Search

Architectural floor plans are tangible cultural history artifacts, valuable for documenting how people live, work, and recreate, both in the present and going back hundreds or even thousands of years. Today, floor plans are created using digital technologies, but prior to the 1990s, the majority were drafted by hand. For archives, the storage, conservation, and handling of hand-drawn floor plans, which are often oversized and fragile, can be challenging. The digitization of floor plans, therefore, can reduce the possibility of damage while providing greater accessibility. However, even with digitization, researchers and archives continue to encounter barriers utilizing and cataloging these collections. Manually processing, collecting, and analyzing information contained in floor plans is time-consuming and prone to human error, particularly when working with large floor plan corpora. A tool to facilitate the broader use of archival floor plans is needed. Here, we propose the Building Database and Analytics System (BuDAS), a tool for the detection, storage, and analysis of floor plan information to assist scholars' research on collections and support metadata capture. In this article, we explore the challenges of detecting floor plan information and provide an overview of BuDAS, which we developed to address these problems. In addition, we test BuDAS's room detection system on two groups of floor plans sourced from archives, representing different levels of detection difficulty. The limitations and future implications of BuDAS and floor plan detection will also be discussed.

Background Development of robust vaccination guidelines against SARS-CoV-2 requires an understanding of the longitudinal antibody (Ab) response to vaccination and interactions with natural infection. Here, we leveraged an observational cohort study of healthcare personnel (HCP) to study the impact of prior SARS-CoV-2 infection on Ab binding and neutralization after mRNA-based vaccination over a 13 month period. Methods From July 2020 to February 2022, HCP at an academic medical center provided blood samples biweekly for 12 weeks and monthly thereafter. First and second vaccine doses became available in mid-December 2021 and boosters were available starting in October 2021. Individuals were excluded if they did not provide any samples, if baseline serostatus was unknown, and if they received a monoclonal Ab treatment for COVID. ELISA measured total immunoglobulin (Ig) and IgG binding to SARS-CoV-2 RBD. Neutralization was measured by live virus Nanoluc SARS-CoV-2ic assay. Demographics, serostatus, and vaccinations for the total study population and the sub-sample of participants with pre- and post-vaccination antibody measurements. Results Of 213 participants, 192 met inclusion criteria. A majority had detectable IgG levels 8 months after a second dose. Prior to vaccination, median total Ig was higher among seropositive vs. seronegative participants (3.7 vs 1.0, p< 0.001). After a first dose, the median total Ig response was two-fold higher in seropositive compared to seronegative participants (13.8 vs. 7.0, p=0.009). A similar pattern was noted with IgG binding and neutralization. After the second dose, median IgG increased to similar levels in both seropositive and seronegative participants (22.1 vs. 21.2, p=0.8). Neutralization after the second dose was slightly higher in seropositive vs. seronegative participants (log10 3.1 vs. 2.5, p=0.075). Durability of IgG responses after second dose of mRNA-based vaccination against SARS-CoV-2 IgG P/N measurements after 5 days post-V2 for the entire study cohort (incident seropositive: yellow circles, prevalent seropositive (red circles), seronegative (open circles) are shown. The solid lines represent Loess curves for incident and prevalent seropositive participants combined (orange line) and those who were seronegative (grey line). SARS-CoV-2-specific total Ig and IgG subtype responses among healthcare personnel before and after vaccination against SARS-CoV-2 with an mRNA-based vaccine. Total Ig P/N ratios at pre-vaccine, post-V1, post-vaccine 2 (post-V2), and post-booster dose (post-boost) timepoints by serostatus (seronegative: n(-), seropositive: n(+)) are shown in the left panel. For the pre-vaccine time point, the most recent antibody level prior to vaccination (for those who were vaccinated) or most recent antibody level overall (for those who were not vaccinated) is shown. For the post-vaccine time points, the first measurement after 5 days post-vaccination is included. Individuals who were infected with SARS-CoV-2 at any time after the first vaccine dose are shown as open circles with black outlines. The black numbers next to the circles indicate the number of days between vaccination and sample collection for seropositive individuals. SARS-CoV-2 specific IgG P/N ratios respectively at pre-vaccine, post-V1, post-V2, and post-boost timepoints by serostatus (seronegative: n(-), seropositive: n(+)) are shown in the right panel. One individual tested positive for SARS-CoV-2 by PCR shortly after the second vaccine dose (V2); post-V2 results were excluded for this participant. For the pre-vaccine time point, the most recent antibody level prior to vaccination (for those who were vaccinated) or most recent antibody level overall (for those who were not vaccinated) is shown. For the post-vaccine time points, the first measurement after 5 days post-vaccination is included. The dotted line is a P/N ratio of 2.4, the cut-off associated with 99.3% specificity (SARS-CoV-2 IgG-positive above the line, IgG-negative below). Individuals who were infected with SARS-CoV-2 at any time after the first vaccine dose are shown as open circles with black outlines. The black numbers next to the circles indicate the number of days between vaccination and sample collection for seropositive individuals. SARS-CoV-2 D614G live virus neutralization among healthcare personnel by serostatus prior to vaccination. Example neutralization curves are shown in Panel A. Panel B shows the SARS-CoV-2 D614G live virus neutralization titers displayed as EC50 for seropositive (prevalent and incident) individuals and a subset of seronegative individuals. Samples for seronegative individuals were selected by matching on age and time between vaccination and sample collection to the samples from seropositive individuals. Conclusion Antibody responses after SARS-CoV-2 vaccination persist up to 1 year with wide individual variability. Though prior infection was associated with greater Ab responses after a first dose, it did not significantly modify responses after second and third doses. Still, we observed overall slightly higher Ab levels among individuals that had a prior infection before any one of the 3 doses of vaccine. These results suggest that immunity against SARS-CoV-2 prior to vaccination has a role in initial response but does not significantly modify circulating Ab titers after multiple doses of vaccination. Disclosures All Authors: No reported disclosures.

