Kiewit Drone Progress Tracking Application

Kiewit is one of the largest construction companies in the world and has long pushed the standards for innovation within the construction industry. Kiewit operates on an initiative to be a data-driven organization. Construction projects track progress data for a variety of reasons, none more important than meeting contractual obligations. Keeping clients in tune with the status of a given project not only gives them peace of mind but also correlates directly with revenue. For solar projects, quantity claiming is done by walking up and down rows of solar panels, posts, and torque tubes across the project site and manually entering the status data. The project goal was to create a secondary automated version of the manual quantity claiming process for construction progress tracking. The Kiewit Drone Progress Tracking application serves as a digital management system that supports tracking the progress of solar panel construction sites in a more streamlined way. It hosts a machine learning model that was built in-house. It predicts the progress of the construction site based on drone-captured geo-location tagged images called GeoTIFFs. The application hosts these GeoTIFFs and allows for the creation and display of labels that ultimately help train and run the machine learning model. The development of this application will increase the training data used to impact the accuracy of the AI model, as well as improve accessibility and efficiency for the current solution and provide the platform for expansion to other construction project types

Cost effectiveness of commercial portable ex vivo lung perfusion at a low-volume US lung transplant center

Background: Portable ex vivo lung perfusion during lung transplantation is a resource-intensive technology. In light of its increasing use, we evaluated the cost-effectiveness of ex vivo lung perfusion at a low-volume lung transplant center in the USA. Methods: Patients listed for lung transplantation (2015–2021) in the United Network for Organ Sharing database were included. Quality-of-life was approximated by Karnofsky Performance Status scores 1-year post-transplant. Total transplantation encounter and 1-year follow-up costs accrued by our academic center for patients listed from 2018 to 2021 were obtained. Cost-effectiveness was calculated by evaluating the number of patients attaining various Karnofsky scores relative to cost. Results: Of the 13 930 adult patients who underwent lung transplant in the United Network for Organ Sharing database, 13 477 (96.7%) used static cold storage and 453 (3.3%) used ex vivo lung perfusion, compared to 30/58 (51.7%) and 28/58 (48.3%), respectively, at our center. Compared to static cold storage, median total costs at 1 year were higher for ex vivo lung perfusion (918 000 dollars vs. 516 000 dollars; p = 0.007) along with the cost of living 1 year with a Karnofsky functional status of 100 after transplant (1 290 000 dollars vs. 841 000 dollars). In simulated scenarios, each Karnofsky-adjusted life year gained by ex vivo lung perfusion was 1.00–1.72 times more expensive. Conclusions: Portable ex vivo lung perfusion is not currently cost-effective at a low-volume transplant centers in the USA, being 1.53 times more expensive per Karnofsky-adjusted life year. Improving donor lung and/or recipient biology during ex vivo lung perfusion may improve its utility for routine transplantation.</p

Periodontal Ehlers-Danlos Syndrome Is Caused by Mutations in C1R and C1S, which Encode Subcomponents C1r and C1s of Complement

Periodontal Ehlers-Danlos syndrome (pEDS) is an autosomal-dominant disorder characterized by early-onset periodontitis leading to premature loss of teeth, joint hypermobility, and mild skin findings. A locus was mapped to an approximately 5.8 Mb region at 12p13.1 but no candidate gene was identified. In an international consortium we recruited 19 independent families comprising 107 individuals with pEDS to identify the locus, characterize the clinical details in those with defined genetic causes, and try to understand the physiological basis of the condition. In 17 of these families, we identified heterozygous missense or in-frame insertion/deletion mutations in C1R (15 families) or C1S (2 families), contiguous genes in the mapped locus that encode subunits C1r and C1s of the first component of the classical complement pathway. These two proteins form a heterotetramer that then combines with six C1q subunits. Pathogenic variants involve the subunit interfaces or inter-domain hinges of C1r and C1s and are associated with intracellular retention and mild endoplasmic reticulum enlargement. Clinical features of affected individuals in these families include rapidly progressing periodontitis with onset in the teens or childhood, a previously unrecognized lack of attached gingiva, pretibial hyperpigmentation, skin and vascular fragility, easy bruising, and variable musculoskeletal symptoms. Our findings open a connection between the inflammatory classical complement pathway and connective tissue homeostasis

