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The city of Christchurch, New Zealand, incurred significant damage due to a series of earthquakes in 2010 and 2011. The city had, by the late 2010s, regained economic and social normalcy after a sustained period of rebuilding and economic recovery. Through the concerted rebuilding effort, a modern central business district (CBD) with redesigned infrastructure and amenities was developed. The Christchurch rebuild was underpinned by a commitment of urban planners to an open and connected city, including the use of innovative technologies to gather, use and share data. As was the case elsewhere, the COVID-19 pandemic brought about significant disruptions to social and economic life in Christchurch. Border closures, lockdowns, trading limitations and other restrictions on movement led to changes in traditional consumer behaviors and affected the retail sector’s resilience. In this study, we used CBD pedestrian traffic data gathered from various locations to predict changes in retail spending and identify recovery implications through the lens of retail resilience. We found that the COVID-19 pandemic and its related lockdowns have driven a substantive change in the behavioral patterns of city users. The implications for resilient retail, sustainable policy and further research are explored.

Prominent tools for assessing and managing risk include risk cubes, risk burndown charts, and automated risk management software. They are generally lacking, however, in accommodating ideation and brainstorming to identify potential problems. A suggested approach for improving the process is to apply strategic management models currently used as commercial best practices. Many are directly applicable and adaptable to systems engineering processes including risk management. This article presents traditional risk tools and introduces a complementary management model tailored to the identification, scoring, and tracking of potential program threats. Additional management models are presented for further investigation and adaptation.

Background: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p=1·84 × 10 −9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p=6·46 × 10 −9). Cannabis use disorder and cannabis use were genetically correlated (r g 0·50, p=1·50 × 10 −21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. Funding: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

Handwriting was since the start of the history, a higher expression of human skills, and was used for documentation of experiences, and for communication. Existing writing technology require a writing tool, like a pen, and a dedicated writing surface, like paper, or more recently an electronic tablet. These accessories of writing, of writing tool and service, are not available in many daily life situations. Furthermore, the writing accessories, are not natural, in many cases are not ergonomic, and thus can cause fatigue, and in extreme cases contribute to muscular and neurological diseases. In this work, we suggest to step back in history and step forward in technology, and to create, for the first time, an alternate writing solution without any accessories, using one own finger as writing tool, and write on almost any surface. For this, we used directional pressure sensors attached to the fingernail. Changes in the pressure induced on the fingertip in different directions while writing, are projected to the fingernail, and then assessed as a voltage pattern by the sensor. Decoding the pattern, can reveal symbols like letters, punctuations, and writing commands. In this paper, we describe the new pressure sensing modality and tailor processing methods. We tested the new technology on two subjects having different writing patterns while writing alphabet and sentences on different surfaces. We reached letter detection of over 80% while writing on a table, and the word detection rate, was near 70%, after applying the correction algorithm include language priors. The results of this work can revolutionize the way people write and communicate using more convenient, and more approachable, finger-tip writing.

Purpose: The effects of two different high-intensity training methods on 2,000 m rowing ergometer performance were examined in a feasibility study of 24 national-level rowers aged 18–27 years (17 males, 2,000 m ergometer time trial 6:21.7 ± 0:14.6 (min:s) and seven females, 2,000 m ergometer 7:20.3 ± 0:12.1. Habitual training for all participants was ~12–16 h per week).Methods: 16 high-intensity ergometer sessions were completed across two 3-week periods. Participants were allocated into two groups according to baseline 2,000 m time. High-intensity interval session-sprint-interval session (HIIT-SIT) completed eight HIIT (8 × 2.5 min intervals; 95% of 2,000 m wattage) followed by eight SIT (three sets of 7 × 30 s intervals; maximum effort). SIT-HIIT completed eight SIT sessions followed by eight HIIT sessions. Both a 2,000-m time trial and a progressive incremental test finishing with 4 min “all-out” performance were completed before and after each 3-week phase.Results: Both groups showed similar improvements in 2,000 m time and 4 min “all-out” distance after the first 3 weeks (2,000 m time: HIIT-SIT: −2.0 ± 0.6%, mean ± 90% CL, p = 0.01; SIT-HIIT: −1.5 ± 0.3%, p = 0.01) with no significant difference between groups. HIIT-SIT demonstrated the greatest improvements in submaximal heart rate (HR) during the progressive incremental test with eight sessions of HIIT showing a greater reduction in submaximal HR than eight sessions of SIT. The net improvement of 16 high-intensity sessions on 2,000 m time was −2.5% for HIIT-SIT (−10.6 ± 3.9 s, p = 0.01) and − 2.2% for SIT-HIIT (−9.0 ± 5.7 s, p = 0.01) and for 4 min “all-out” performance was 3.1% for HIIT-SIT (36 ± 25 m, p = 0.01) and 2.8% for SIT-HIIT (33 ± 27 m, p = 0.01).Conclusion: Eight sessions of high-intensity training can improve 2,000 m ergometer rowing performance in national-level rowers, with a further eight sessions producing minimal additional improvement. The method of high-intensity training appears less important than the dose.

