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Evaluation of the 3D bone morphology of the glenohumeral joint is necessary for pre-surgical planning. Zero echo time (ZTE) magnetic resonance imaging (MRI) provides excellent bone contrast and can potentially be used in the place of computed tomography. Segmentation of the shoulder anatomy, particularly the humeral head and the acetabulum, is needed for the detailed assessment of each anatomy and for pre-surgical preparation. In this study, we compared the performance of two popular deep learning models based on Google’s DeepLab and U-Net to perform automated segmentation on ZTE MRI of human shoulders. Axial ZTE images of normal shoulders (n = 31) acquired at 3-Tesla were annotated for training with DeepLab and 2D U-Net, and the trained model was validated with testing data (n = 13). While both models showed visually satisfactory results for segmenting the humeral bone, U-Net slightly over-estimated while DeepLab under-estimated the segmented area compared to the ground truth. Testing accuracy quantified by Dice score was significantly higher (p < 0.05) for U-Net (88%) than DeepLab (81%) for the humeral segmentation. We have also implemented the U-Net model onto an MRI console for push-button DL segmentation processing. Although this is an early work with limitations, our approach has the potential to improve shoulder MR evaluation hindered by manual post-processing and may provide clinical benefit for quickly visualizing bones of the glenohumeral joint.

Direct imaging of semi-solid lipids, such as myelin, is of great interest as a noninvasive biomarker of neurodegenerative diseases. Yet, the short T2 relaxation times of semi-solid lipid protons hamper direct detection through conventional magnetic resonance imaging (MRI) pulse sequences. In this study, we examined whether a three-dimensional ultrashort echo time (3D UTE) sequence can directly acquire signals from membrane lipids. Membrane lipids from red blood cells (RBC) were collected from commercially available blood as a general model of the myelin lipid bilayer and subjected to D2O exchange and freeze-drying for complete water removal. Sufficiently high MR signals were detected with the 3D UTE sequence, which showed an ultrashort T2* of ∼77–271 µs and a short T1 of ∼189 ms for semi-solid RBC membrane lipids. These measurements can guide designing UTE-based sequences for direct in vivo imaging of membrane lipids.

Numerous models have been proposed to describe and predict how human translations evolve in time. Some of these models suggest hierarchically embedded processes of fast and slow processing that unfold on different timelines. However, the assumed mental processes have often been conceptualized without a clear description of how they can be assessed, measured or retrieved in behavioral data. Other approaches suggest fragmenting behavioral data into various kinds of units, but the status of these units with respect to their cognitive reality is not always very clear.In this paper, we propose a novel annotation taxonomy for behavioral data, assuming three broad states that translators experience during translation production: A state of orientation (O) reflects the experience of epistemic foraging in which a translator reads or scans a piece of the source text (ST) or searches for information. In a flow state (F), a translator engages in fluent translation production that is characterized by focus and involvement in the production process. A state of hesitation (H) can be described in terms of uncertainty or doubt which results in patterns of re-reading, text modification and disfluent translation production. This novel HOF taxonomy aims at describing mental states that elicit different experiential qualities during the translation process, associated with typical behavioral patterns that can be retrieved in recorded translation process data (TPD, i.e., logged keystrokes and gaze data).We describe a manual annotation of a small set of TPD with the HOF taxonomy and we develop a method that ensures high inter-rater agreement (kappa 0.88). We show how our HOF translation states cluster into higher-level translation strategies (so-called policies). We discuss how these policies are optimized during the translation process and how translation states trigger off lower-level translation processes. We compare our taxonomy with other annotation approaches and propose a deep-temporal architecture that assumes a hierarchy of embedded translation processes that interact in various ways.

This paper develops an outline for a hierarchically embedded architecture of an artificial agent that models human translation processes based on principles of active inference (AIF) and predictive processing (PP). AIF and PP posit that the mind constructs a model of the environment which guides behavior by continually generating and integrating predictions and sensory input. The proposed model of the translation agent consists of three processing strata: a sensorimotor layer, a cognitive layer, and a phenomenal layer. Each layer consists of a network of states and transitions that interact on different time scales. Following the AIF framework, states are conditioned on observations which may originate from the environment and/or the embedded processing layer, while transitions between states are conditioned on actions that implement plans to optimize goal-oriented behavior. The AIF agent aims at simulating the variation in translational behavior under various conditions and to facilitate investigating the underlying mental mechanisms. It provides a novel framework for generating and testing new hypotheses of the translating mind.

