Strong- vs. weak-coupling pictures of jet quenching: a dry run using QED

High-energy partons ($E \gg T$) traveling through a quark-gluon plasma lose energy by splitting via bremsstrahlung and pair production. Regardless of whether or not the quark-gluon plasma itself is strongly coupled, an important question lying at the heart of philosophically different approaches to energy loss is whether the high-energy partons of an in-medium shower can be thought of as a collection of individual particles, or whether their coupling to each other is also so strong that a description as high-energy `particles' is inappropriate. We discuss some possible theorists' tests of this question for simple situations (e.g. an infinite, non-expanding plasma) using thought experiments and first-principles quantum field theory calculations (with some simplifying approximations). The physics of in-medium showers is substantially affected by the Landau-Pomeranchuk-Midgal (LPM) effect, and our proposed tests require use of what might be called `next-to-leading order' LPM results, which account for quantum interference between consecutive splittings. The complete set of such results is not yet available for QCD but is already available for the theory of large-$N_f$ QED. We therefore use large-$N_f$ QED as an example, presenting numerical results as a function of $N_f\alpha$, where $\alpha$ is the strength of the coupling at the relevant high-energy scale characterizing splittings of the high-energy particles.Comment: 31 pages + appendices for 48 pages total, 21 figures. [Difference from version 2: Main change was to eliminate some summary formulas of NLO rates in section III.B, made unnecessary by a clear summary of formulas having been added to ref. [13].

Type 3 T helper cell and myeloid derived suppressor cell population dynamics in a mammary carcinoma model

Immunotherapies that augment Type I immunity show robust responses in diffuse blood cancers yet remain relatively ineffective in breast and other solid tumor malignancies. Breast tumor resistance to immunotherapies is associated with polarization towards pro-tumor Type 2 immunity, as well as the expansion of a myeloid derived suppressor cell (MDSC) population that inhibits Type 1 T helper (Th) and CD8+ cytotoxic T cells. Does polarization toward Type 3 immunity play a role in mammary tumor formation? This question had not been investigated prior to these studies despite established relationships between MDSCs and Type 3 Th cells in other inflammatory pathologies. Therefore, we investigated involvement of Type 3 Th cells (Th17 and Th22) and their association with expanding MDSC populations in the 4T1 mouse mammary carcinoma model. When evaluated at multiple time points after 4T1 injection (days 7, 14, 21, and 28), tumor infiltration of Th17 and Th22 cells was first detected at d 14, and Th17 populations declined after this time while Th22 remained unchanged. In peripheral organs, Th17 increased by d 7 before declining, while Th22 were not elevated until later times. Only Th17 and MDSC expansion in the bone marrow were positively correlated, suggesting further that Th17 and Th22 are functionally distinct lineages and that MDSCs may play a role in Th17 fate determination in breast cancer. To further address a possible relationship between MDSCs and Type 3 Th cells in mammary carcinoma, we used CRISPR-Cas9 to knock out tumor cell-specific production of interleukin (IL) -6 (IL6-KO), which functions in Th maturation, myelopoiesis, and MDSC recruitment. Tumor-resident Th17, Th22, and MDSCs did not change in IL-6 KO tumors, suggesting a limited role for IL-6 in local recruitment. However, induction of Th22 and MDSCs in peripheral tissues was significantly reduced with IL6-KO tumors, while Th17 cells were increased. These concomitant changes in peripheral Type 3 Th and MDSCs suggests direct functional interactions between these populations, yet additional studies are required to confirm this. To conclude, we identify and characterize a pro-tumor Type 3 Th immune response that accompanies MDSC expansion in a model of metastatic breast cancer. This is important because these populations are associated with reduced efficacy of cancer immunotherapies

Sentiment Analysis of Afaan Oromoo Facebook Media Using Deep Learning Approach

The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger.  This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits. The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured. Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products. Therefore, sentiment analysis has increasingly become a major area of research interest in the field of Natural Language Processing and Text Mining. In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative. To the best of the researcher’s knowledge, there is no Deep learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content. Therefore, in this study, we focused on investigating Convolutional Neural Network and Long Short Term Memory deep learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments. To this end, a total of 1452 comments collected from the official site of the Facebook page of Oromo Democratic Party/ODP for the study. After collecting the data, manual annotation is undertaken. Preprocessing, normalization, tokenization, stop word removal of the sentence are performed. We used the Keras deep learning python library to implement both deep learning algorithms. Long Short Term Memory and Convolutional Neural Network, we used word embedding as a feature. We conducted our experiment on the selected classifiers. For classifiers, we used 80% training and 20% testing rule. According to the experiment, the result shows that Convolutional Neural Network achieves the accuracy of 89%. The Long Short Memory achieves accuracy of 87.6%. Even though the result is promising there are still challenges. Keywords: Sentiment Analysis; Opinionated Afaan Oromoo facebook comments; Oromo Democratic Party Facebook page DOI: 10.7176/NMMC/90-02 Publication date:May 31st 202

Curricula for Teaching MRI Safety and MRI/CT Contrast Safety To Residents: How Effective Are Live Lectures and Online Modules?

Purpose The advent of the diagnostic radiology core examination and the new ACGME “milestone” evaluation system for radiology residents places new emphasis on topics in MRI and CT safety, and MRI and CT contrast agents. We evaluated whether either lecture-based teaching or online modules would improve baseline resident knowledge in these areas, and assessed which intervention was more effective. Methods Before didactic intervention, 2 cohorts were created from 57 radiology residents, with equal numbers and a matched level of training. The residents were tested on their baseline knowledge of MRI, MRI contrast safety, and CT contrast safety, using a multiple-choice examination. One group attended a live, 1-hour lecture on the preceding topics. The other engaged in 3 short online educational modules. After 6 weeks, the residents were again tested with the same questions to assess for improvement in their understanding. Results Both the module and lecture cohorts demonstrated a statistically significant increase in questions answered correctly on CT contrast safety (13.1%, P < .001, and 19.1%, P < .001, respectively), and on MRI and MRI contrast safety (12.9%, P < .001, and 14.4%, P < .001). The preintervention and postintervention scores, and degree of improvement postintervention, were similar for the module versus lecture groups, without a statistically significant difference (P = .70). Resident confidence improved in both groups, for both modalities. Conclusions Focused didactic intervention improves resident knowledge of MRI and CT safety, and MRI and CT contrast agents. Live lectures and online modules can be equally effective, allowing residency programs flexibility

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