Floods and droughts are key driving forces shaping aquatic ecosystems. Climate change may alter key attributes of these events and consequently health and distribution of aquatic species. Improved knowledge of biological responses to different types of floods and droughts in rivers should allow the better prediction of the ecological consequences of climate change‐induced flow alterations. This review highlights that in unmodified ecosystems, the intensity and direction of biological impacts of floods and droughts vary, but the overall consequence is an increase in biological diversity and ecosystem health. To predict the impact of climate change, metrics that allow the quantitative linking of physical disturbance attributes to the directions and intensities of biological impacts are needed. The link between habitat change and the character of biological response is provided by the frequency of occurrence of the river wave characteristic—that is the event's predictability. The severity of impacts of floods is largely related to the river wave amplitude (flood magnitude), while the impact of droughts is related to river wavelength (drought duration).

Phragmites or Common Reed (Phragmites australis) is a tall rhizomatous cosmopolitan grass. While native to Australia, it can be invasive in wetlands, forming dense monocultures and reducing their ecological integrity. We assessed the potential for the cutting of Phragmites reeds prior to planting to promote the establishment of indigenous shrubs that might ultimately outcompete Phragmites. We established ten 5 m × 5 m quadrats in an area dominated by Phragmites, brush-cut the reeds to ground level in five of them and left five uncut as controls. Within each quadrat, we planted 20 plants (~40 cm tall) of each of five indigenous shrub species, unguarded (4 plants/m2). We surveyed the plants one month after planting and annually for the following four years for survival, height and browsing damage. Browsing damage to plants was common (>50%) and unaffected by cutting. After four years, overall plant survival rates were ~25% and mean plant heights for the five shrub species ranged between 120 and 174 cm. Cutting of Phragmites had no positive effect on plant survival or height. In fact, two Melaleuca species grew taller in the uncut quadrats. Cutting of Phragmites reed beds prior to planting is unlikely to promote the establishment of woody plantings. However, planting within established Phragmites with or without prior brush-cutting is worthy of further trialling as a potential tool for reinstating native diversity at Phragmites-dominated sites.

