University of San Francisco
  • San Francisco, CA, United States
Recent publications
Background: The coronavirus pandemic continues to shake the embedded structures of traditional in-person education across all learning levels and across the globe. In healthcare simulation, the pandemic tested the innovative and technological capabilities of simulation programs, educators, operations staff, and administration. This study aimed to answer the question: What is the state of distance simulation practice in 2021? Methods: This was an IRB-approved, 34-item open survey for any profession involved in healthcare simulation disseminated widely and internationally in seven languages from January 14, 2021, to March 3, 2021. Development followed a multistep process of expert design, testing, piloting, translation, and recruitment. The survey asked questions to understand: Who was using distance simulation? What driving factors motivated programs to initiate distance sim? For what purposes was distance sim being used? What specific types or modalities of distance simulation were occurring? How was it being used (i.e., modalities, blending of technology and resources and location)? How did the early part of the pandemic differ from the latter half of 2020 and early 2021? What information would best support future distance simulation education? Data were cleaned, compiled, and analyzed for dichotomized responses, reporting frequencies, proportions, as well as a comparison of response proportions. Results: From 32 countries, 618 respondents were included in the analysis. The findings included insights into the prevalence of distance simulation before, during, and after the pandemic; drivers for using distance simulation; methods and modalities of distance simulation; and staff training. The majority of respondents (70%) reported that their simulation center was conducting distance simulation. Significantly more respondents indicated long-term plans for maintaining a hybrid format (82%), relative to going back to in-person simulation (11%, p < 0.001). Conclusion: This study gives a perspective into the rapid adaptation of the healthcare simulation community towards distance teaching and learning in reaction to a radical and quick change in education conditions and environment caused by COVID-19, as well as future directions to pursue understanding and support of distance simulation.
Background Transthyretin amyloidosis (ATTR amyloidosis) is a rare, life-threatening disease caused by the accumulation of variant or wild-type (ATTRwt amyloidosis) transthyretin amyloid fibrils in the heart, peripheral nerves, and other tissues and organs. Methods Established in 2007, the Transthyretin Amyloidosis Outcomes Survey (THAOS) is the largest ongoing, global, longitudinal observational study of patients with ATTR amyloidosis, including both inherited and wild-type disease, and asymptomatic carriers of pathogenic TTR mutations. This descriptive analysis examines baseline characteristics of symptomatic patients and asymptomatic gene carriers enrolled in THAOS since its inception in 2007 (data cutoff: August 1, 2021). Results This analysis included 3779 symptomatic patients and 1830 asymptomatic gene carriers. Symptomatic patients were predominantly male (71.4%) and had a mean (standard deviation [SD]) age of symptom onset of 56.3 (17.8) years. Val30Met was the most common genotype in symptomatic patients in South America (80.9%), Europe (55.4%), and Asia (50.5%), and more patients had early- versus late-onset disease in these regions. The majority of symptomatic patients in North America (58.8%) had ATTRwt amyloidosis. The overall distribution of phenotypes in symptomatic patients was predominantly cardiac (40.7%), predominantly neurologic (40.1%), mixed (16.6%), and no phenotype (2.5%). In asymptomatic gene carriers, mean (SD) age at enrollment was 42.4 (15.7) years, 42.4% were male, and 73.2% carried the Val30Met mutation. Conclusions This 14-year global overview of THAOS in over 5000 patients represents the largest analysis of ATTR amyloidosis to date and highlights the genotypic and phenotypic heterogeneity of the disease. Identifier : NCT00628745.
Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.
In recent decades, mindfulness-based interventions have been shown to be a very effective and economical approach in treating psychological disorders, with the literature from studies in the Western world, . On the other hand, mindfulness interventions in the Philippine context are just beginning, with limited studies published regarding its efficacy. This study is one of the first exploratory investigations on the potential of mindfulness-based strategies for young Filipino sample. Findings suggest that an adapted mindfulness based cognitive therapy approach on a college population can bring changes in the areas of stress, depression, anxiety, and over-all psychological well-being. This study suggests that mindfulness interventions might be a cross-culturally effective approach and leads the way for more studies within the Filipino context.
