Iowa City Pathologies

This study is focused on the pathologies and skeletal abnormalities present among specimens from the Iowa City Collection, narrowed down to four individuals in particular. It compares abnormalities observed on the bones themselves with the written record from the inventory of this collection, identifying which pathology is most common among the specimens, why those pathologies leave their marks in bony tissue, and speculates about the possible origin of this collection before it was loaned to Iowa State from the University of Iowa to become part of the teaching collection for classes in skeletal biology and forensic anthropology

Long-term changes in acidification and recovery at nine calibrated catchments in Norway, Sweden and Finland

International agreements to reduce the emissions of acidifying pollutants have resulted in major changes in deposition of sulphur and nitrogen in southern Scandinavia over the past 25 years. Long-term monitoring of deposition and run-off chemistry over the past 12-25 years at nine small calibrated catchments in Finland, Norway and Sweden provide the basis for analysis of trends with special attention to recovery in response to decreased sulphur and nitrogen deposition in the 1980s and 1990s. During the 1980s and 1990s sulphate deposition in the region decreased by 30 to 60%, whereas inorganic nitrogen deposition showed very little change until the mid-1990s. Deposition of non-marine base cations (especially calcium) declined in the 1990s most markedly in southern Finland. Run-off response to these changes in deposition has been rapid and clear at the nine catchments. Sulphate and base cations (mostly calcium) concentrations declined and acid neutralising capacity increased. Occasional years with unusually high inputs of sea-salt confound the general trends. Trends at all the catchments show the same general picture as that from small lakes in Scandinavia and in acid-sensitive waters elsewhere in Europe.</p> <p style='line-height: 20px;'><b>Keywords: </b>acidification, recovery, Scandinavia, catchment, trend analysi

The specialized tomograph rotary table design in the T-Flex CAD system

Статья "Проектирование поворотного стола специализированного томографа в T-Fleх CAD" посвящена актуальной проблеме обеспечения правильного расположения объектов контроля в томографических комплексах. Представлен вариант конструкции поворотного стола, спроектированный в среде T-Fleх CAD.The article "Designing of the Rotary Table of the Specialized Tomograph in the T-Flex CAD system" is devoted to the urgent problem of the object correct arrangement control in a tomographic complex. The rotary table embodiment is designed with the T-Flex CAD system

Long-term changes in acidification and recovery at nine calibrated catchments in Norway, Sweden and Finland

International audienceInternational agreements to reduce the emissions of acidifying pollutants have resulted in major changes in deposition of sulphur and nitrogen in southern Scandinavia over the past 25 years. Long-term monitoring of deposition and run-off chemistry over the past 12-25 years at nine small calibrated catchments in Finland, Norway and Sweden provide the basis for analysis of trends with special attention to recovery in response to decreased sulphur and nitrogen deposition in the 1980s and 1990s. During the 1980s and 1990s sulphate deposition in the region decreased by 30 to 60%, whereas inorganic nitrogen deposition showed very little change until the mid-1990s. Deposition of non-marine base cations (especially calcium) declined in the 1990s most markedly in southern Finland. Run-off response to these changes in deposition has been rapid and clear at the nine catchments. Sulphate and base cations (mostly calcium) concentrations declined and acid neutralising capacity increased. Occasional years with unusually high inputs of sea-salt confound the general trends. Trends at all the catchments show the same general picture as that from small lakes in Scandinavia and in acid-sensitive waters elsewhere in Europe. Keywords: acidification, recovery, Scandinavia, catchment, trend analysi

Domain randomization using synthetic electrocardiograms for training neural networks

We present a method for training neural networks with synthetic electrocardiograms that mimic signals produced by a wearable single lead electrocardiogram monitor. We use domain randomization where the synthetic signal properties such as the waveform shape, RR-intervals and noise are varied for every training example. Models trained with synthetic data are compared to their counterparts trained with real data. Detection of r-waves in electrocardiograms recorded during different physical activities and in atrial fibrillation is used to assess the performance. By allowing the randomization of the synthetic signals to increase beyond what is typically observed in the real-world data the performance is on par or superseding the performance of networks trained with real data. Experiments show robust model performance using different seeds and on different unseen test sets that were fully separated from the training phase. The ability of the model to generalize well to hidden test sets without any specific tuning provides a simple and explainable alternative to more complex adversarial domain adaptation methods for model generalization. This method opens up the possibility of extending the use of synthetic data towards domain insensitive cardiac disease classification when disease specific a priori information is used in the electrocardiogram generation. Additionally, the method provides training with free-to-collect data with accurate labels, control of the data distribution eliminating class imbalances that are typically observed in health-related data, and the generated data is inherently private

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference

Supervised machine learning techniques have traditionally been very successful at reconstructing biological networks, such as protein-ligand interaction, protein-protein interaction and gene regulatory networks. Many supervised techniques for network prediction use linear models on a possibly nonlinear pairwise feature representation of edges. Recently, much emphasis has been placed on the correct evaluation of such supervised models. It is vital to distinguish between using a model to either predict new interactions in a given network or to predict interactions for a new vertex not present in the original network. This distinction matters because (i) the performance might dramatically differ between the prediction settings and (ii) tuning the model hyperparameters to obtain the best possible model depends on the setting of interest. Specific cross-validation schemes need to be used to assess the performance in such different prediction settings.In this work we discuss a state-of-the-art kernel-based network inference technique called two-step kernel ridge regression. We show that this regression model can be trained efficiently, with a time complexity scaling with the number of vertices rather than the number of edges. Furthermore, this framework leads to a series of cross-validation shortcuts that allow one to rapidly estimate the model performance for any relevant network prediction setting. This allows computational biologists to fully assess the capabilities of their models. The machine learning techniques with the algebraic shortcuts are implemented in the RLScore software package: https://github.com/aatapa/RLScore

Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review

Background: Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs).Objective: This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach.Methods: This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported.Results: A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.Conclusions: Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.</p