Compression ratio: a novel method to quantify compressibility as a diagnostic measurement in giant cell arteritis.

Objectives: Ultrasonographic assessment of giant cell arteritis (GCA) relies on the demonstration of a non-compressible halo. Several ultrasonographic methods have been developed to quantify arterial wall thickness, however arterial compressibility has not been quantified. This study presents a possible solution for quantifying compressibility to assist in diagnosing GCA.

Data-Driven Strategies for Carbimazole Titration: Exploring Machine Learning Solutions in Hyperthyroidism Control.

Background: University Hospitals Dorset (UHD) has over 1,000 thyroid patient contacts annually. These are primarily patients with autoimmune hyperthyroidism treated with Carbimazole titration. Dose adjustments are made by a healthcare professional (HCP) based on the results of thyroid function tests, who then prescribes a dose and communicates this to the patient via letter.

Dual Input Stream Transformer for Vertical Drift Correction in Eye-Tracking Reading Data.

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against eleven classical approaches on a comprehensive suite of nine diverse datasets.

Evidence of humans in North America during the Last Glacial Maximum.

Archaeologists and researchers in allied fields have long sought to understand human colonization of North America. Questions remain about when and how people migrated, where they originated, and how their arrival affected the established fauna and landscape. Here, we present evidence from excavated surfaces in White Sands National Park (New Mexico, United States), where multiple in situ human footprints are stratigraphically constrained and bracketed by seed layers that yield calibrated radiocarbon ages between ~23 and 21 thousand years ago.

Sexing white 2D footprints using convolutional neural networks.

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture.

Technological innovation in the recovery and analysis of 3D forensic footwear evidence: Structure from motion (SfM) photogrammetry.

The recovery of three-dimensional footwear impressions at crime scenes can be a challenge but can also yield important investigative data. Traditional methods involve casting 3D impressions but these methods have limitations: the trace is usually destroyed during capture; the process can be time consuming, with a risk of failure; and the resultant cast is bulky and therefore difficult to share and store. The use of Structure from Motion (SfM) photogrammetry has been used widely to capture fossil footprints in the geological record and while there is a small body of work advocating its use in forensic practice the full potential of this technique has yet to be realised in an operational context.

Recovery via SfM photogrammetry of latent footprint impressions in carpet.

Impression evidence retained in carpet is usually recovered, if at all, in two dimensions via a vertical photograph. Here, we show that recovery is also possible via SfM photogrammetry and this gives good quality results that allow digital measurements both in the x-y plane and by depth (z-axis). This study focuses on recovery from polypropylene carpets which are widespread due to their resistance to wear and low cost.

Do readers use character information when programming return-sweep saccades?

Reading saccades that occur within a single line of text are guided by the size of letters. However, readers occasionally need to make longer saccades (known as return-sweeps) that take their eyes from the end of one line of text to the beginning of the next. In this study, we tested whether return-sweep saccades are also guided by font size information and whether this guidance depends on visual acuity of the return-sweep target area.

Proof of concept for a syndromic surveillance system based on routine ambulance records in the South West of England, for the influenza season 2016/2017.

Introduction: The introduction of electronic patient records in the ambulance service provides new opportunities to monitor the population. Approximately 36% of patients presenting to English ambulance services are discharged at scene. Ambulance records are therefore an ideal data source for syndromic early event detection systems to monitor infectious disease in the pre-hospital population.

Footprints preserve terminal Pleistocene hunt? Human-sloth interactions in North America.

Predator-prey interactions revealed by vertebrate trace fossils are extremely rare. We present footprint evidence from White Sands National Monument in New Mexico for the association of sloth and human trackways. Geologically, the sloth and human trackways were made contemporaneously, and the sloth trackways show evidence of evasion and defensive behavior when associated with human tracks.

Stat-tracks and mediotypes: powerful tools for modern ichnology based on 3D models.

Vertebrate tracks are subject to a wide distribution of morphological types. A single trackmaker may be associated with a range of tracks reflecting individual pedal anatomy and behavioural kinematics mediated through substrate properties which may vary both in space and time. Accordingly, the same trackmaker can leave substantially different morphotypes something which must be considered in creating ichnotaxa.

Metalearning: a survey of trends and technologies.

Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

Can we identify non-stationary dynamics of trial-to-trial variability?

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience.

Density-preserving sampling: robust and efficient alternative to cross-validation for error estimation.

Estimation of the generalization ability of a classification or regression model is an important issue, as it indicates the expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures, such as cross-validation (CV) or bootstrap, are stochastic and, thus, require multiple repetitions in order to produce reliable results, which can be computationally expensive, if not prohibitive. The correntropy-inspired density-preserving sampling (DPS) procedure proposed in this paper eliminates the need for repeating the error estimation procedure by dividing the available data into subsets that are guaranteed to be representative of the input dataset.

Robust predictive modelling of water pollution using biomarker data.

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels. The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required 'under the hood' knowledge.