Background Health care personnel (HCP) are at high risk for exposure to the SARS-CoV-2 virus. While personal protective equipment (PPE) may mitigate this risk, prospective data collection on its use and other risk factors for seroconversion in this population is needed. Objective The primary objectives of this study are to (1) determine the incidence of, and risk factors for, SARS-CoV-2 infection among HCP at a tertiary care medical center and (2) actively monitor PPE use, interactions between study participants via electronic sensors, secondary cases in households, and participant mental health and well-being. Methods To achieve these objectives, we designed a prospective, observational study of SARS-CoV-2 infection among HCP and their household contacts at an academic tertiary care medical center in North Carolina, USA. Enrolled HCP completed frequent surveys on symptoms and work activities and provided serum and nasal samples for SARS-CoV-2 testing every 2 weeks. Additionally, interactions between participants and their movement within the clinical environment were captured with a smartphone app and Bluetooth sensors. Finally, a subset of participants’ households was randomly selected every 2 weeks for further investigation, and enrolled households provided serum and nasal samples via at-home collection kits. Results As of December 31, 2020, 211 HCP and 53 household participants have been enrolled. Recruitment and follow-up are ongoing and expected to continue through September 2021. Conclusions Much remains to be learned regarding the risk of SARS-CoV-2 infection among HCP and their household contacts. Through the use of a multifaceted prospective study design and a well-characterized cohort, we will collect critical information regarding SARS-CoV-2 transmission risks in the health care setting and its linkage to the community. International Registered Report Identifier (IRRID) DERR1-10.2196/25410

Public health surveillance systems likely underestimate the true prevalence and incidence of SARS-CoV-2 infection due to limited access to testing and the high proportion of subclinical infections in community-based settings. This ongoing prospective, observational study aimed to generate accurate estimates of the prevalence and incidence of, and risk factors for, SARS-CoV-2 infection among residents of a central North Carolina county. From this cohort, we collected survey data and nasal swabs every two weeks and venous blood specimens every month. Nasal swabs were tested for the presence of SARS-CoV-2 virus (evidence of active infection), and serum specimens for SARS-CoV-2-specific antibodies (evidence of prior infection). As of June 23, 2021, we have enrolled a total of 153 participants from a county with an estimated 76,285 total residents. The anticipated study duration is at least 24 months, pending the evolution of the pandemic. Study data are being shared on a monthly basis with North Carolina state health authorities and future analyses aim to compare study data to state-wide metrics over time. Overall, the use of a probability-based sampling design and a well-characterized cohort will enable collection of critical data that can be used in planning and policy decisions for North Carolina and may be informative for other states with similar demographic characteristics.

Good sleep quality is important to cognition, physical health, mental well–being, and creativity—factors critical to academic and professional success. But, undergraduate students often report engaging in short, irregular, and poor–quality sleep. Anecdotal and questionnaire data suggest that poor sleep habits might be prevalent in students who are in studio– or project–based majors that implicitly encourage consecutive nights of disrupted sleep to complete projects. We investigated sleep quantity and quality using both objective measures (wristband actigraphy monitoring) and subjective measures (sleep diary) in 28 interior design undergraduate students for a 7–day period. Our primary aim was to measure sleep quantity (total sleep time) and quality (e.g., nighttime awakenings) and to compare whether undergraduate interior design students’ objective measures of sleep (actigraphy) differed from their subjective measures (sleep diary). The secondary aim was to investigate detrimental outcomes of poor sleep habits on laboratory–based measures of cognitive function (symmetry span, prospective memory, Raven's progressive matrices, remote associates task) that were administered pre– and poststudy. We found that the interior design students in our study overestimated their total sleep time by 36 minutes, that 79% of students slept for fewer than 7 hours at least three nights per week, and that many students cycled between nights of restricted/short sleep and recovery/long sleep. Importantly, students who maintained short sleep durations, highly variable night–to–night sleep durations, or had fragmented sleep (i.e., waking after sleep onset) demonstrated pre– to poststudy declines on the laboratory measure of creativity (remote associates task). These findings suggest the need for further investigations, which may lead to a broader discussion of studio culture and the role of the “all–nighter,” both in professional practice and in design education.

