Study of spacecraft transponder power amplifier Final report

Communications satellite wideband transponder feasibility study with direct RF to RF CONVERSION and TWT in re-entrant mod

BISC 103 Antibiotic Resistance Lab

BISC 103 Inquiry into Life, Laboratory I (accompanies BISC 102

Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 mu m was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.U.S. National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) [DGE-1143953]; U.S. National Institutes of Health - National Institute of Environmental Health Sciences (NIH-NIEHS) [R25ES025494]; Western Alliance to Expand Student Opportunities (WAESO) at Arizona State University; U.S. National Institutes of Health -National Institute of General Medical Sciences (NIH-NIGMS) [T32GM084905]; Korea Institute of Ocean Science and Technology (KIOST)Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Community Acquired Pneumonia Order Set Utilization Evaluation: A Retrospective Chart Review

Presented at the ASHP Midyear Clinical Meeting. December 2023. This poster evaluated adherence of antimicrobial regimen selection using a community acquired pneumonia (CAP) order set updated to Infectious Disease Society of America/American Thoracic Society guidelines

Optimization of beta-lactam dosing in critically ill patients with continuous veno-venous hemofiltration or augmented renal clearance.

Presented at the ASHP Midyear Clinical Meeting. New Orleans, LA This evaluation found that traditional dosing of beta-lactams found in practice is often (20 to 90%) suboptimal when compared to ideal evidence-based dosing, that optimizes efficacy while limiting adverse effects from drug toxicity

Issue 15: Pulmonary & Critical Care Insider

Pulmonary & Critical Care Insider Issue 15 Compiled by Bharat Bajantri, MD, and librarian Sarah Ellsworth, MLS for the clinicians of the Pulmonary and Critical Care team of Parkview. The Pulmonary & Critical Care Insider newsletter was created by Dr. Bharat Bajantri, MD and Sarah Ellsworth, MLS in 2023 as a form of current awareness for current practice at our hospital, Parkview Health. Topics: 2025 Guidelines for ANCA Vasculitis Airway Scaffolds for Emphysema: Six-Month BREATHE TRIAL Results Tackling Emphysema-Related Hyperinflation: Scaffolds, Valves, or Vapor? How They Work? Bactericidal vs. Bacteriostatic Antibiotics in Critically Ill Patients Modified Cuff Leak Test (CLT) and Post-Extubation Stridor 2025 PADIS Guideline Updates Haloperidol is Safe- No Extra Arrhythmia Risk. A BREATHE of Fresh new antifibrotics? What You Eat is What You Breathe! :Systemic Inflammation, Diet Quality, and COPD Outcomes Rewriting the Playbook: Molecular Subtypes and Targeted Therapy in Small Cell Lung Cancer Segmentectomy, Lobectomy, or Wedge Resection in Stage IA NSCLC: What Does the Evidence Say

TimeNorm: a novel normalization method for time course microbiome data

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis

Teachers\u27 Experiences Implementing the 2018 Mississippi College and Career Readiness Standards for Science

Teachers have had to use standards to teach with since the 1980’s. The CCRS has made a significant shift in how standards are written and the expectations of students in learning them. With the three-dimensional design, teachers are required to change their pedagogy in order to implement the standards with fidelity. That requires training, support, and resources for the teachers. The purpose of this study was to provide insight in the experiences of high school teachers in Mississippi as they implemented the standards. Additionally, the researcher revealed emotions related to implementation and differences related to teachers in a tested subject and teachers in a non-tested subject. To implement standards effectively, teachers needed training, support, resources, and time for reflection

Normalization Methods on Single-Cell RNA-Seq Data and Metagenomics Data

DNA and RNA sequencing uncover the genomes and transcriptomes of cells, permitting greater understanding of the biological processes that fuel them and their relationship to one another. Advances in sequencing technology have expanded such studies to include both single-cell RNA sequencing, which analyzes cells individually instead of in bulk, and metagenomics, which describes the microbial composition of ecosystems found both in nature and in the human body. Normalization is required to accurately assess the genetic content of cells, adjusting for uneven sample sizes, stochasticity due to low input material, and dropout from uneven RNA amplification, all of which obscure the ground truth of a sample. This dissertation presents two novel normalization methods: one for single-cell RNA sequencing and one for metagenomics sequencing. It opens with a survey of normalization methods on single-cell RNA-seq data to provide ample background on common practices among existing approaches. Weighted Between Groups Normalization (WeBe) is proposed to normalize single-cell RNA sequencing data by utilizing external spike-in RNAs to establish relationships both within and between cell conditions/groups/types; 2-Stage Scaling Normalization (2SS) is designed for metagenomics sequencing data, first normalizing within conditions before identifying a set of stable features across conditions, which are used for across conditions normalization. Simulation studies and real data analysis demonstrate the effectiveness of each new method.Release after 09/23/202