Etiquette for medical students email communication with faculty members: a single-institution study

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background Email is widely used as a means of communication between faculty members and students in medical education because of its practical and educational advantages. However, because of the distinctive nature of medical education, students inappropriate email etiquette may adversely affect their learning as well as faculty members perception of them. Little data on medical students competency in professional email writing is available; therefore, this study explored the strengths and weaknesses of medical students email etiquette and factors that contribute to professional email writing. Methods A total of 210 emails from four faculty members at Seoul National University College of Medicine were collected. An evaluation criteria and a scoring rubric were developed based on the various email-writing guidelines. The rubric comprised 10 items, including nine items for evaluation related to the email components and one item for the assessment of global impression of politeness. Three evaluators independently assessed all emails according to the criteria. Results Students were identified as being 61.0 % male and 52.8 % were in the undergraduate-entry program. The sum of each component score was 62.21 out of 100 and the mean value for global impression was 2.6 out of 4. The results demonstrated that students email etiquettes remained low-to-mediocre for most criteria, except for readability and honorifics. Three criteria, salutation (r=0.668), closing (r=0.653), and sign-off (r=0.646), showed a strong positive correlation with the global impression of politeness. Whether a student entered a graduate-entry program or an undergraduate-entry program significantly contributed to professional email writing after other variables were controlled. Conclusions Although students in the graduate-entry program demonstrated a relatively superior level of email etiquette, the majority of medical students did not write emails professionally. Educating all medical students in email etiquette may well contribute to the improvement of student–faculty relationships as well as their email writing

Searching for Clues for a Matter Dominated Universe in Liquid Argon Time Projection Chambers

Liquid Argon Time Projection Chambers (LArTPCs) represent one of the most widely utilized neutrino detection techniques in neutrino experiments, for instance, in the Short Baseline Neutrino (SBN) program and the future large-scale LArTPC: Deep Underground Neutrino Experiment (DUNE). The high-end technique, facilitating excellent spatial and calorimetric reconstruction resolution, also enables testing exotic Beyond Standard Model (BSM) theories, such as baryon number violation (BNV) processes (e.g., proton-decay, neutron-antineutron oscillation). At the same time, Machine Learning (ML) techniques have demonstrated their ubiquitous use in recent decades; ML techniques have also become some of the most powerful tools in high-energy physics (HEP) analyses. Furthermore, the development of algorithms to cater to the needs of problems in HEP (i.e., triggering, reconstruction, improving sensitivity, etc.) has also become an active area of research. By developing a combined approach using Convolutional Neural Network (CNN) and Boosted Decision Tree (BDT) techniques, the sensitivity of neutron-antineutron oscillation in DUNE is evaluated for a projected exposure of 400kton⋅ years. Additionally, to meet the triggering requirement to select such rare events in DUNE, such a search is only supported with highly efficient self-triggering algorithms. An ML-based self-triggering scheme for large-scale LArTPCs, such as DUNE, is also developed with the intention of implementation on field-programmable gate arrays (FPGAs). The ML-based approach for searching for neutron-antineutron oscillation can be demonstrated and validated on the current LArTPC MicroBooNE. The analysis in MicroBooNE represents the first-ever search for neutron-antineutron oscillation in a LArTPC. DUNE's projected 90％ C.L. sensitivity to the neutron antineutron oscillation lifetime is 6.45✕10³² years, assuming 1.327✕10³⁵ neutron⋅ years, equivalent to 10 years of DUNE far detector exposure (400kton⋅ years). For MicroBooNE, assuming 372 seconds of exposure (equivalent to 3.13✕10³⁶ neutron⋅ years), the 90% C.L. lifetime sensitivity is found at 3.07✕10²⁵ yrs, after accounting for Monte-Carlo statistical uncertainty and systematic uncertainty from detector effects

Real-Time Inference With 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-Rate Particle Imaging Detectors

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network quantization is further used to minimize the computing resource utilization of the network. We use “High Level Synthesis for Machine Learning” (hls4ml) tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is an FPGA technology proposed for use in the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) particle detector. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the currently planned DUNE data acquisition system. This represents the first-ever exploration of employing 2D CNNs on FPGAs for DUNE.</jats:p

Image_1_Clinical implications of the tumor microenvironment using multiplexed immunohistochemistry in patients with advanced or metastatic renal cell carcinoma treated with nivolumab plus ipilimumab.tif

PurposeImmune checkpoint inhibitors (ICIs) such as nivolumab and ipilimumab (N/I) are important treatment options for advanced renal cell carcinoma (RCC). The tumor microenvironment (TME) in these ICI-treated patients is largely unknown.MethodsTwenty-four patients treated with N/I between July 2015 and June 2020 were analyzed. Multiplexed immunohistochemistry (mIHC) was conducted to define the TME, including various T cell subsets, B cells, macrophages, and dendritic cells.ResultsThe median age of the study patients was 61 years (range, 39–80) and 75.0% of these cases were men. The objective response rate with N/I was 50.0%. The densities of the CD8+ cytotoxic T cells (P=0.005), specifically CD137+ CD8+ T cells (P=0.017), Foxp3- CD4+ helper T cells (P=0.003), Foxp3+ CD4+ regulatory T cells (P=0.045), CD68+ CD206- M1 macrophages (P=0.008), and CD68+ CD206+ M2 macrophages (P=0.021) were significantly higher in the treatment responders. At a median follow-up duration of 24.7 months, the median progression-free survival (PFS) was 11.6 months. The high densities (≥median) of Foxp3- CD4+ helper T cells (P=0.016) and CD68+ CD206- M1 macrophages (P=0.008) were significantly associated with better PFS, and the density of CD137+ CD8+ cytotoxic T cells (P=0.079) was marginally associated with better PFS. After multivariate analysis, the higher density of Foxp3- CD4+ helper T cells was independently associated with better PFS (hazard ratio 0.19; P=0.016).ConclusionThe properties and clinical implications of the TME properties in RCC indicate that Foxp3- CD4+ helper T cells, M1 macrophages, and CD137+ CD8+ T cells are potential predictive biomarkers and treatment targets.</p