Improving Loss Estimation for Woodframe Buildings. Volume 2: Appendices

This report documents Tasks 4.1 and 4.5 of the CUREE-Caltech Woodframe Project. It presents a theoretical and empirical methodology for creating probabilistic relationships between seismic shaking severity and physical damage and loss for buildings in general, and for woodframe buildings in particular. The methodology, called assembly-based vulnerability (ABV), is illustrated for 19 specific woodframe buildings of varying ages, sizes, configuration, quality of construction, and retrofit and redesign conditions. The study employs variations on four basic floorplans, called index buildings. These include a small house and a large house, a townhouse and an apartment building. The resulting seismic vulnerability functions give the probability distribution of repair cost as a function of instrumental ground-motion severity. These vulnerability functions are useful by themselves, and are also transformed to seismic fragility functions compatible with the HAZUS software. The methods and data employed here use well-accepted structural engineering techniques, laboratory test data and computer programs produced by Element 1 of the CUREE-Caltech Woodframe Project, other recently published research, and standard construction cost-estimating methods. While based on such well established principles, this report represents a substantially new contribution to the field of earthquake loss estimation. Its methodology is notable in that it calculates detailed structural response using nonlinear time-history structural analysis as opposed to the simplifying assumptions required by nonlinear pushover methods. It models physical damage at the level of individual building assemblies such as individual windows, segments of wall, etc., for which detailed laboratory testing is available, as opposed to two or three broad component categories that cannot be directly tested. And it explicitly models uncertainty in ground motion, structural response, component damageability, and contractor costs. Consequently, a very detailed, verifiable, probabilistic picture of physical performance and repair cost is produced, capable of informing a variety of decisions regarding seismic retrofit, code development, code enforcement, performance-based design for above-code applications, and insurance practices

Real-time earthquake hazard assessment in California; the early post-earthquake damage assessment tool and the Caltech-USGS broadcast of earthquakes

A real-time earthquake monitoring system which provides source parameters to user groups through a commercial paging service is now in place in California. A GIS-based system to predict and display near real-time damage and casualty estimates is currently being developed by EQE International under contract with the State of California. These new technologies offer immediate tangible benefits to state and local governments, utilities, lifelines and corporations with facilities or operations at risk. This paper will outline the development of these new technologies, identify the contributions they will make to emergency management and explore some directions these innovative systems may take in the future

Optimal MRI sequences for 68Ga-PSMA-11 PET/MRI in evaluation of biochemically recurrent prostate cancer.

BackgroundPET/MRI can be used for the detection of disease in biochemical recurrence (BCR) patients imaged with 68Ga-PSMA-11 PET. This study was designed to determine the optimal MRI sequences to localize positive findings on 68Ga-PSMA-11 PET of patients with BCR after definitive therapy. Fifty-five consecutive prostate cancer patients with BCR imaged with 68Ga-PSMA-11 3.0T PET/MRI were retrospectively analyzed. Mean PSA was 7.9 ± 12.9 ng/ml, and mean PSA doubling time was 7.1 ± 6.6 months. Detection rates of anatomic correlates for prostate-specific membrane antigen (PSMA)-positive foci were evaluated on small field of view (FOV) T2, T1 post-contrast, and diffusion-weighted images. For prostate bed recurrences, the detection rate of dynamic contrast-enhanced (DCE) imaging for PSMA-positive foci was evaluated. Finally, the detection sensitivity for PSMA-avid foci on 3- and 8-min PET acquisitions was compared.ResultsPSMA-positive foci were detected in 89.1% (49/55) of patients evaluated. Small FOV T2 performed best for lymph nodes and detected correlates for all PSMA-avid lymph nodes. DCE imaging performed the best for suspected prostate bed recurrence, detecting correlates for 87.5% (14/16) of PSMA-positive prostate bed foci. The 8-min PET acquisition performed better than the 3-min acquisition for lymph nodes smaller than 1 cm, detecting 100% (57/57) of lymph nodes less than 1 cm, compared to 78.9% (45/57) for the 3-min acquisition.ConclusionPSMA PET/MRI performed well for the detection of sites of suspected recurrent disease in patients with BCR. Of the MRI sequences obtained for localization, small FOV T2 images detected the greatest proportion of PSMA-positive abdominopelvic lymph nodes and DCE imaging detected the greatest proportion of PSMA-positive prostate bed foci. The 8-min PET acquisition was superior to the 3 min acquisition for detection of small lymph nodes