Sleep is critical to physical health, mental well–being, attention, and creativity. During the week of final exams, however, fewer than 10% of undergraduate students maintain the recommended average of 8 hours/night (or even the recommended minimum of 7 hours/night). For students completing multifaceted projects in studio–based majors (e.g., interior design, architecture, graphic design, studio art), anecdotal and questionnaire data suggest that the end–of–semester reduction in sleep duration may be even worse. One potential solution is to offer students an incentive to maintain healthy sleep durations. We offered interior design students, who were enrolled in a freshman–level graphics studio course, an optional extra credit incentive to maintain optimal sleep durations for five nights leading up to the due date of their final project. If participants maintained an average sleep duration of ≥8.0 hours for five nights, they would earn extra credit. By contrast, if they slept an average of 7.0–7.9 hours, there would be no grade change, and if they slept an average of ≤6.9 hours, they were instructed that they would lose points (no points were actually deducted). Of the 27 students enrolled in the course, 22 students attempted the challenge (81.5%), and we monitored their sleep duration objectively using wristband actigraphy devices. We compared their sleep duration to that of a group of 22 nonincentivized students enrolled in the same program. In the nonincentivized comparison group, very few students averaged 8 hours (9%) or even 7 hours (14%) of sleep per night. In dramatic contrast, the 8–hour challenge increased the percentage of 8– and 7–hour sleepers to 59% and 86%, respectively. Participants who took the 8–hour challenge slept an average of 98 minutes more each night than nonincentivized students and 82 minutes more than they self–reported to sleeping during the semester. The substantial increase in nightly sleep duration did not come at a cost to project performance. Individuals who opted in to the sleep challenge performed as well on the final project as students who did not opt in, and students who showed more consistent sleep (i.e., fewer nights of poor sleep followed by rebound sleep) performed better than students who showed inconsistent sleep. Thus, even during highly stressful “deadline” weeks, students can maintain healthy sleeping patterns without exacting a cost on their project performance.

We assessed how the emerging field of ecohydraulics research has changed over two decades by examining the proceedings of the biennial International Symposium on Ecohydraulics. By using Natural Language Processing (NLP) in word usage, this paper provides a deep analysis of a longitudinal dataset and enables us to test more detailed questions than previous snapshots of the ecohydraulics literature. We formulated three main hypotheses related to the degree of multidisciplinarity, interdisciplinarity and transdisciplinarity within ecohydraulics. We investigated temporal changes in author affiliation patterns and identified dominant topics of research. The total number of proceeding papers has increased over time and the field is becoming increasingly global. This and the identification of 10 distinctive macro-topics suggest well-developed multidisciplinarity in ecohydraulics. There has been reasonable stability in individual topics across time, except for 11 (out of 51) significant trends within the ...

Despite the existence of many approaches to reference-condition modeling, Bayesian statistical methods have not been used. We assessed whether a hybrid approach that combined features of existing reference-condition approaches with Bayesian model fitting and assessment of test sites could provide superior results to existing established methods. We used 4 Bayesian models of increasing complexity to develop and test reference-condition models for 5 biotic endpoints across 3 data sets. Our best models were comparable or superior to standard approaches (Benthic Assessment of Sediment, Australian River Assessment System) using the same data. Those of our models with the simplest endpoint (species richness) performed best. On average, those models with the simplest model structures also performed best, but differences in performance among models of different complexity were small. All models performed poorly at detecting the lower levels of simulated impact in the test data. However, these impacts were...

Human-induced changes in river flow regimes are ubiquitous worldwide. Although numerous case studies have identified negative ecological impacts of changes in different aspects of flow regimes (e.g. magnitude, timing), there have been few attempts to systematically review this literature to derive general relationships regarding ecological responses to changes in flow regimes. Systematic literature reviews can inform science and management in ecologically complex systems not amenable to experimentation. However, such analysis of existing literature is often limited by inconsistent study design and data reporting. To attempt to overcome these difficulties, we used the recently developed Eco Evidence method and software to analyse 165 studies of ecological responses to changes in river flow regimes. Eco Evidence provides a rule set and standardised list of terms to assist reviewers to interpret consistently the results of disparate studies. The companion software assists with the synthesis of this information to reach transparent and repeatable conclusions regarding cause-effect hypotheses of ecological responses to environmental drivers. We compared our results to those of a recent, informal systematic review of the same studies, which is proving extremely influential. Stronger conclusions are reached when evidence is weighted, classified and combined according to the rules in Eco Evidence. Compared to the original review, we reached informative conclusions for a larger number of flow-response hypotheses, found that hypotheses for which the most evidence was available returned inconsistent results, addressed hypotheses at levels of conceptual resolution relevant to management and identified where insufficient evidence exists to reach a conclusion. Analyses conducted at several levels of conceptual resolution found strong support for many hypotheses regarding ecological impacts. We found a consistent sensitivity to changes in flow regime for both fish and riparian vegetation across a variety of performance metrics. While macroinvertebrate responses varied among performance metrics (e.g. abundance was negatively affected by increases or decreases in flows, diversity was only negatively affected by flow decreases, and assemblage structure was affected by neither), they were largely consistent within these metrics. We thus conclude that the Eco Evidence approach allowed us to extract more knowledge from the data set than was possible in the original review. Eco Evidence can improve synthesis of the burgeoning ecological literature and improve our general understanding in ecology. Amid widespread calls for 'evidence-based' environmental management, this powerful tool provides managers with a means of using research to help inform complex environmental decision-making. © 2013 The Authors. Freshwater Biology.