Raising environmental awareness and product development are two separate and costly investments that many small and medium-sized fashion businesses cannot afford to achieve sustainability. Therefore, there is a need to determine which factors exert a more significant impact on consumer loyalty and purchase intention toward eco-friendly fashions. Thus, this study employs a mixed-methods approach with thematic analysis and the SEM-PLS technique to research how Vietnamese Gen Z’s perceptions of product-service quality, environmental awareness, and pro-environmental behavior influence their purchase intention and loyalty toward eco-friendly fashion products. Most interviewees acknowledged that they primarily gained knowledge about eco-friendly fashion through social media platforms. The qualitative results further showed that their knowledge of and attitudes toward eco-friendly fashion practices were insufficient to convince young customers to afford eco-friendly fashion products. The SEM-PLS results of 313 participants show that while customers’ perceived behavioral control plays a more significant role in stimulating purchase intention, only product-service quality factors impact loyalty. Hence, this study suggests that businesses should prioritize improving service and product quality rather than funding green marketing when targeting Vietnamese Gen Z in case of financial constraints. Government should prioritize financial and technological support for fashion firms to develop high-quality eco-friendly fashion to ensure the product availability.
Background Terminology to describe extent of resection in glioblastoma is inconsistent across clinical trials. A surgical classification system was previously proposed based upon residual contrast-enhancing (CE) tumor. We aimed to (I) explore the prognostic utility of the classification system and (II) define how much removed non-CE tumor translates into a survival benefit. Methods The international RANO resect group retrospectively searched previously compiled databases from seven neuro-oncological centers in the USA and Europe for patients with newly diagnosed glioblastoma per WHO 2021 classification. Clinical and volumetric information from pre- and post-operative MRI were collected. Results We collected 1008 patients with newly diagnosed IDHwt glioblastoma. 744 IDHwt glioblastomas were treated with radiochemotherapy per EORTC 26981/22981 (TMZ/RT→TMZ) following surgery. Among these homogenously treated patients, lower absolute residual tumor volumes (in cm 3) were favorably associated with outcome: patients with ‘maximal CE resection’ (class 2) had superior outcome compared to patients with ‘submaximal CE resection’ (class 3) or ‘biopsy’ (class 4). Extensive resection of non-CE tumor (≤5 cm 3 residual non-CE tumor) was associated with better survival among patients with complete CE resection, thus defining class 1 (‘supramaximal CE resection’). The prognostic value of the resection classes was retained on multivariate analysis when adjusting for molecular and clinical markers. Conclusions The proposed “RANO categories for extent of resection in glioblastoma” are highly prognostic and may serve for stratification within clinical trials. Removal of non-CE tumor beyond the CE tumor borders may translate into additional survival benefit, providing a rationale to explicitly denominate such ‘supramaximal CE resection’.
A walking gait has been identified in a range of vertebrate species with different body plans, habitats, and life histories. With increased application of this broad umbrella term, it has become necessary to assess the physical characteristics, analytical approaches, definitions, and diction used to describe walks. To do this, we reviewed studies of slow speed locomotion across a range of vertebrates to refine the parameters used to define walking, evaluate analytical techniques, and propose approaches to maximize consistency across subdisciplines. We summarize nine key parameters used to characterize walking behaviors in mammals, birds, reptiles, amphibians, and fishes. After identifying consistent patterns across groups, we propose a comprehensive definition for a walking gait. A walk is a form of locomotion where the majority of the forward propulsion of the animal comes from forces generated by the appendages interacting with the ground. During a walk, an appendage must be out of phase with the opposing limb in the same girdle and there is always at least one limb acting as ground-support (no suspension phase). Additionally, walking occurs at dimensionless speeds <1 v* and the duty factor of the limbs is always >0.5. Relative to other gaits used by the same species, the stance duration of a walk is long, the cycle frequency is low, and the cycle distance is small. Unfortunately, some of these biomechanical parameters, while effectively describing walks, may also characterize other, non-walking gaits. Inconsistent methodology likely contributes to difficulties in comparing data across many groups of animals; consistent application of data collection and analytical techniques in research methodology can improve these comparisons. Finally, we note that the kinetics of quadrupedal movements are still poorly understood and much work remains to be done to understand the movements of small, exothermic tetrapods.
