Dizziness in adults with chronic otitis media at two otology referral centres in Colombia: a cross-sectional study in a middle-income country.

OBJECTIVE: This study aimed to determine the prevalence of dizziness and its associated factors in patients with COM at two otologic referral centres in a middle-income country. DESIGN: Cross-sectional study. Adults with and without COM diagnosis from two otology-referral centres in Bogotá (Colombia) were included. Dizziness and quality of life were assessed using the "Chronic Suppurative Otitis Media Questionnaire-12" (COMQ-12), and sociodemographic questionnaires were applied. Otoscopic evaluation and audiometric data were collected. STUDY SAMPLE: A total of 231 adults. RESULTS: Of the 231 participants, up to 64.5% (n = 149) reported at least mild inconvenience due to dizziness. Factors associated with dizziness included female sex (aPR: 1.23; 95% CI: 1.04-1.46), chronic suppurative otitis media (aPR: 3.02; 95% CI: 1.21-7.52), and severe tinnitus (aPR: 1.75; 95% CI: 1.24-2.48). An interaction was found between socioeconomic status and educational level, with more frequent reports of dizziness in the middle/high economic status and secondary education (aPR: 3.09; 95% CI: 0.52-18.55; p < 0.001). Differences of 14 points in symptom severity and 18.5 points in the total score of the COMQ-12 were found between the groups with dizziness and without dizziness. CONCLUSIONS: Dizziness was frequent in patients with COM and was associated with severe tinnitus and quality of life deterioration.

Morphology and diet are decoupled in nearshore notothenoids from King George Island, West Antarctica.

Antarctic notothenioid fishes show wide adaptive morphological radiation, linked to habitat preferences and food composition. However, direct comparisons of phenotypic variability and feeding habits are still lacking, particularly in stages inhabiting nearshore areas. To assess these relationships, we collected juveniles and adults of the most common benthic species inhabiting shallow waters off the South Shetland Islands within a similar size range, the plunderfish Harpagifer antarcticus, the black rockcod Notothenia coriiceps, and the marbled rockcod Notothenia rossii. Individual size ranges varied from 44.0 to 98.9 mm standard length (LS) (H. antarcticus), from 95.8 to 109.3 mm LS (N. coriiceps), and from 63.0 to 113.0 mm LS (N. rossii). Notothenioid fish showed different morphospace variability, being larger for H. antarcticus than the other Notothenia species and associated with the position of the posterior end of the operculum, along with the location and relative size of the eye. The evolutionary allometry was low, but the static allometry was much higher, especially for H. antarcticus and N. rossii. The diet was mainly carnivorous, consisting of amphipods and euphausiids. Macroalgae were scarce or totally absent in the gut contents of all species. Only H. antarcticus showed an increase in the prey number and ingested prey volume with fish size. Finally, there was a significant covariation between shape changes and LS in all species (allometric effects), however, not with prey composition, probably due to the small size range or ontogenetic stage and the relative similarity (or lack of contrast) in the benthic environment that they utilized.

Corrigendum: Applications and techniques for fast machine learning in science.

[This corrects the article DOI: 10.3389/fdata.2022.787421.].

Lorentz group equivariant autoencoders.

There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group SO+(3,1), with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

How to stratify the risk of thrombosis for esthetic plastic surgery? The proposal of a new scale.

Scales to qualify the risk of thrombosis do not include all thrombogenic factors that are generated in esthetic plastic surgery. Methods: We performed a systematic review to assess the risk of thrombosis in plastic surgery. Thrombogenic factors in esthetic surgery were analyzed by a panel of experts. We proposed a scale with 2 versions. In the first version, factors were stratified according to their impact on the possible risk of thrombosis. The second version includes the same factors but in a simplified form. We evaluated the efficacy of the proposed scale by comparing it with the Caprini score; we scored the risk in 124 cases and controls. Results: Using the Caprini score, we found that 81.45% of the patients studied and 62.5% of the cases of thrombosis were observed in the low-risk group. Only 1 case of thrombosis was reported in the high-risk group. Using the stratified version of the scale, we found that the low-risk group comprised 25% of the patients, and there were no cases of thrombosis. The high-risk group included 14.51% of patients; 10 presented thrombosis (62.5%). The proposed scale was very effective in detecting both low-risk and high-risk patients undergoing esthetic surgery procedures.

Applications and Techniques for Fast Machine Learning in Science.

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science-the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

Graph Neural Networks for Charged Particle Tracking on FPGAs.

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph-nodes represent hits, while edges represent possible track segments-and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows.

We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

A FAIR and AI-ready Higgs boson decay dataset.

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference.

Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption. Two popular techniques for reducing computation in neural networks are pruning, removing insignificant synapses, and quantization, reducing the precision of the calculations. In this work, we explore the interplay between pruning and quantization during the training of neural networks for ultra low latency applications targeting high energy physics use cases. Techniques developed for this study have potential applications across many other domains. We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics. We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task. Further, quantization-aware pruning typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization. Surprisingly, while networks with different training configurations can have similar performance for the benchmark application, the information content in the network can vary significantly, affecting its generalizability.

What is the Process for Breast Implant Manufacturing? Inside Eight Breast Implant Factories.

BACKGROUND: With the recent association between breast implants and anaplastic large cell lymphoma, breast implants have become the focus of many warnings. Surgeons and health professionals are not involved in all the processes of the manufacturing and distribution of this product. Not all countries have breast implant factories that are easy for surgeons to visit and better understand the manufacturing process. METHODS: A questionnaire about breast implant manufacturing and distribution was validated in consensus and form. Two plastic surgeons visited eight factories and administered the questionnaire in the presence of a photographer, who documented that the questionnaire was answered in the same way for all visits. Once the visitors finished obtaining the information (questionnaire responses and video recording), this information was validated by a different member of a safety committee in Mexico. For the observations to be considered valid, the information from the questionnaire and the video must be presented. RESULTS: We visited eight factories: three in France (Sebbin, Arion and Eurosilicone), two in Costa Rica (Allergan and Motiva), one in Scotland (Nagor), one in Germany (Polytech) and one in Korea (Bellagel). In four factories (Eurosilicone, Motiva, Nagor and Sebbin), the information on the process for manufacturing an implant was observed and recorded (validated). The quality laboratory was visited, and video recording was performed in six factories (Bellagel, Eurosilicone, Motiva, Nagor, Polytech and Sebbin). CONCLUSION: It was possible to observe and verify that most of the companies that distribute breast implants in Mexico perform their manufacturing processes according to ISO standards. A breast implant registry can help people further understand how BIA-ALCL will behave in the future and allow more tests to better understand this pathology. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one µs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

Niche partitioning and morphospace in early stages of two sympatric Diogenichthys species (Myctophidae).

Diet and morphospace of larval stages of two sympatric lanternfish Diogenichthys atlanticus and D. laternatus from the south-east Pacific Ocean were compared and the covariance between both variables was assessed for each species. Diogenichthys atlanticus stomach contents consisted mainly of copepod nauplii and digested remains and this species had a broader niche than D. laternatus, in which stomach contents were highly digested. No dietary overlap was found between both species. The covariance between skull shape and diet for D. atlanticus was given by a wider mouth gape related to the presence of copepod nauplii, whilst for D. laternatus, a shorter snout and posteriorly displaced eye were related to the presence of highly digested stomach contents. Interspecific differences between diets and skull shapes suggest that both species may have undergone morphological or niche divergence to avoid competition, such as feeding at different hours or depth stratification.

Localización inusual de lesiones líticas de mieloma múltiple con resonancia magnética. Reporte de dos casos / Unusual location of multiple myeloma lytic lesions with magnetic resonance imaging. Report of two cases

Resumen: El mieloma múltiple (MM) es una enfermedad maligna hematológica que se caracteriza por la proliferación de células plasmáticas monoclonales en la médula ósea. La prueba de diagnóstico estándar de oro para MM es un aspirado y/o biopsia de médula ósea (MO), que define la cantidad de células plasmáticas atípicas y constituye la base del sistema de clasificación diagnóstica del Grupo de Trabajo Internacional del Mieloma. Las lesiones del MM en tejido óseo son líticas y su localización más frecuente es en la columna vertebral, pelvis, cráneo y costillas. Aunque las lesiones óseas predominan en estas regiones del esqueleto y en las extremidades proximales también se presentan en menor proporción en codos, rodillas y escápula. En los estudios por imagen la radiografía simple es el principal estudio diagnóstico en la detección de cambios óseos destructivos por MM, sin embargo tiene baja sensibilidad. El estudio de resonancia magnética (RM) es el estudio de elección por tener mayor sensibilidad y especificidad para el diagnóstico de esta enfermedad. Objetivo: Informar 2 casos con infiltración por mieloma múltiple en regiones inusuales.

Abstract: Multiple myeloma (MM) is an hematologic malignancy characterized by the proliferation of malignant monoclonal plasma cells in the bone marrow. The diagnostic test for MM is a bone marrow aspirate or biopsy to define the amount of atypical plasma cells and it is the basis of the diagnostic classification system of the International Working Group Myeloma. MM lesions are lytic bone tissue and are most frequently located in the spine, pelvis, skull and ribs. Bone lesions predominate in these regions of the skeleton and proximal extremities but may occur to a lesser extent on elbows, knees and scapula. In imaging studies plain radiography remains the primary diagnostic study in detecting destructive bone changes multiple myeloma, however the MRI study is the study of choice because the sensitivity and specificity for the diagnosis of this disease. Objectives: Report two cases with multiple myeloma infiltration in unusual regions.