical Trials among People Living with Dementia and Their Caregivers: State of the Art

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156003/1/jgs16617_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156003/2/jgs16617.pd

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave

Background:The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. Objective:This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. Methods:At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.” Results:“No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. Conclusions:Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy

Issues with variability in electronic health record data about race and ethnicity: Descriptive analysis of the National COVID Cohort Collaborative Data Enclave

BACKGROUND: The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. OBJECTIVE: This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. METHODS: At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as Declined were grouped with Refused, and Multiple Race was grouped with Two or more races and Multiracial. RESULTS: No matching concept was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. CONCLUSIONS: Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy

In situ regeneration of bioactive coatings enabled by an evolved Staphylococcus aureus sortase A

Surface immobilization of bioactive molecules is a central paradigm in the design of implantable devices and biosensors with improved clinical performance capabilities. However, in vivo degradation or denaturation of surface constituents often limits the long-term performance of bioactive films. Here we demonstrate the capacity to repeatedly regenerate a covalently immobilized monomolecular thin film of bioactive molecules through a two-step stripping and recharging cycle. Reversible transpeptidation by a laboratory evolved Staphylococcus aureus sortase A (eSrtA) enabled the rapid immobilization of an anti-thrombogenic film in the presence of whole blood and permitted multiple cycles of film regeneration in vitro that preserved its biological activity. Moreover, eSrtA transpeptidation facilitated surface re-engineering of medical devices in situ after in vivo implantation through removal and restoration film constituents. These studies establish a rapid, orthogonal and reversible biochemical scheme to regenerate selective molecular constituents with the potential to extend the lifetime of bioactive films

Information Technology to Support Improved Care For Chronic Illness

BackgroundIn populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.ObjectiveTo summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.DesignA systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.ResultsThe expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.ConclusionsSpecific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care

The Evolutionary Analysis of Emerging Low Frequency HIV-1 CXCR4 Using Variants through Time—An Ultra-Deep Approach

Large-scale parallel pyrosequencing produces unprecedented quantities of sequence data. However, when generated from viral populations current mapping software is inadequate for dealing with the high levels of variation present, resulting in the potential for biased data loss. In order to apply the 454 Life Sciences' pyrosequencing system to the study of viral populations, we have developed software for the processing of highly variable sequence data. Here we demonstrate our software by analyzing two temporally sampled HIV-1 intra-patient datasets from a clinical study of maraviroc. This drug binds the CCR5 coreceptor, thus preventing HIV-1 infection of the cell. The objective is to determine viral tropism (CCR5 versus CXCR4 usage) and track the evolution of minority CXCR4-using variants that may limit the response to a maraviroc-containing treatment regimen. Five time points (two prior to treatment) were available from each patient. We first quantify the effects of divergence on initial read k-mer mapping and demonstrate the importance of utilizing population-specific template sequences in relation to the analysis of next-generation sequence data. Then, in conjunction with coreceptor prediction algorithms that infer HIV tropism, our software was used to quantify the viral population structure pre- and post-treatment. In both cases, low frequency CXCR4-using variants (2.5–15%) were detected prior to treatment. Following phylogenetic inference, these variants were observed to exist as distinct lineages that were maintained through time. Our analysis, thus confirms the role of pre-existing CXCR4-using virus in the emergence of maraviroc-insensitive HIV. The software will have utility for the study of intra-host viral diversity and evolution of other fast evolving viruses, and is available from http://www.bioinf.manchester.ac.uk/segminator/