Automated quantification of data acquired as part of an MRI exam requires identification of the specific acquisition of relevance to a particular analysis. This motivates the development of methods capable of reliably classifying MRI acquisitions according to their nominal contrast type, e.g., T1 weighted, T1 post-contrast, T2 weighted, T2-weighted FLAIR, proton-density weighted. Prior studies have investigated using imaging-based methods and DICOM metadata-based methods with success on cohorts of patients acquired as part of a clinical trial. This study compares the performance of these methods on heterogeneous clinical datasets acquired with many different scanners from many institutions. RF and CNN models were trained on metadata and pixel data, respectively. A combined RF model incorporated CNN logits from the pixel-based model together with metadata. Four cohorts were used for model development and evaluation: MS research (n = 11,106 series), MS clinical (n = 3244 series), glioma research (n = 612 series, test/validation only), and ADNI PTSD (n = 477 series, training only). Together, these cohorts represent a broad range of acquisition contexts (scanners, sequences, institutions) and subject pathologies. Pixel-based CNN and combined models achieved accuracies between 97 and 98% on the clinical MS cohort. Validation/test accuracies with the glioma cohort were 99.7% (metadata only) and 98.4 (CNN). Accurate and generalizable classification of MRI acquisition contrast types was demonstrated. Such methods are important for enabling automated data selection in high-throughput and big-data image analysis applications.
Background Research on the influences on bike share use and potential favorable relationships between use and obesity is limited, particularly in the U.S. context. Therefore, the aims of this exploratory study were to examine correlates of awareness and use of Boston’s Bluebikes bike share system and assess the association between use and weight status. Methods Students, faculty, and staff (n = 256) at a public urban university completed an online survey that assessed sociodemographic, behavioral, and physical activity characteristics, Bluebikes awareness, and use of Bluebikes and personal bikes. Multivariable logistic regression models were estimated to examine associations between sociodemographic and behavioral factors and bike share awareness and use; and between use and overweight/obesity status. Results Respondents were mostly students (72.2%), female (69.1%), White (62.1%), and the mean age was 32.4±13.8 years. The percentage of respondents classified as aware of Bluebikes was 33.6% with only 12.9% reporting any use of the system. Living in a community where bike share stations were located (odds ratio (OR) = 2.01, 95% confidence interval (CI): 1.10, 3.67), personal bike ownership (OR = 2.27, 95% CI:1.27, 4.45), and not exclusively commuting to campus via car (OR = 3.19, 95% CI:1.63, 6.22) had significant positive associations with awareness. Living in a bike share community (OR = 2.34; 95% CI:1.04, 5.27) and personal bike ownership (OR = 3.09; 95% CI:1.27, 7.52) were positively associated with bike share use. Any reported use of Bluebikes was associated with 60% lower odds of being overweight/obese (OR = 0.40; 95% CI:0.17, 0.93). Conclusions Several environmental and behavioral variables, including access to stations and personal bicycle ownership, were significantly associated with Bluebikes awareness and use. Findings also suggest a potential benefit to bike share users in terms of maintaining a healthy weight, though further longitudinal studies are needed to rule out the possibility that more active and leaner individuals tend to use bike share more frequently.
Over the last 30 years, the optical property community has shifted from conducting dissolved organic matter (DOM) measurements on new complex mixtures in natural and engineered systems to furthering ecosystem understanding in the context of past, present, and future carbon (C) cycling regimes. However, the appropriate use of optical properties to understand DOM behavior in complex biogeochemical systems is of recent debate. This critical review provides an extensive survey of DOM optical property literature across atmospheric, marine, and terrestrial biospheres using a categorical approach that probes each biosphere and its subdivisions. Using this approach, a rubric of ecosystem variables, such as productive nature, C cycling rate, C inputs, and water quality, sets the foundation for interpreting commonly used optical property DOM metrics such as fluorescence index (FI), humification index (HIX), and specific ultraviolet absorbance at 254 nm (SUVA254). Case studies and a meta-analysis of each biosphere and subdivision found substantial overlap and characteristic distributions corresponding to ecosystem context for FI, HIX, and SUVA254, signifying chromophores and fluorophores from different ecosystems may be more similar than originally thought. This review challenges researchers to consider ecosystem connectivity when applying optical property results rather than making traditional "if this, then that" results-style conclusions.