Knowledge of temporal change in ecological condition is important for the understanding and management of ecosystems. However, analyses of trends in biological condition have been rare, as there are usually too few data points at any single site to use many trend analysis techniques. We used a Bayesian hierarchical model to analyse temporal trends in stream ecological condition (as measured by the invertebrate-based index SIGNAL) across Melbourne, Australia. The Bayesian hierarchical approach assumes dependency amongst the sampling sites. Results for each site "borrow strength" from the other data because model parameter values are assumed to be drawn from a larger common distribution. This leads to robust inference despite the few data that exist at each site. Utilising the flexibility of the Bayesian approach, we also modelled change over time as a function of catchment urbanisation, allowed for potential temporal and spatial autocorrelation of the data and trend estimates, and used prior information to improve the estimate of data uncertainty. We found strong evidence of a widespread decline in SIGNAL scores for edge habitats (areas of little or no flow). The rate of decline was positively associated with catchment urbanisation. There was no evidence of such declines for riffle habitats (areas with rapid and turbulent flow). Melbourne has experienced a decline in rainfall, indicative of either drought and/or longer-term climate change. The results are consistent with the expected coupled effects of these rainfall changes and increasing urbanisation, but more research is needed to isolate a causal mechanism. More immediately, however, the Bayesian hierarchical approach has allowed us to identify a pattern in a biological monitoring data set that might otherwise have gone un-noticed, and to demonstrate a large-scale temporal decline in biological condition. © 2009 Ecography.

Traditional formulas, such as the Harris and Benedict equation (HBE), do not accurately predict energy expenditure (EE) in mechanically ventilated, critically ill patients (MVCIP). The purpose of this study was to develop a predictive EE equation to assess EE requirements in MVCIP. A portable metabolic cart was used to measure indirectly EE in 112 MVCIP. Patients were studied at rest and for 30 min on the first or second day of ICU admission. No nutrition was received during the study period. Variables investigated were: age, BSA, Acute Physiology and Chronic Health Evaluation (APACHE II) score, sepsis score, Injury Severity Score (ISS), respiratory rate (f), tidal volume (VT), minute ventilation, mean arterial pressure, heart rate, body temperature (Temp), and outcome. Patient age, APACHE II score, sepsis score, ISS, and BSA were 50 +/- 20 yr, 16 +/- 7, 11 +/- 6, 32 +/- 14, and 1.80 +/- 0.27 m2, respectively. Correlation and multiple regression analyses were used with EE as the dependent variable. A predictive equation (EE [kcal/day] = 945 BSA -6.4 age + 108 Temp + 24.2 f + 817 VT -4349) was determined from variables that contributed greater than 3% to the variance of EE: BSA (52%), age (10%), f (5%), VT (5%), and Temp (3%). The HBE underestimated measured EE by 34 +/- 19% and in 79 patients deviated greater than 15%. Using the new equation, only 15 patients' EE deviated greater than 15% from measured EE. The new predictive EE equation can accurately assess EE in MVCIP.

Resting energy expenditure (EE) is often used as the basis of nutritional support for critically ill patients but whether resting EE is representative of total daily EE is not known. EE was measured for 24 h in ten mechanically ventilated, critically ill patients (average Acute Physiology and Chronic Health Evaluation II score 23) to determine EE, resting EE, and the energy expended during various ICU activities. Although activities, such as weighing the patient on a sling-type bed scale, repositioning, and chest physiotherapy resulted in dramatic EE increases above resting levels (36%, 31%, and 20%, respectively), the actual contribution of these activities to total EE was small (1.1%, 2.1%, and 3.6%, respectively). The mean measured resting EE was 47.3 +/- 22.3% above mean predicted EE based on the Harris and Benedict equation, and the mean total 24-h EE was 6.9 +/- 2.6(SD)% above the mean measured resting EE. In this group of mechanically ventilated, critically ill patients, an activity factor of no greater than 10% above resting EE is appropriate.