A pupil size response model to assess fear learning

During fear conditioning, pupil size responses dissociate between conditioned stimuli that are contingently paired (CS+) with an aversive unconditioned stimulus, and those that are unpaired (CS-). Current approaches to assess fear learning from pupil responses rely on ad hoc specifications. Here, we sought to develop a psychophysiological model (PsPM) in which pupil responses are characterized by response functions within the framework of a linear time-invariant system. This PsPM can be written as a general linear model, which is inverted to yield amplitude estimates of the eliciting process in the central nervous system. We first characterized fear-conditioned pupil size responses based on an experiment with auditory CS. PsPM-based parameter estimates distinguished CS+/CS- better than, or on par with, two commonly used methods (peak scoring, area under the curve). We validated this PsPM in four independent experiments with auditory, visual, and somatosensory CS, as well as short (3.5 s) and medium (6 s) CS/US intervals. Overall, the new PsPM provided equal or decisively better differentiation of CS+/CS- than the two alternative methods and was never decisively worse. We further compared pupil responses with concurrently measured skin conductance and heart period responses. Finally, we used our previously developed luminance-related pupil responses to infer the timing of the likely neural input into the pupillary system. Overall, we establish a new PsPM to assess fear conditioning based on pupil responses. The model has a potential to provide higher statistical sensitivity, can be applied to other conditioning paradigms in humans, and may be easily extended to nonhuman mammals

Whole blood coagulation and platelet activation in the athlete: A comparison of marathon, triathlon and long distance cycling

<p>Abstract</p> <p>Introduction</p> <p>Serious thrombembolic events occur in otherwise healthy marathon athletes during competition. We tested the hypothesis that during heavy endurance sports coagulation and platelets are activated depending on the type of endurance sport with respect to its running fraction.</p> <p>Materials and Methods</p> <p>68 healthy athletes participating in marathon (MAR, running 42 km, n = 24), triathlon (TRI, swimming 2.5 km + cycling 90 km + running 21 km, n = 22), and long distance cycling (CYC, 151 km, n = 22) were included in the study. Blood samples were taken before and immediately after completion of competition to perform rotational thrombelastometry. We assessed coagulation time (CT), maximum clot firmness (MCF) after intrinsically activation and fibrin polymerization (FIBTEM). Furthermore, platelet aggregation was tested after activation with ADP and thrombin activating peptide 6 (TRAP) by using multiple platelet function analyzer.</p> <p>Results</p> <p>Complete data sets were obtained in 58 athletes (MAR: n = 20, TRI: n = 19, CYC: n = 19). CT significantly decreased in all groups (MAR -9.9%, TRI -8.3%, CYC -7.4%) without differences between groups. In parallel, MCF (MAR +7.4%, TRI +6.1%, CYC +8.3%) and fibrin polymerization (MAR +14.7%, TRI +6.1%, CYC +8.3%) were significantly increased in all groups. However, platelets were only activated during MAR and TRI as indicated by increased AUC during TRAP-activation (MAR +15.8%) and increased AUC during ADP-activation in MAR (+50.3%) and TRI (+57.5%).</p> <p>Discussion</p> <p>While coagulation is activated during physical activity irrespective of type we observed significant platelet activation only during marathon and to a lesser extent during triathlon. We speculate that prolonged running may increase platelet activity, possibly, due to mechanical alteration. Thus, particularly prolonged running may increase the risk of thrombembolic incidents in running athletes.</p

Pump-probe polarized transient hole burning (PTHB) dynamics of hydrated electron revisited

Femtosecond PTHB spectroscopy was expected to demonstrate the existence of distinct s-p absorption subbands originating from the three nondegenerate p-like excited states of hydrated electron in anisotropic solvation cavity. Yet no conclusive experimental evidence either for this subband structure or the reorientation of the cavity on the picosecond time scale has been obtained. We demonstrate that rapid reorientation of s-p transition dipole moments in response to small scale motion of water molecules is the likely culprit. The polarized bleach is shown to be too small and too short lived to be observed reliably on the sub-picosecond time scale.Comment: 10 pages + 3 figures + supplement, will be submitted shortly to Chem. Phys. Let

A comprehensive ovine model of blood transfusion

Background: The growing awareness of transfusion-associated morbidity and mortality necessitates investigations into the underlying mechanisms. Small animals have been the dominant transfusion model but have associated limitations. This study aimed to develop a comprehensive large animal (ovine) model of transfusion encompassing: blood collection, processing and storage, compatibility testing right through to post-transfusion outcomes. Materials and methods: Two units of blood were collected from each of 12 adult male Merino sheep and processed into 24 ovine-packed red blood cell (PRBC) units. Baseline haematological parameters of ovine blood and PRBC cells were analysed. Biochemical changes in ovine PRBCs were characterized during the 42-day storage period. Immunological compatibility of the blood was confirmed with sera from potential recipient sheep, using a saline and albumin agglutination cross-match. Following confirmation of compatibility, each recipient sheep (n = 12) was transfused with two units of ovine PRBC. Results: Procedures for collecting, processing, cross-matching and transfusing ovine blood were established. Although ovine red blood cells are smaller and higher in number, their mean cell haemoglobin concentration is similar to human red blood cells. Ovine PRBC showed improved storage properties in saline-adenine-glucose-mannitol (SAG-M) compared with previous human PRBC studies. Seventy-six compatibility tests were performed and 17·1% were incompatible. Only cross-match compatible ovine PRBC were transfused and no adverse reactions were observed. Conclusion: These findings demonstrate the utility of the ovine model for future blood transfusion studies and highlight the importance of compatibility testing in animal models involving homologous transfusions

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, our extensive results on three benchmark datasets with different labeled settings validate the effectiveness of our proposed MONA which achieves new state-of-the-art under different labeled settings

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective

For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth label, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical features and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group sampling theory in medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation, and show that certain variance-reduction techniques are particularly beneficial in medical image segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on three benchmark datasets with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing medical image analysis tasks

Aquatics reconstruction software: the design of a diagnostic tool based on computer vision algorithms

Computer vision methods can be applied to a variety of medical and surgical applications, and many techniques and algorithms are available that can be used to recover 3D shapes and information from images range and volume data. Complex practical applications, however, are rarely approachable with a single technique, and require detailed analysis on how they can be subdivided in subtasks that are computationally treatable and that, at the same time, allow for the appropriate level of user-interaction. In this paper we show an example of a complex application where, following criteria of efficiency, reliability and user friendliness, several computer vision techniques have been selected and customized to build a system able to support diagnosis and endovascular treatment of Abdominal Aortic Aneurysms. The system reconstructs the geometrical representation of four different structures related to the aorta (vessel lumen, thrombus, calcifications and skeleton) from CT angiography data. In this way it supports the three dimensional measurements required for a careful geometrical evaluation of the vessel, that is fundamental to decide if the treatment is necessary and to perform, in this case, its planning. The system has been realized within the European trial AQUATICS (IST-1999-20226 EUTIST-M WP 12), and it has been widely tested on clinical data