Purpose Current guidelines for patients with HER2+ breast cancer brain metastases (BCBrM) diverge based on the status of extracranial disease (ECD). An in-depth understanding of the impact of ECD on outcomes in HER2+ BCBrM has never been performed. Our study explores the implications of ECD status on intracranial progression-free survival (iPFS) and overall survival (OS) after first incidence of HER2+ BCBrM and radiation. Methods A retrospective analysis was performed of 153 patients diagnosed with initial HER2+ BCBrM who received radiation therapy to the central nervous system (CNS) at Duke between 2008 and 2020. The primary endpoint was iPFS defined as time from first CNS radiation treatment to intracranial progression or death. OS was defined as time from first CNS radiation or first metastatic disease to death. Systemic staging scans within 30 days of initial BCBrM defined ECD status. Results In this cohort, >70% of patients had controlled ECD with either isolated intracranial relapse (27%) or stable/responding ECD (44%). OS from initial metastatic disease to death was markedly worse for patients with isolated intracranial relapse (median=28.4m) compared to those with progressive or stable/responding ECD (48.8m and 68.1m, respectively, p=0.0035). OS from first CNS radiation to death was significantly worse for patients with progressive ECD (17.8m) versus stable/responding (36.6m) or isolated intracranial relapse (28.4m, p=0.008). iPFS did not differ statistically. Conclusion OS in patients with HER2+ isolated BCBrM was inferior to those with concurrent progressive or stable/responding ECD. Studies investigating initiation of brain penetrable HER2-targeted therapies earlier in the disease course of isolated HER2+ intracranial relapse patients are warranted.
In order to explore global biennials of contemporary art, this study provides a geospatial analysis of eleven global biennials to examine where artists are drawn from in these international exhibitions. The project aims to cut across a broad scope of biennials held in multiple regions to examine how artists are circulating in the contemporary world, where they are showing and, most importantly, how biennials are defining international contemporary art in the era of globalization. By mapping a series of biennials held around the globe over several iterations in the 2010s, this study provides unprecedented evidence of the geography of biennial selection among major exhibitions, how this has changed over time and whether patterns emerge for participation in global art world events. More than half of these biennials are held in countries that are in the Global South; this means that most of these locations are emerging art centers responding to new economic patterns under globalization. The use of maps to show the geographic distribution of biennial participants will point to various, competing models of the geography of global contemporary art and will allow reflection upon how new biennials are changing the geospatial dynamics of international art exhibitions today.
Large, severe wildfires continue to burn in frequent-fire adapted forests but the mechanisms that contribute to them and their predictability are important questions. Using a combination of ground based and remotely sensed data we analyzed the behavior and patterns of the 2020 Creek Fire where drought and bark beetles had previously created substantial levels of tree mortality in the southern Sierra Nevada. We found that dead biomass and live tree densities were the most important variables predicting fire severity; high severity fire encompassed 41% of the area and the largest high severity patch (19,592 ha) comprised 13% of total area burned. Areas with the highest amounts of dead biomass and live tree densities were also positively related to high severity fire patch size indicating that larger, more homogenous conditions of this forest characteristic resulted in adverse, landscape-scale fire effects. The first two days of the Creek Fire were abnormally hot and dry but weather during the days of the greatest fire growth was largely within the normal range of variation for that time of year with one day with lower windspeeds. From September 5 to 8th the fire burned almost 50% of its entire area and fire intensity patterns inferred from remotely sensed brightness-temperature data were typical except on September 6th when heat increased towards the interior of the fire. Not only was the greatest heat concentrated away from the fire perimeter, but a significant amount of heat was still being generated within the fire perimeter from the previous day. This is a classic pattern for a mass fire and the high amount of dead biomass created from the drought and bark beetles along with high live tree densities were critical factors in developing mass fire behavior. Operational fire behavior models were not able to predict this behavior largely because they do not include post-frontal combustion and fire-atmosphere interactions. An important question regarding this mass fire is if the tree mortality event that preceded it could have been avoided or reduced or was it within the natural range of variation for these forests? We found that the mortality episode was outside of historical analogs and was exacerbated by past management decisions. The Creek Fire shows us how vulnerable of our current frequent-fire forest conditions are to suffering high tree mortality and offering fuel conditions capable of generating mass fires from which future forest recovery is questionable because of type conversion and probable reoccurring high severity fire.