Seafood represents up to 20% of animal protein consumption in global food consumption and is a critical dietary and income resource for the world's population. Currently, over 30% of marine fish stocks are harvested at unsustainable levels, and the industry faces challenges related to Illegal, Unregulated and Unreported (IUU) fishing. Accurate species identification is one critical component of successful stock management and helps combat fraud. Existing DNA-based technologies permit identification of seafood even when morphological features are removed, but are either too time-consuming, too expensive, or too specific for widespread use throughout the seafood supply chain. FASTFISH-ID is an innovative commercial platform for fish species authentication, employing closed-tube barcoding in a portable device. This method begins with asymmetric PCR amplification of the full length DNA barcode sequence and subsequently interrogates the resulting single-stranded DNA with a universal set of Positive/Negative probes labeled in two fluorescent colors. Each closed-tube reaction generates two species-specific fluorescent signatures that are then compared to a cloud-based library of previously validated fluorescent signatures. This novel approach results in rapid, automated species authentication without the need for complex, time consuming, identification by DNA sequencing, or repeated analysis with a panel of species-specific tests. Performance of the FASTFISH-ID platform was assessed in a blinded study carried out in three laboratories located in the UK and North America. The method exhibited a 98% success rate among the participating laboratories when compared to species identification via conventional DNA barcoding by sequencing. Thus, FASTFISH-ID is a promising new platform for combating seafood fraud across the global seafood supply chain. © 2021 The Authors

BACKGROUND In the face of hospital capacity strain, hospitals have developed multifaceted plans to try to improve patient flow. Many of these initiatives have focused on the timing of discharges and on lowering lengths of stay, and they have met with variable success. We deployed a novel tool in the electronic health record to enhance discharge communication. OBJECTIVE The aim of this study is to evaluate the effectiveness of a discharge communication tool. METHODS This was a prospective, single-center, pre-post study. Hospitalist physicians and advanced practice providers (APPs) used the Discharge Today Tool to update patient discharge readiness every morning and at any time the patient status changed throughout the day. Primary outcomes were tool use, time of day the clinician entered the discharge order, time of day the patient left the hospital, and hospital length of stay. We used linear mixed modeling and generalized linear mixed modeling, with team and discharging provider included in all the models to account for patients cared for by the same team and the same provider. RESULTS During the pilot implementation period from March 5, 2019, to July 31, 2019, a total of 4707 patients were discharged (compared with 4558 patients discharged during the preimplementation period). A total of 352 clinical staff had used the tool, and 84.85% (3994/4707) of the patients during the pilot period had a discharge status assigned at least once. In a survey, most respondents reported that the tool was helpful (32/34, 94% of clinical staff) and either saved time or did not add additional time to their workflow (21/24, 88% of providers, and 34/34, 100% of clinical staff). Although improvements were not observed in either unadjusted or adjusted analyses, after including starting morning census per team as an effect modifier, there was a reduction in the time of day the discharge order was entered into the electronic health record by the discharging physician and in the time of day the patient left the hospital (decrease of 2.9 minutes per additional patient, P=.07, and 3 minutes per additional patient, P=.07, respectively). As an effect modifier, for teams that included an APP, there was a significant reduction in the time of day the patient left the hospital beyond the reduction seen for teams without an APP (decrease of 19.1 minutes per patient, P=.04). Finally, in the adjusted analysis, hospital length of stay decreased by an average of 3.7% (P=.06). CONCLUSIONS The Discharge Today tool allows for real time documentation and sharing of discharge status. Our results suggest an overall positive response by care team members and that the tool may be useful for improving discharge time and length of stay if a team is staffed with an APP or in higher-census situations.

We present experimental inferences of cross-field impurity transport coefficients for Alcator C-Mod plasmas using a novel forward model for the entire Ca K-alpha spectrum, including satellite lines within the spectral range, to compare to high-resolution X-ray Imaging Crystal Spectroscopy (XICS). These measurements are complemented by Extreme Ultra-Violet (EUV) spectroscopy that constrains transport closer to the edge. Using new atomic data sets for both XICS and EUV analysis has enabled consideration of line ratios across both spectral ranges and has increased the accuracy of inferred transport coefficients. Inclusion of charge exchange between edge thermal neutrals and impurities is shown to be extremely important in C-Mod pedestals. We obtain D atomic neutral densities from experimental D Ly-alpha measurements at the midplane and compare these to SOLPS-ITER simulations, finding good agreement. Bayesian inferences of impurity transport coefficients are presented for L-, EDA H-, and I-mode discharges, making use of the Aurora package for forward modeling and combining our spectroscopic constraints. Experimentally inferred diffusion profiles are found to match turbulent transport models at midradius within uncertainties, using both quasilinear gyro-fluid TGLF SAT-1 and nonlinear ion-scale gyrokinetic CGYRO simulations. Significant discrepancies in convection are observed in some cases, suggesting difficulties in predictions of flat or hollow impurity profiles., 23 pages of main body; 38 pages including appendices. 17 figures. Submitted to Nuclear Fusion

There is limited information about the utility of donor-specific antibody (DSA) against HLA monitoring and the role of protocol kidney biopsy for de novo DSA (dnDSA) in simultaneous liver and kidney (SLK) transplant recipients.We analyzed SLK transplant recipients transplanted between January 2005 and December 2017, who had DSA checked posttransplant. Patients were divided into 2 groups based on whether they developed dnDSA posttransplant (dnDSA+) or not (dnDSA-). Kidney graft rejection ±45 d of dnDSA and a kidney death-censored graft survival were the primary endpoints.A total of 83 SLK transplant recipients fulfilled our selection criteria. Of those, 23 were dnDSA+ and 60 were dnDSA-. Twenty-two of 23 dnDSA+ patients had DSA against class II HLA, predominantly against DQ. Fifteen recipients underwent kidney biopsy ±45 d of dnDSA. Six of these were clinically indicated due to kidney graft dysfunction. The other 9 had a protocol kidney biopsy only due to dnDSA, and 6 of these 9 had a rejection. Also, 3 recipients had sequential biopsies of both the kidney and liver grafts. Among those with sequential biopsies of both grafts, there was a difference between the organs in the rate and types of rejections. At last follow up, dnDSA was not associated with graft failure of either the kidney or liver.Although our study was limited by a small sample size, it suggests the potential utility of DSA monitoring and protocol kidney biopsy for dnDSA.