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

High Dimension Prediction

Prediction methods may be high dimension in either outcome or covariate matrix. Here we examine shrinkage methods for multivariate prediction, multivariate calibration, and penalized regression for a single outcome with a large number of covariates--we motivate this section with microbiome data

MIXREGLS: A Program for Mixed-Effects Location Scale Analysis

MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects on both the between-subject and within-subject variances. Another use is for clustered data in which subjects are nested within clusters (e.g., clinics, hospitals, schools, etc.) and interest is in modeling the between-cluster and within-cluster variances in terms of covariates. MIXREGLS was written in Fortran and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution. Estimation of the random effects is accomplished using empirical Bayes methods. Examples illustrating stand-alone usage and features of MIXREGLS are provided, as well as use via the SAS and R software packages

Building Use Efficiency Platform

With the rise of hybrid workforces, people are shifting the way they interact with the built environment. Accelerated by COVID-19, businesses are feeling the pressure to adjust given the rising costs of office ownership and leasing. With the ever-changing space utilization requirements by companies and employees alike, businesses need data-driven insights to answer the questions: How do their employees use their current space? How can they lower utility costs? How can they optimize their future floor plans and remodeling projects? Introducing the Olsson Building Use Efficiency Platform, a custom sensor and web application platform that provides an end-to-end experience for building engineers and building science specialists to collect, analyze, and visualize data of building and office spaces. With data collection of environmental factors such as temperature, humidity, lux, UV, and occupancy, this custom product enables Olsson to create a new product line that empowers clients with data-driven recommendations for future remodeling, building, and engineering projects with Olsson. The team developed a custom hardware solution based on the Z-Wave wireless protocol using two different components: 1) Off-the-shelf environmental sensors to enable the on-site installation of data-collecting sensors and 2) Raspberry Pi single-board computers to coordinate sensor configuration, data collection, and data uploading. This data is sent to a custom web application—compatible on desktop, tablet, and mobile—to enable the provisioning and pairing of sensors and hubs, filtering and sorting of sensor data, visualizing desired data via a robust charting experience, and exporting data as a CSV file for further exploration

A Social Media Quality Review of Popular Sinusitis Videos on TikTok

Objective: Social media may inform health care decisions among younger patient populations. TikTok is a social media platform that allows users to post short-form videos. This study aimed to assess the quality of sinusitis-related videos on TikTok. Study Design: We searched TikTok on January 29, 2023, for sinusitis-related hashtags: #sinusitis, #sinus, #sinusinfection. Setting: Internet. Methods: The number of views/shares per day, uploader type (nonmedical influencer, lay individual, and medical professional) content categories (medical advice, marketing, comedy, and lifestyle/acceptability), and content type (educational vs factual) were collected. The Patient Education Materials Assessment Tool for Audiovisual Material and Journal of the American Medical Association criteria score was used to measure understandability, actionability, and reliability. The Global Quality Scale (GQS) was used to evaluate the quality of videos; the harm/benefit score was used to evaluate causative effects. Analyses were performed using analysis of variance (α = .05). Results: There were 221 videos identified, which garnered over 300 million views and 1 million shares. Almost half of the videos were published by nonmedical influencers. When controlling for covariates, nonmedical influencers and lay uploaders were more likely to have harmful harm/benefit scores, less understandable videos, and lower GQS scores compared to medical professionals. Less than half of videos posted by nonmedical influencers categorized as educational were factual (46.7%); lay individuals and medical professionals had higher rates of factual educational content (79.9% and 83.7%, respectively). Conclusion: Most nonmedical influencer-posted TikTok videos about sinusitis are inaccurate, despite being portrayed as medical advice/educational. Rhinologists must find modern ways to disseminate true disease-related content via social media to combat medical misinformation.</p