In this paper we analyse translation of compounds from English to German relating our observations to the phenomenon of implicitation, explicitation and structural equivalence using translation process data from the CRITT translation process research database (TPR-DB). We look into complex nominal phrases, e.g. nominal compounds in English-German translations. We determine a number of product and process features that have an impact on cognitive effort during compound translation. Our results show that in most cases translators opt for the structural equivalent, and that choosing a direct equivalent requires less cognitive effort than choosing an implicitation. This is explained by the fact that direct equivalents can be easily activated whereas changing the structure and using a more implicit, less typical translation requires more effort, which in turn leads to longer production pauses.

There is increasing interest in machine assistance for literary translation, but research on how computer-assisted translation (CAT) tools and machine translation (MT) combine in the translation of literature is still incipient, especially for non-European languages. This article presents two exploratory studies where English-to-Chinese translators used neural MT to translate science fiction short stories in Trados Studio. One of the studies compares post-editing with a ‘no MT’ condition. The other examines two ways of presenting the texts on screen for postediting, namely by segmenting them into paragraphs or into sentences. We collected the data with the Qualititivity plugin for Trados Studio and describe a method for analysing data collected with this plugin through the translation process research database of the Center for Research in Translation and Translation Technology (CRITT). While post-editing required less technical effort, we did not find MT to be appreciably timesaving. Paragraph segmentation was associated with less postediting effort on average, though with high translator variability. We discuss the results in the light of broader concepts, such as status-quo bias, and call for more research on the different ways in which MT may assist literary translation, including its use for comparison purposes or, as mentioned by a participant, for ‘inspiration’.

Introduction: The objective of this study was to assess the bi-exponential relaxation times and fractions of the short and long components of the human patellar tendon ex vivo using three-dimensional ultrashort echo time T1ρ (3D UTE-T1ρ) imaging. Materials and Methods: Five cadaveric human knee specimens were scanned using a 3D UTE-T1ρ imaging sequence on a 3T MR scanner. A series of 3D UTE-T1ρ images were acquired and fitted using single-component and bi-component models. Single-component exponential fitting was performed to measure the UTE-T1ρ value of the patellar tendon. Bi-component analysis was performed to measure the short and long UTE-T1ρ values and fractions. Results: The single-component analysis showed a mean single-component UTE-T1ρ value of 8.4 ± 1.7 ms for the five knee patellar tendon samples. Improved fitting was achieved with bi-component analysis, which showed a mean short UTE-T1ρ value of 5.5 ± 0.8 ms with a fraction of 77.6 ± 4.8%, and a mean long UTE-T1ρ value of 27.4 ± 3.8 ms with a fraction of 22.4 ± 4.8%. Conclusion: The 3D UTE-T1ρ sequence can detect the single- and bi-exponential decay in the patellar tendon. Bi-component fitting was superior to single-component fitting.

PurposeQuantitative susceptibility mapping (QSM) has surfaced as a promising non-invasive quantitative biomarker that provides information about tissue composition and microenvironment. Recently, ultrashort echo time quantitative susceptibility mapping (UTE-QSM) has been investigated to achieve QSM of short T2 tissues. As the feasibility of UTE-QSM has not been demonstrated in the brain, the goal of this study was to develop a UTE-QSM with an efficient 3D cones trajectory and validate it in the human brain.Materials and methodsAn ultrashort echo time (UTE) cones sequence was implemented in a 3T clinical MRI scanner. Six images were acquired within a single acquisition, including UTE and gradient recalled echo (GRE) images. To achieve QSM, a morphology-enabled dipole inversion (MEDI) algorithm was incorporated, which utilizes both magnitude and phase images. Three fresh cadaveric human brains were scanned using the 3D cones trajectory with eight stretching factors (SFs) ranging from 1.0 to 1.7. In addition, five healthy volunteers were recruited and underwent UTE-QSM to demonstrate the feasibility in vivo. The acquired data were processed with the MEDI-QSM pipeline.ResultsThe susceptibility maps estimated by UTE-QSM showed reliable tissue contrast. In the ex vivo experiment, high correlations were found between the baseline (SF of 1.0) and SFs from 1.1 to 1.7 with Pearson’s correlations of 0.9983, 0.9968, 0.9959, 0.9960, 0.9954, 0.9943, and 0.9879, respectively (all p-values < 0.05). In the in vivo experiment, the measured QSM values in cortical gray matter, juxtacortical white matter, corpus callosum, caudate, and putamen were 25.4 ± 4.0, −21.8 ± 3.2, −22.6 ± 10.0, 77.5 ± 18.8, and 53.8 ± 7.1 ppb, consistent with the values reported in the literature.ConclusionUltrashort echo time quantitative susceptibility mapping enables direct estimation of the magnetic susceptibility in the brain with a dramatically reduced total scan time by use of a stretched 3D cones trajectory. This technique provides a new biomarker for susceptibility mapping in the in vivo brain.

