Citadel

Objectives Our primary objective was to assess the association between symptoms at the time of surgery and postoperative pulmonary complications and mortality in patients with COVID-19. Our secondary objective was to compare postoperative outcomes between patients who had recovered from COVID-19 and asymptomatic patients and explore the effect of the time elapsed between infection and surgery in the former. Our hypotheses were that symptomatic patients had a higher risk of pulmonary complications, whereas patients who had recovered from the infection would exhibit outcomes similar to those of asymptomatic patients. Background Managing COVID-19-positive patients requiring surgery is complex due to perceived heightened perioperative risks. However, Canadian data in this context remains scarce. Design To address this gap, we conducted a multicentre observational cohort study. Setting Across seven hospitals in the province of Québec, the Canadian province was most affected during the initial waves of the pandemic. Participants We included adult surgical patients with either active COVID-19 at the time of surgery or those who had recovered from the disease, from March 22, 2020 to April 30, 2021. Outcomes We evaluated the association between symptoms or recovery time and postoperative pulmonary complications and hospital mortality using multivariable logistic regression and Cox models. The primary outcome was a composite of any postoperative pulmonary complication (atelectasis, pneumonia, acute respiratory distress syndrome and pneumothorax). Our secondary outcome was hospital mortality, assessed from the date of surgery up to hospital discharge. Results We included 105 patients with an active infection (47 were symptomatic and 58 were asymptomatic) at the time of surgery and 206 who had recovered from COVID-19 prior to surgery in seven hospitals. Among patients with an active infection, those who were symptomatic had a higher risk of pulmonary complications (OR 3.19, 95% CI 1.12 to 9.68, p=0.03) and hospital mortality (HR 3.67, 95% CI 1.19 to 11.32, p=0.02). We did not observe any significant effect of the duration of recovery prior to surgery on patients who had recovered from their infection. Their postoperative outcomes were also similar to those observed in asymptomatic patients. Interpretation Symptomatic status should be considered in the decision to proceed with surgery in COVID-19-positive patients. Our results may help optimise surgical care in this patient population. Study registration ClinicalTrials.gov Identifier: NCT04458337 registration date: 7 July 2020.

Phytoplankton can encounter dynamic changes in their environment including fluctuating nutrient supply, and therefore require survival mechanisms to compete for such growth-limiting resources. Diatoms, single-celled eukaryotic microalgae, are typically first responders when crucial macronutrients phosphorus (P) and nitrogen (N) enter the marine environment and therefore must have tightly regulated nutrient sensory systems. While nutrient starvation responses have been described, comparatively little is known about diatom nutrient sensing mechanisms. We previously identified that the model diatoms Phaeodactylum tricornutum and Thalassiosira pseudonana use calcium (Ca²⁺) ions as a rapid intracellular signaling response following phosphate resupply. This response is evident only in phosphate deplete conditions, suggesting that it is coordinated in P-starved cells. Rapid increases in N uptake and assimilation pathways observed following phosphate resupply, indicate tight interplay between P and N signaling. To regulate such downstream changes, Ca²⁺ ions must bind to Ca²⁺ sensors following phosphate induced Ca²⁺ signals, yet this molecular machinery is unknown. Here, we describe our findings in relation to known diatom P starvation signaling mechanisms and discuss their implications in the context of environmental macronutrient metadata and in light of recent developments in the field. We also consider the importance of studying phytoplankton nutrient signaling systems in the face of future ocean conditions.

This article provides an overview of the Survivors: Local Stories of Domestic Violence (hereafter, Survivors) civic engagement project. Survivors’ learning objectives were to increase the understanding of the complexity of intimate partner abuse and foster empathy in outsiders’ responses, something at the cornerstone of the #MeToo social movement and connected to trauma-informed teaching. To accomplish these goals, students were given quotes from individuals who suffered abuse and were asked to create a “body” that reflected the abuse and the “after” (coping/healing). This project was then presented at a local theater and included community members who read the quotes while the students presented their work. After the event, event participants were invited to submit their feedback via a brief survey or interviews, which resulted in involvement from 45 individuals. Analysis of both items reveals that Survivors was a meaningful experience for the students and community members involved.

DNA-Encoded Library (DEL) technology allows the screening of millions, or even billions, of encoded compounds in a pooled fashion which is faster and cheaper than traditional approaches. These massive amounts of data related to DEL binders and not-binders to the target of interest enable Machine Learning (ML) model development and screening of large, readily accessible, drug-like libraries in an ultra-high-throughput fashion. Here, we report a comparative assessment of the DEL+ML pipeline for hit discovery using three DELs and five ML models (fifteen DEL+ML combinations using two different feature representations). Each ML model was used to screen a diverse set of drug-like compound collections to identify orthosteric binders of two therapeutic targets, Casein kinase 1𝛼/δ (CK1𝛼/δ). Overall, 10% and 94% of the predicted binders and not-binders were confirmed in biophysical assays, including two nanomolar binders (187 and 69.6 nM affinity for CK1𝛼 and CK1δ, respectively). Our study provides insights into the DEL+ML paradigm for hit discovery: the importance of an ensemble ML approach in identifying a diverse set of confirmed binders, the usefulness of large training data and chemical diversity in the DEL, and the significance of model generalizability over accuracy. We shared our results via an open-source repository for further use and development of similar efforts.