Use of Laparoscopy in Trauma at a Level II Trauma Center

Although laparoscopy is little used in trauma, it may have a significant role in a select subset of patients

Chest CT Imaging Signature of Coronavirus Disease 2019 Infection In Pursuit of the Scientific Evidence:in pursuit of the scientific evidence

BACKGROUND: Chest CT may be used for the diagnosis of coronavirus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19.RESEARCH QUESTION: What is the chest CT imaging signature of COVID-19 infection?STUDY DESIGN AND METHODS: A systematic literature search was performed for original studies on chest CT imaging findings in patients with COVID-19. Methodologic quality of studies was evaluated. Pooled prevalence of chest CT imaging findings were calculated with the use of a random effects model in case of between-study heterogeneity (predefined as I-2 =50); otherwise, a fixed effects model was used.RESULTS: Twenty-eight studies were included. The median number of patients with COVID-19 per study was 124 (range, 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range, &gt;76.3%-100%). Twenty-seven of the studies (96%) had a retrospective design. Methodologic quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (eight studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT imaging findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT imaging findings ranged between 10.5% and 63.2%.INTERPRETATION: Studies on chest CT imaging findings in COVID-19 suffer from methodologic quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT imaging findings appear to be suggestive of COVID-19, but normal chest CT imaging findings do not exclude COVID-19, not even in symptomatic patients.</p

Systematic Review and Meta-Analysis on the Value of Chest CT in the Diagnosis of Coronavirus Disease (COVID-19):Sol Scientiae, Illustra Nos

OBJECTIVE. The purpose of this article is to systematically review and meta-analyze the diagnostic accuracy of chest CT in detecting coronavirus disease (COVID-19). MATERIALS AND METHODS. MEDLINE was systematically searched for publications on the diagnostic performance of chest CT in detecting COVID-19. Methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Meta-analysis was performed using a bivariate random-effects model. RESULTS. Six studies were included, comprising 1431 patients. All six studies included patients at high risk of COVID-19, and five studies explicitly reported that they included only symptomatic patients. Mean prevalence of COVID-19 was 47.9% (range, 27.6–85.4%). High or potential risk of bias was present throughout all QUADAS-2 domains in all six studies. Sensitivity ranged from 92.9% to 97.0%, and specificity ranged from 25.0% to 71.9%, with pooled estimates of 94.6% (95% CI, 91.9–96.4%) and 46.0% (95% CI, 31.9–60.7%), respectively. The included studies were statistically homogeneous in their estimates of sensitivity (p = 0.578) and statistically heterogeneous in their estimates of specificity (p < 0.001). CONCLUSION. Diagnostic accuracy studies on chest CT in COVID-19 suffer from methodologic quality issues. Chest CT appears to have a relatively high sensitivity in symptomatic patients at high risk of COVID-19, but it cannot exclude COVID-19. Specificity is poor. These data, along with other local factors such as COVID-19 prevalence, available real-time reverse transcriptase–polymerase chain reaction tests, staff, hospital, and CT scanning capacity, can be useful to healthcare professionals and policy makers to decide on the utility of chest CT for COVID-19 detection in the hospital setting

Sequential mechanisms underlying concentration invariance in biological olfaction

Concentration invariance—the capacity to recognize a given odorant (analyte) across a range of concentrations—is an unusually difficult problem in the olfactory modality. Nevertheless, humans and other animals are able to recognize known odors across substantial concentration ranges, and this concentration invariance is a highly desirable property for artificial systems as well. Several properties of olfactory systems have been proposed to contribute to concentration invariance, but none of these alone can plausibly achieve full concentration invariance. We here propose that the mammalian olfactory system uses at least six computational mechanisms in series to reduce the concentration-dependent variance in odor representations to a level at which different concentrations of odors evoke reasonably similar representations, while preserving variance arising from differences in odor quality. We suggest that the residual variance then is treated like any other source of stimulus variance, and categorized appropriately into “odors” via perceptual learning. We further show that naïve mice respond to different concentrations of an odorant just as if they were differences in quality, suggesting that, prior to odor categorization, the learning-independent compensatory mechanisms are limited in their capacity to achieve concentration invariance