Thinking through Objects

Thinking through objects: is the culmination of my research on objects and design. It is a reflection of my interest in the concise construction of a three dimensional object from cut, manipulated, and glued sheets of paper. As a material, paper has an implied commonness, familiarity, fragility, and temporariness, with clearly understood characteristics and qualities. In the same way, the objects the paper sculptures represent have a familiarity and temporariness about them, serving a specific role for a period of time before being replaced or updated. Selected from my own person experience, the works represent a personal history or narrative. Each piece serves as a bookmark in my own timeline, with the cohesive body of work illustrating an evolution of objects through my life

Profile: The Kilifi Health and Demographic Surveillance System (KHDSS).

The Kilifi Health and Demographic Surveillance System (KHDSS), located on the Indian Ocean coast of Kenya, was established in 2000 as a record of births, pregnancies, migration events and deaths and is maintained by 4-monthly household visits. The study area was selected to capture the majority of patients admitted to Kilifi District Hospital. The KHDSS has 260 000 residents and the hospital admits 4400 paediatric patients and 3400 adult patients per year. At the hospital, morbidity events are linked in real time by a computer search of the population register. Linked surveillance was extended to KHDSS vaccine clinics in 2008. KHDSS data have been used to define the incidence of hospital presentation with childhood infectious diseases (e.g. rotavirus diarrhoea, pneumococcal disease), to test the association between genetic risk factors (e.g. thalassaemia and sickle cell disease) and infectious diseases, to define the community prevalence of chronic diseases (e.g. epilepsy), to evaluate access to health care and to calculate the operational effectiveness of major public health interventions (e.g. conjugate Haemophilus influenzae type b vaccine). Rapport with residents is maintained through an active programme of community engagement. A system of collaborative engagement exists for sharing data on survival, morbidity, socio-economic status and vaccine coverage

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests

© 2019 The Author(s) Background: Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups. Methods: Patients undergoing primary unilateral total knee arthroplasty (n = 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery. Results: Patients separated into 2 clusters (n = 46 and n = 22) with significantly different test completion times (12.6 s vs 21.6 s, P \u3c .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P = .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P = .07). Features with the largest effect size between groups were distinguished as important functional parameters. Conclusion: This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters

Variation in Periodontal Diagnosis and Treatment Planning Among Clinical Instructors

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153684/1/jddj002203372005693tb03919x.pd

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Motivation: Epistasis, the presence of gene–gene interactions, has been hypothesized to be at the root of many common human diseases, but current genome-wide association studies largely ignore its role. Multifactor dimensionality reduction (MDR) is a powerful model-free method for detecting epistatic relationships between genes, but computational costs have made its application to genome-wide data difficult. Graphics processing units (GPUs), the hardware responsible for rendering computer games, are powerful parallel processors. Using GPUs to run MDR on a genome-wide dataset allows for statistically rigorous testing of epistasis

A technique for rapid source apportionment applied to ambient organic aerosol measurements from a thermal desorption aerosol gas chromatograph (TAG)

We present a rapid method for apportioning the sources of atmospheric organic aerosol composition measured by gas chromatography–mass spectrometry methods. Here, we specifically apply this new analysis method to data acquired on a thermal desorption aerosol gas chromatograph (TAG) system. Gas chromatograms are divided by retention time into evenly spaced bins, within which the mass spectra are summed. A previous chromatogram binning method was introduced for the purpose of chromatogram structure deconvolution (e.g., major compound classes) (Zhang et al., 2014). Here we extend the method development for the specific purpose of determining aerosol samples’ sources. Chromatogram bins are arranged into an input data matrix for positive matrix factorization (PMF), where the sample number is the row dimension and the mass-spectra-resolved eluting time intervals (bins) are the column dimension. Then twodimensional PMF can effectively do three-dimensional factorization on the three-dimensional TAG mass spectra data. The retention time shift of the chromatogram is corrected by applying the median values of the different peaks’ shifts. Bin width affects chemical resolution but does not affect PMF retrieval of the sources’ time variations for low-factor solutions. A bin width smaller than the maximum retention shift among all samples requires retention time shift correction. A six-factor PMF comparison among aerosol mass spectrometry (AMS), TAG binning, and conventional TAG compound integration methods shows that the TAG binning method performs similarly to the integration method. However, the new binning method incorporates the entirety of the data set and requires significantly less pre-processing of the data than conventional single compound identification and integration. In addition, while a fraction of the most oxygenated aerosol does not elute through an underivatized TAG analysis, the TAG binning method does have the ability to achieve molecular level resolution on other bulk aerosol components commonly observed by the AMS

