A survey of obstetric complications and pregnancy outcomes in paediatric and nonpaediatric anaesthesiologists

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73884/1/j.1460-9592.2003.01079.x.pd

First detection of PCV4 in swine in the United States: codetection with PCV2 and PCV3 and direct detection within tissues

Since PCV4 was first described in 2019, the virus has been identified in several countries in SoutheastAsia and Europe. Most studies have been limited to detecting PCV4 by PCR. Thus, PCV4 has an unclearassociation with clinical disease. This study utilized 512 porcine clinical lung, feces, spleen, serum,lymphoid tissue, and fetus samples submitted to the ISU‑VDL from June–September 2023. PCV4 wasdetected in 8.6% of samples with an average Ct value of 33. While detection rates among sample typeswere variable, lymphoid tissue had the highest detection rate (18.7%). Two ORF2 sequences wereobtained from lymphoid tissue samples and had 96.36–98.98% nucleotide identity with referencesequences. Direct detection of PCV4 by RNAscope revealed viral replication in B lymphocytes andmacrophages in lymph node germinal centers and histiocytic and T lymphocyte infiltration in thelamina propria of the small intestine. PCV4 detection was most commonly observed in nursery tofinishing aged pigs displaying respiratory and enteric disease. Coinfection with PCV2, PCV3, and otherendemic pathogens was frequently observed, highlighting the complex interplay between differentPCVs and their potential roles in disease pathogenesis. This study provides insights into the frequencyof detection, tissue distribution, and genetic characteristics of PCV4 in the US.Fil: Kroeger, Molly. University of Iowa; Estados UnidosFil: Vargas Bermudez, Diana S.. Universidad Nacional de Colombia. Sede Bogotá; ColombiaFil: Jaime, Jairo. Universidad Nacional de Colombia. Sede Bogotá; ColombiaFil: Parada, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Groeltz, Jennifer. University of Iowa; Estados UnidosFil: Gauger, Philip. University of Iowa; Estados UnidosFil: Piñeyro, Pablo. University of Iowa; Estados Unido

Magnetic micro-swimmers propelling through bio-rheological liquid bounded within an active channel

The dynamics of a micro-organism swimming through a channel with undulating walls subject to constant transverse applied magnetic field is investigated. The micro-organism is modeled as self-propelling undulating sheet which is out of phase with the channel waves while the electrically conducting biofluid (through which micro-swimmers propel) is characterized by the non-Newtonian shear-rate dependent Carreau fluid model. Creeping flow is mobilized in the channel due to the self-propulsion of the micro-organism and the undulatory motion of narrow gapped walls. Under these conditions the conservation equations are formulated under the long wavelength and low Reynolds number assumptions. The speed of the self-propelling sheet and the rate of work done at higher values of rheological parameters are obtained by using a hybrid numerical technique (MATLAB routine bvp-4c combined with a modified Newton-Raphson method). The results are validated through an alternative hybrid numerical scheme (implicit finite difference method (FDM) in conjunction with a modified Newton-Raphson method). The assisting role of magnetic field and rheological effects of the surrounding biofluid on the swimming mode are shown graphically and interpreted at length. The global behavior of biofluid is also expounded via visualization of the streamlines in both regions (above and below the swimming sheet) for realistic micro-organism speeds. The computations reveal that optimal swimming conditions for the micro-organism (i.e., greater speed with lower energy losses) are achievable in magnetohydrodynamic (MHD) environments including magnetic field-assisted cervical treatments. Keywords: Micro-organism; peristaltic (active) channel; Carreau fluid; Swimming speed; biomagnetohydrodynamics (bioMHD); Rate of work done; Hybrid numerical method, Newton-Raphson method; Cervical magnetic therap

The Value of Intraoperative Parathyroid Hormone Monitoring in Localized Primary Hyperparathyroidism: A Cost Analysis

Minimally invasive parathyroidectomy (MIP) is the preferred approach to primary hyperparathyroidism (PHPT) when a single adenoma can be localized preoperatively. The added value of intraoperative parathyroid hormone (IOPTH) monitoring remains debated because its ability to prevent failed parathyroidectomy due to unrecognized multiple gland disease (MGD) must be balanced against assay-related costs. We used a decision tree and cost analysis model to examine IOPTH monitoring in localized PHPT. Literature review identified 17 studies involving 4,280 unique patients, permitting estimation of base case costs and probabilities. Sensitivity analyses were performed to evaluate the uncertainty of the assumptions associated with IOPTH monitoring and surgical outcomes. IOPTH cost, MGD rate, and reoperation cost were varied to evaluate potential cost savings from IOPTH. The base case assumption was that in well-localized PHPT, IOPTH monitoring would increase the success rate of MIP from 96.3 to 98.8%. The cost of IOPTH varied with operating room time used. IOPTH reduced overall treatment costs only when total assay-related costs fell below $110 per case. Inaccurate localization and high reoperation cost both independently increased the value of IOPTH monitoring. The IOPTH strategy was cost saving when the rate of unrecognized MGD exceeded 6$12,000 (compared with initial MIP cost of $3733). Setting the positive predictive value of IOPTH at 100% and reducing the false-negative rate to 0% did not substantially alter these findings. Institution-specific factors influence the value of IOPTH. In this model, IOPTH increased the cure rate marginally while incurring approximately 4% additional cost

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.publishedVersio

Emulating Aerosol Microphysics with Machine Learning

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average score of . On a GPU we achieve a speed-up of 120 compared to the original model

Emulating Aerosol Microphysics with Machine Learning

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average score of . On a GPU we achieve a speed-up of 120 compared to the original model

Physics-informed learning of aerosol microphysics

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input–output pairs to train a neural network (NN) on it. We are able to learn the variables’ tendencies achieving an average R² score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model