Chikungunya virus (CHIKV) is a representative alphavirus causing debilitating arthritogenic disease in humans. Alphavirus particles assemble into two icosahedral layers: the glycoprotein spike shell embedded in a lipid envelope and the inner nucleocapsid (NC) core. In contrast to matrix-driven assembly of some enveloped viruses, the assembly/budding process of two-layered icosahedral particles remains poorly understood. Here we used cryogenic electron tomography (cryo-ET) to capture snapshots of the CHIKV assembly in infected human cells. Subvolume classification of the snapshots revealed 12 intermediates representing different stages of assembly at the plasma membrane. Further subtomogram average structures ranging from subnanometre to nanometre resolutions show that immature non-icosahedral NCs function as rough scaffolds to trigger icosahedral assembly of the spike lattice, which in turn progressively transforms the underlying NCs into icosahedral cores during budding. Further, analysis of CHIKV-infected cells treated with budding-inhibiting antibodies revealed wider spaces between spikes than in icosahedral spike lattice, suggesting that spacing spikes apart to prevent their lateral interactions prevents the plasma membrane from bending around the NC, thus blocking virus budding. These findings provide the molecular mechanisms for alphavirus assembly and antibody-mediated budding inhibition that provide valuable insights for the development of broad therapeutics targeting the assembly of icosahedral enveloped viruses. Cryogenic electron tomography analysis of Chikungunya virus particle assembly reveals 12 intermediate structural stages during virus assembly/budding at the plasma membrane and shows that non-icosahedral nucleocapsid proteins serve as scaffold to induce icosahedral assembly of the glycoprotein spike lattice. Structural analysis also shows that budding-inhibiting antibodies act by interfering with lateral spike interactions.
It is important to assess existing reinforced concrete (RC) structures with damages. This task, however, is traditionally empirical and time‐consuming. To improve assessment accuracy and efficiency, this study proposed an approach to semantic model damaged RC beams based on point clouds. A slice‐based method for automatically modeling deformed beams and a color‐based crack detection method were developed for generating building information modeling (BIM) and finite element (FE) models. Furthermore, to enhance structural assessment and decision‐making by providing both damage and loading performance analysis data, a framework to extend the existing IFC standard was proposed for interoperability issues between FE models and BIM, aiming to integrate semantic‐enrichment damages and FE analysis results in the as‐is BIM model. Experiments were carried out on a simply supported RC beam subjected to a concentrated load. As a result, the improvement of the developed as‐is BIM model for damage visualization and structural assessment was confirmed.
We apply numerical algebraic geometry to the invariant-theoretic problem of detecting symmetries between two plane algebraic curves. We describe an efficient equality test which determines, with “probability-one”, whether or not two rational maps have the same image up to Zariski closure. The application to invariant theory is based on the construction of suitable signature maps associated to a group acting linearly on the respective curves. We consider two versions of this construction: differential and joint signature maps. In our examples and computational experiments, we focus on the complex Euclidean group, and introduce an algebraic joint signature that we prove determines equivalence of curves under this action and the size of a curve's symmetry group. We demonstrate that the test is efficient and use it to empirically compare the sensitivity of differential and joint signatures to different types of noise.
Autonomous vehicle (AV) technology can help disabled Americans achieve their desired level of mobility. However, realizing this potential depends on vehicle manufacturers, policymakers, and state and municipal agencies collaborating to accommodate the needs of disabled individuals at different stages of trip making through information system design, vehicle design, and infrastructure design. Integrating accessibility at this stage of the AV revolution would finally allow us an opportunity to develop a transportation system that treats accessibility as a guiding principle, not as an afterthought. This paper documents accessibility considerations for disabled individuals followed by a review of relevant Americans with Disabilities Act (ADA) regulations. The review of regulations is followed by a review of nine case studies, five corresponding to the on-demand microtransit service model and four corresponding to the paratransit service model. These case studies are essentially different prototypes currently being deployed on a pilot basis. Each of these specific case studies is then evaluated for its ability to provide potential accessibility features that would fulfill the requirement set forth by relevant ADA regulations in the absence of an operator/driver. Based on this review of relevant research, ADA regulations, and case studies, recommendations are provided for researchers, private firms, policymakers, and agencies involved in AV development and deployment. The recommendations include better collaboration and adoption of best practices to address the needs of individuals with different disability types (e.g., Cognitive, Visual, Auditory). ADA regulations should be used as one of the tools in addition to universal design principles and assistive technologies in order to maximize accessibility.
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3,888 members
Sonja Martin Poole
  • School of Management
Naupaka Bruce Zimmerman
  • Department of Biology
Indre V Viskontas
  • Department of Psychology
Shirley Mcguire
  • Department of Psychology
Xiaosheng Huang
  • Department of Physics and Astronomy
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