Translation process research (TPR) has generated a large number of models that aim at explaining human translation processes. In this paper, I suggest an extension of the monitor model to incorporate aspects of relevance theory (RT) and to adopt the free energy principle (FEP) as a generative model to elucidate translational behaviour. The FEP—and its corollary, active inference—provide a general, mathematical framework to explain how organisms resist entropic erosion so as to remain within their phenotypic bounds. It posits that organisms reduce the gap between their expectations and observations by minimising a quantity called free energy. I map these concepts on the translation process and exemplify them with behavioural data. The analysis is based on the notion of translation units (TUs) which exhibit observable traces of the translator’s epistemic and pragmatic engagement with their translation environment, (i.e., the text) that can be measured in terms of translation effort and effects. Sequences of TUs cluster into translation states (steady state, orientation, and hesitation). Drawing on active inference, sequences of translation states combine into translation policies that reduce expected free energy. I show how the notion of free energy is compatible with the concept of relevance, as developed in RT, and how essential concepts of the monitor model and RT can be formalised as deep temporal generative models that can be interpreted under a representationalist view, but also support a non-representationalist account.

BackgroundThis study examines longitudinal bio-psychological dynamics and their interplay in IBD patients undergoing conventional and biological therapies.MethodsFifty IBD participants (24 UC, 26 CD) in clinical remission were followed for 12 months. Complete longitudinal datasets, biological samples, validated scores of psychological status were collected monthly for analysis of association. Microbiome analysis was performed to identify microbial dynamics and signatures. Patients were grouped on disease phenotype (CD, UC) and mode of treatment (biological therapies, non-biological treatment). General linear models, mixed models, cluster analysis, and analyses of variance were used to examine the longitudinal trends of the variables and their associations over time. Results were corrected for multiple testing.ResultsResults substantiated different interactions between biological therapy and longitudinal trends of inflammatory biomarkers in remission CD and UC patients as well as significant differences between CD and UC patients in their psychological measures during clinical remission, with UC patients having inferior condition compared to CD. A significant reduction in microbial diversity in CD patients compared to UC was identified. Results characterized considerable differences in longitudinal microbial profile between those taking and not taking biological treatment in UC patients, but not in CD patients.ConclusionA different trajectory of interdependence was identified between psychological state, sleep, and microbial dynamics with mode of treatment when compared between CD and UC patients. Further studies should investigate the causal relationships between bio-psychological factors for improved treatment purposes.

Ultra-short echo time (UTE) MRI with post-processing is a promising technique in bone imaging that produces a similar contrast to computed tomography (CT). Here, we propose a 3D slab-selective ultrashort echo time (UTE) sequence together with image post-processing to image bone structures in the lumbar spine. We also explore the intermodality agreement between the UTE and CT images. The lumbar spines of two healthy volunteers were imaged with 3D UTE using five different resolutions to determine the best imaging protocol. Then, four patients with low back pain were imaged with both the 3D UTE sequence and CT to investigate agreement between the imaging methods. Two other patients with low back pain were then imaged with the 3D UTE sequence and clinical conventional T1-weighted and T2-weighted fast spin-echo (FSE) MRI sequences for qualitative comparison. The 3D UTE sequence together with post-processing showed high contrast images of bone and high intermodality agreement with CT images. In conclusion, post-processed slab-selective UTE imaging is a feasible approach for highlighting bone structures in the lumbar spine and demonstrates significant anatomical correlation with CT images.

PurposeThe osteochondral junction (OCJ) region—commonly defined to include the deep radial uncalcified cartilage, tidemark, calcified cartilage, and subchondral bone plate—functions to absorb mechanical stress and is commonly associated with the pathogenesis of osteoarthritis. However, magnetic resonance imaging of the OCJ region is difficult due to the tissues’ short transverse relaxation times (i.e., short T2 or T2*), which result in little or no signal with conventional MRI. The goal of this study is to develop a 3D adiabatic inversion recovery prepared fat saturated zero echo time (IR-FS-ZTE) sequence for high-contrast imaging of the OCJ.MethodAn IR-FS-ZTE MR sequence was developed to image the OCJ on a clinical 3T MRI scanner. The IR-FS-ZTE sequence employed an adiabatic inversion pulse followed by a fat saturation pulse that suppressed signals from the articular cartilage and fat. At an inversion time (TI) that was matched to the nulling point of the articular cartilage, continuous ZTE imaging was performed with a smoothly rotating readout gradient, which enabled time-efficient encoding of the OCJ region’s short T2 signal with a minimal echo time (TE) of 12 μs. An ex vivo experiment with six cadaveric knee joints, and an in vivo experiment with six healthy volunteers and three patients with OA were performed to evaluate the feasibility of the proposed approach for high contrast imaging of the OCJ. Contrast-to-noise ratios (CNRs) between the OCJ and its neighboring femoral and tibial cartilage were measured.ResultsIn the ex vivo experiment, IR-FS-ZTE produced improved imaging of the OCJ region over the clinical sequences, and significantly improved the contrast compared to FS-ZTE without IR preparation (p = 0.0022 for tibial cartilage and p = 0.0019 for femoral cartilage with t-test). We also demonstrated the feasibility of high contrast imaging of the OCJ region in vivo using the proposed IR-FS-ZTE sequence, thereby providing more direct information on lesions in the OCJ. Clinical MRI did not detect signal from OCJ due to the long TE (>20 ms).ConclusionIR-FS-ZTE allows direct imaging of the OCJ region of the human knee and may help in elucidating the role of the OCJ in cartilage degeneration.

