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Manufacturing on Autopilot: Ohio Automation on Manufacturing Wages and Employment
Get PDFIn 2017, automation was forecasted to see a 47% increase in the next two decades (Bughin et al. 2017). With the usage of algorithms, automation can now be used for more than routine tasks and has the ability to replace labor in cognitive tasks, greatly expanding the range of roles in the labor market it could take on (Frey et al. 2017). With this, there will be the subsequent impacts on Ohio’s economy and productivity levels in manufacturing and productivity. Ohio is a main state in manufacturing, and 7.6% of Ohio jobs have a high level of exposure to automation (Exposure to Automation in Ohio 2021). We use data from the Bureau of Labor Statistics to create initial data visualizations on Ohio’s manufacturing employment. Through this initial research, we hypothesize a negative correlation between robotic expenditures and manufacturing employment and wages. However, in the future, we hope to run regressions with variables such as robotic expenditures and robot count using data from national manufacturing surveys
Pain Screening in Youth with Sickle Cell Disease: A Quality Improvement Study
Get PDFBackground: Youth with sickle cell disease (SCD) face several challenges as they age, including increased pain frequency, duration, and interference. The purpose of this study was to (i) determine the feasibility of routine pain screening; (ii) identify and describe various clinical pain presentations; and (iii) understand preferences/resources related to engaging in integrative health and medicine (IHM) modalities within an outpatient pediatric SCD clinic. Methods: During routine outpatient visits, patients aged 8–18 completed measures of pain frequency, duration, and chronic pain risk (Pediatric Pain Screening Tool [PPST]). Participants screening positive for (i) persistent or chronic pain or (ii) medium or high risk for persistent symptoms and disability on the PPST were asked to complete measures of pain interference, pain catastrophizing, and interest in/resources for engaging in IHM modalities. Results: Between March 2022 and May 2023, 104/141 (73.8%) patients who attended at least one outpatient visit were screened. Of these 104 (mean age 12.46, 53.8% female, 63.5% HbSS), 34 (32.7%) reported persistent or chronic pain, and 48 (46.2%) reported medium or high risk for persistent symptoms and disability. Patients completing subsequent pain screening measures reported a mean pain interference T-score of 53.2 ± 8.8 and a mean pain catastrophizing total score of 24.3 ± 10.2. Patients expressed highest interest in music (55.6%) and art therapy (51.9%) and preferred in-person (81.5%) over virtual programming (22.2%). Conclusions: Comprehensive pain screening is feasible within pediatric SCD care. Classifying patients by PPST risk may provide a means of triaging patients to appropriate services to address pain-related psychosocial factors
The Effect of Early Postnatal Auditory Stimulation on Outcomes in Preterm Infants
Get PDFAbstract: Preterm infants are deprived of in utero sensory stimulation during the third trimester, an important period of central nervous system development. As a result, maturational trajectories are often reduced in infants born preterm. One such system affected is the brain including the auditory and respiratory control pathways. During normal pregnancy the intrauterine environment attenuates external auditory stimuli while exposing the fetus to filtered maternal voice, intra-abdominal sounds, and external stimuli. In contrast, during the third trimester of development, preterm infants are exposed to a vastly different soundscape including non-attenuated auditory sounds and a lack of womb related stimuli, both of which may affect postnatal brain maturation. Therefore, fostering a nurturing postnatal auditory environment during hospitalization may have a significant impact on related outcomes of preterm infants. Studies using a range of postnatal auditory stimulations have suggested that exposure to sounds or lack thereof can have a significant impact on outcomes. However, studies are inconsistent with sound levels, duration of exposure to auditory stimuli, and the gestational age at which infants are exposed. Impact: Auditory stimulation can provide a low cost and low risk intervention to stabilize respiration, improve neuronal maturation and reduce long-term sequelae in preterm infants. The potential benefits of auditory stimulation are dependent on the type of sound, the duration of exposure and age at time of exposure. Future studies should focus on the optimal type and duration of sound exposure and postnatal developmental window to improve outcomes
The History of Diapers and their Environmental Impact
Get PDFImpact: This article examines diaper practices around the world throughout history. This article reviews the innovation of the modern diaper and the environmental effects of disposable diapers
The Spectrum of Pneumonia Among Intubated Neonates in the Neonatal Intensive Care Unit
Get PDFWe review the pathophysiology, epidemiology, diagnosis, treatment, and prevention of ventilator-associated pneumonia (VAP) in neonates. VAP has been studied primarily in adult ICU patients, although there has been more focus on pediatric and neonatal VAP (neo-VAP) in the last decade. The definition as well as diagnosis of VAP in neonates remains a challenge to date. The neonatal intensivist needs to be familiar with the current diagnostic tools and prevention strategies available to treat and reduce VAP to reduce neonatal morbidity and the emergence of antibiotic resistance. This review also highlights preventive strategies and old and emerging treatments available