Long timescale Molecular Dynamics (MD) simulation of small molecules is crucial in drug design and basic science. To accelerate a small data set that is executed for a large number of iterations, high-efficiency is required. Recent work in this domain has demonstrated that among COTS devices only FPGA-centric clusters can scale beyond a few processors. The problem addressed here is that, as the number of on-chip processors has increased from fewer than 10 into the hundreds, previous intra-chip routing solutions are no longer viable. We find, however, that through various design innovations, high efficiency can be maintained. These include replacing the previous broadcast networks with ring-routing and then augmenting the rings with out-of-order and caching mechanisms. Others are adding a level of hierarchical filtering and memory recycling. Two novel optimized architectures emerge, together with a number of variations. These are validated, analyzed, and evaluated. We find that in the domain of interest speed-ups over GPUs are achieved. The potential impact is that this system promises to be the basis for scalable long timescale MD with commodity clusters.

The effectiveness of COVID-19 vaccines depends on widespread vaccine uptake. Employing a telephone-administered weighted survey with 19,502 participants, we examined the determinants of COVID-19 vaccine acceptance among adults in Texas. We used multiple regression analysis with LASSO-selected variables to identify factors associated with COVID-19 vaccine uptake and intentions to receive the vaccine among the unvaccinated. The prevalence of unvaccinated individuals (22%) was higher among those aged 18–39, males, White respondents, English speakers, uninsured individuals, those facing financial challenges, and individuals expressing no concern about contracting the illness. In a fully adjusted regression model, higher odds of being unvaccinated were observed among males (aOR 1.11), the uninsured (aOR 1.38), smokers (aOR 1.56), and those facing financial struggles (aOR 1.62). Conversely, Asians, Blacks, and Hispanics were less likely to be unvaccinated compared to Whites. Among the unvaccinated, factors associated with stronger intent to receive the vaccine included age (over 65 years), Black and Hispanic ethnicity, and perceived risk of infection. Hispanic individuals, the uninsured, those covered by public insurance, and those facing financial challenges were more likely to encounter barriers to vaccine receipt. These findings underscore the importance of devising tailored strategies, emphasizing nuanced approaches that account for demographic, socioeconomic, and attitudinal factors in vaccine distribution and public health interventions.

The COVID-19 vaccine is safe and effective for children, yet parental hesitancy towards vaccinating children against the virus persists. We conducted a telephone-administered weighted survey in Texas to examine parents’ sociodemographic factors and medical conditions associated with COVID-19 vaccination intention for parents with unvaccinated children ages 5–17 years. We collected responses from 19,502 participants, of which 4879 were parents of children ages 5–17 years. We conducted multiple logistic regression with Lasso-selected variables to identify factors associated with children’s vaccination status and parents’ intention to vaccinate their children. From the unweighted sample, less than half of the parents (46.8%) had at least one unvaccinated child. These parents were more likely to be White, English-speaking, not concerned about illness, privately insured, and unvaccinated for COVID-19 themselves (p < 0.001). In the adjusted regression model, parents who were unvaccinated (vs. having COVID-19 booster, aOR = 28.6) and financially insecure (aOR = 1.46) had higher odds of having unvaccinated children. Parents who were Asian (aOR = 0.50), Black (aOR = 0.69), Spanish-speaking (aOR = 0.57), concerned about illness (aOR = 0.63), had heart disease (aOR = 0.41), and diabetes (aOR = 0.61) had lower odds of having unvaccinated children. Parents who were Asian, Black, Hispanic, Spanish-speaking, concerned about illness for others, and vaccine-boosted were more likely to have vaccination intention for their children (p < 0.001). Children’s vaccination is essential to reduce COVID-19 transmission. It is important to raise awareness about the value of pediatric COVID-19 vaccination while considering parents’ sociodemographic and medical circumstances.

Congestion pricing policies are increasingly being considered to aid in congestion management and transportation funding in urban areas. This article presents a case study of the optimization of congestion pricing policy design using the Berkeley Integrated System for Transportation Optimization (BISTRO), an open-sourced transportation planning and decision support system (DSS) that uses an agent-based simulation (ABS) and optimization framework to evaluate transportation system interventions. The study exemplifies how the granularity offered by activity-based travel demand models and ABS can be leveraged to enhance the interpretability of multi-objective transportation policy optimization through rich analyses of the effects of policy design on both individual-and system-level outcomes. The location and size of a circular charging zone with two different pricing schemes (a cordon fee and a cordoned mileage fee) are encoded as inputs to an ABS with an activity-based travel model of 15,000 travelers. Through an analysis of the effects of various weighting schemes across congestion-, social-, and revenue-based objectives, we present a method for interpretation of the inherent trade-offs in transportation policy optimization and demonstrate the importance of cultivating transparency in policy DSS that use black-box optimization in order to produce explainable, defensible policy strategies. We find that cordoned mileage fees Pareto-dominate cordon tolls, enabling greater improvements across all objectives studied. However, the prioritization of congestion reduction poses a challenge for pricing optimization in that it may result in unnecessarily large mode shifts away from driving which significantly worsens travel cost burden. We explore the role of weighting schemes in shifting the priority of optimal pricing schemes to social equity while mitigating congestion and maintaining toll revenue.