758 research outputs found
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
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