Using Imaging Modalities to Predict Nanoparticle Distribution and Treatment Efficacy in Solid Tumors: The Growing Role of Ultrasound
Get PDFNanomedicine in oncology has not had the success in clinical impact that was anticipated in the early stages of the field\u27s development. Ideally, nanomedicines selectively accumulate in tumor tissue and reduce systemic side effects compared to traditional chemotherapeutics. However, this has been more successful in preclinical animal models than in humans. The causes of this failure to translate may be related to the intra- and inter-patient heterogeneity of the tumor microenvironment. Predicting whether a patient will respond positively to treatment prior to its initiation, through evaluation of characteristics like nanoparticle extravasation and retention potential in the tumor, may be a way to improve nanomedicine success rate. While there are many potential strategies to accomplish this, prediction and patient stratification via noninvasive medical imaging may be the most efficient and specific strategy. There have been some preclinical and clinical advances in this area using MRI, CT, PET, and other modalities. An alternative approach that has not been studied as extensively is biomedical ultrasound, including techniques such as multiparametric contrast-enhanced ultrasound (mpCEUS), doppler, elastography, and super-resolution processing. Ultrasound is safe, inexpensive, noninvasive, and capable of imaging the entire tumor with high temporal and spatial resolution. In this work, we summarize the in vivo imaging tools that have been used to predict nanoparticle distribution and treatment efficacy in oncology. We emphasize ultrasound imaging and the recent developments in the field concerning CEUS. The successful implementation of an imaging strategy for prediction of nanoparticle accumulation in tumors could lead to increased clinical translation of nanomedicines, and subsequently, improved patient outcomes. This article is categorized under: Diagnostic Tools In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery Emerging Technologies
A Practical Guide to Light-Sheet Microscopy for Nanoscale Imaging: Looking Beyond the Cell
Get PDFWe present a comprehensive guide to light-sheet microscopy (LSM) to assist scientists in navigating the practical implementation of this microscopy technique. Emphasizing the applicability of LSM to image both static microscale and nanoscale features, as well as diffusion dynamics, we present the fundamental concepts of microscopy, progressing through beam profile considerations, to image reconstruction. We outline key practical decisions in constructing a home-built system and provide insight into the alignment and calibration processes. We briefly discuss the conditions necessary for constructing a continuous 3D image and introduce our home-built code for data analysis. By providing this guide, we aim to alleviate the challenges associated with designing and constructing LSM systems and offer scientists new to LSM a valuable resource in navigating this complex field
Simmrd: An Open-Source Tool to Perform Simulations in Mendelian Randomization
Get PDFMendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing simulations to evaluate the performance of MR methods in a range of scenarios encountered in practice. Researchers can directly modify the simmrd source code so that the research community may arrive at a widely accepted framework for researchers to evaluate the performance of different MR methods
Examining Adverse Childhood Experiences and Black Youth\u27s Engagement in a Hospital-Based Violence Intervention Program Using Administrative Data
No full textBackground: Stemming from poverty and systemic racism, Black youth are disproportionately represented in hospital-based violence intervention programs (HVIPs) due to greater violence exposure. HVIPs are a critical intervention that have been shown to reduce rates of reinjury in urban hospitals and trauma centers across the United States; however, they are plagued by low enrollment and engagement rates. Few studies have examined factors related to engagement, particularly among Black youth. Methods: Guided by Trauma Theory and Critical Race Theory, this study uses a retrospective cohort design. Between-group differences of adverse childhood experiences (ACEs) among engaged youth compared to nonengaged youth who were violently injured and recruited for a HVIP were examined using chi-square and logistic regression. ACEs were approximated using a novel approach with administrative data. Results: Results indicated that the total ACE score was not significantly associated with engagement status. Individual ACEs were tested across age groups. Conclusions: This study highlights a novel approach to understanding ACEs among a hard-to-reach population and illuminates the significant level of ACEs faced by violence-exposed Black youth at young ages. Considering theory, Black families may be more reluctant to engage due to fear and past harms in social service systems stemming from systemic racism. Though ACEs did not predict engagement in this study, considering the high rates of ACEs experienced by Black youth and their families in the context of systemic racism suggests that HVIPs should acknowledge historical harms and foster trauma-informed and healing-centered interactions during recruitment and later stages of engagement
A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences
No full textPhase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems
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