Are coastal habitats important nurseries? A meta-analysis

Nearshore‐structured habitats—including underwater grasses, mangroves, coral, and other biogenic reefs, marshes, and complex abiotic substrates—have long been postulated to function as important nurseries for juvenile fishes and invertebrates. Here, we review the evolution of the “nursery habitat hypothesis” and use \u3e11,000 comparisons from 160 peer‐reviewed studies to test whether and which structured habitats increase juvenile density, growth, and survival. In general, almost all structured habitats significantly enhanced juvenile density—and in some cases growth and survival—relative to unstructured habitats. Underwater grasses and mangroves also promoted juvenile density and growth beyond what was observed in other structured habitats. These conclusions were robust to variation among studies, although there were significant differences with latitude and among some phyla. Our results confirm the basic nursery function of certain structured habitats, which lends further support to their conservation, restoration, and management at a time when our coastal environments are becoming increasingly impacted. They also reveal a dearth of evidence from many other systems (e.g., kelp forests) and for responses other than density. Although recent studies have advocated for increasingly complex approaches to evaluating nurseries, we recommend a renewed emphasis on more straightforward assessments of juvenile growth, survival, reproduction, and recruitment

Prognostic Value of Stress Myocardial Perfusion Positron Emission Tomography: Results From A Multicenter Observational Registry

ObjectivesThe primary objective of this multicenter registry was to study the prognostic value of positron emission tomography (PET) myocardial perfusion imaging (MPI) and the improved classification of risk in a large cohort of patients with suspected or known coronary artery disease (CAD).BackgroundLimited prognostic data are available for MPI with PET.MethodsA total of 7,061 patients from 4 centers underwent a clinically indicated rest/stress rubidium-82 PET MPI, with a median follow-up of 2.2 years. The primary outcome of this study was cardiac death (n = 169), and the secondary outcome was all-cause death (n = 570). Net reclassification improvement (NRI) and integrated discrimination analyses were performed.ResultsRisk-adjusted hazard of cardiac death increased with each 10% myocardium abnormal with mildly, moderately, or severely abnormal stress PET (hazard ratio [HR]: 2.3 [95% CI: 1.4 to 3.8; p = 0.001], HR: 4.2 [95% CI: 2.3 to 7.5; p < 0.001], and HR: 4.9 [95% CI: 2.5 to 9.6; p < 0.0001], respectively [normal MPI: referent]). Addition of percent myocardium ischemic and percent myocardium scarred to clinical information (age, female sex, body mass index, history of hypertension, diabetes, dyslipidemia, smoking, angina, beta-blocker use, prior revascularization, and resting heart rate) improved the model performance (C-statistic 0.805 [95% CI: 0.772 to 0.838] to 0.839 [95% CI: 0.809 to 0.869]) and risk reclassification for cardiac death (NRI 0.116 [95% CI: 0.021 to 0.210]), with smaller improvements in risk assessment for all-cause death.ConclusionsIn patients with known or suspected CAD, the extent and severity of ischemia and scar on PET MPI provided powerful and incremental risk estimates of cardiac death and all-cause death compared with traditional coronary risk factors

A Clinical Tool to Identify Candidates for Stress-First Myocardial Perfusion Imaging

Objectives: This study sought to develop a clinical model that identifies a lower-risk population for coronary artery disease that could benefit from stress-first myocardial perfusion imaging (MPI) protocols and that can be used at point of care to risk stratify patients. Background: There is an increasing interest in stress-first and stress-only imaging to reduce patient radiation exposure and improve patient workflow and experience. Methods: A secondary analysis was conducted on a single-center cohort of patients undergoing single-photon emission computed tomography (SPECT) and positron emission tomography (PET) studies. Normal MPI was defined by the absence of perfusion abnormalities and other ischemic markers and the presence of normal left ventricular wall motion and left ventricular ejection fraction. A model was derived using a cohort of 18,389 consecutive patients who underwent SPECT and was validated in a separate cohort of patients who underwent SPECT (n = 5,819), 1 internal cohort of patients who underwent PET (n=4,631), and 1 external PET cohort (n = 7,028). Results: Final models were made for men and women and consisted of 9 variables including age, smoking, hypertension, diabetes, dyslipidemia, typical angina, prior percutaneous coronary intervention, prior coronary artery bypass graft, and prior myocardial infarction. Patients with a score ≤1 were stratified as low risk. The model was robust with areas under the curve of 0.684 (95% confidence interval [CI]: 0.674 to 0.694) and 0.681 (95% CI: 0.666 to 0.696) in the derivation cohort, 0.745 (95% CI: 0.728 to 0.762) and 0.701 (95% CI: 0.673 to 0.728) in the SPECT validation cohort, 0.672 (95% CI: 0.649 to 0.696) and 0.686 (95% CI: 0.663 to 0.710) in the internal PET validation cohort, and 0.756 (95% CI: 0.740 to 0.772) and 0.737 (95% CI: 0.716 to 0.757) in the external PET validation cohort in men and women, respectively. Men and women who scored ≤1 had negative likelihood ratios of 0.48 and 0.52, respectively. Conclusions: A novel model, based on easily obtained clinical variables, is proposed to identify patients with low probability of having abnormal MPI results. This point-of-care tool may be used to identify a population that might qualify for stress-first MPI protocols