Bee sting injuries to the eye are relatively uncommon. The outcomes of corneal bee sting injuries are highly variable, and there is no consensus on the management at the time of initial presentation. We report a case of a 17-year-old male with a corneal bee sting injury that significantly improved in the acute stage without any treatment, which to the best of our knowledge is the first report in the literature documenting the natural history of a corneal bee sting injury in the acute stage.

The paper develops a definition of translation literality that is based on the syntactic and semantic similarity of the source and the target texts. We provide theoretical and empirical evidence that absolute literal translations are easy to produce. Based on a multilingual corpus of alternative translations we investigate the effects of cross-lingual syntactic and semantic distance on translation production times and find that non-literality makes from-scratch translation and post-editing difficult. We show that statistical machine translation systems encounter even more difficulties with non-literality.

Recent research demonstrates that white matter of the brain contains not only long T2 components, but a minority of ultrashort T2* components. Adiabatic inversion recovery prepared dual echo ultrashort echo time (IR-dUTE) sequences can be used to selectively image the ultrashort T2* components in white matter of the brain using a clinical whole body scanner. The T2*s of the ultrashort T2* components can be quantified using mono-exponential decay fitting of the IR-dUTE signal at a series of different TEs. However, accurate T1 measurement of the ultrashort T2* components is technically challenging. Efficient suppression of the signal from the majority of long T2 components is essential for robust T1 measurement. In this paper we describe a novel approach to this problem based on the use of IR-dUTE data acquisitions with different TR and TI combinations to selectively detect the signal recovery of the ultrashort T2* components. Exponential recovery curve fitting provides efficient T1 estimation, with minimized contamination from the majority of long T2 components. A rubber phantom and a piece of bovine cortical bone were used for validation of this approach. Six healthy volunteers were studied. An averaged T2* of 0.32 ± 0.09 ms, and a short mean T1 of 226 ± 46 ms were demonstrated for the healthy volunteers at 3T.

Eyetrackers which allow for free head movements are in many cases imprecise to the extent that reading patterns become heavily distorted. The poor usability and interpretability of these gaze patterns is corroborated by a "naïve" fixation-to-symbol mapping, which often wrongly maps the possibly drifted center of the observed fixation onto the symbol directly below it. In this paper I extend this naïve fixation-to-symbol mapping by introducing background knowledge about the translation task. In a first step, the sequence of fixation-to-symbol mappings is extended into a lattice of several possible fixated symbols, including those on the line above and below the naïve fixation mapping. In a second step a dynamic programming algorithm applies a number of heuristics to find the best path through the lattice, based on the probable distance in characters, in words and in pixels between successive fixations and the symbol locations, so as to smooth the gazing path according to the background gazing model. A qualitative and quantitative evaluation shows that the algorithm increases the accuracy of the re-mapped symbol sequence.

The ubiquity and pervasiveness of mobile network technologies has made them so deeply ingrained in our everyday lives that by interacting with them for very simple purposes (e.g., messaging or browsing the Internet), we produce an unprecedented amount of data that can be analyzed to understand our behavior. While this practice has been extensively adopted by telcos and big tech companies in the last few years, this condition, which was unimaginable just 20 years ago, has only been mildly exploited to fight the COVID-19 pandemic. In this paper, we discuss the possible alternatives that we could leverage in the current mobile network ecosystem to provide regulators and epidemiologists with the right understanding of our mobility patterns, to maximize the efficiency and extent of the introduced countermeasures. To validate our analysis, we dissect a fine-grained dataset of user positions in two major European countries severely hit by the pandemic. The potential of using these data, harvested employing traditional mobile network technologies, is unveiled through two exemplary cases that tackled macro and microscopic aspects. [ABSTRACT FROM AUTHOR]