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Echocardiography at discharge and after 2.5 years (2 months-6 years) of follow-up showed neither a postoperative increase of tricuspid regurgitation nor any relevant residual shunt. Postoperative electrocardiograms were normal without any sign of higher degree AV block. TVD offers enhanced exposure and safe treatment of VSDs. It did not result in higher rates of TV regurgitation or relevant AV block compared with the control group.

As inpatients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are at increased risk for venous thromboembolism (VTE), identifying high-risk patients requiring thromboprophylaxis is critical to reduce the mortality and morbidity associated with VTE. This study aimed to evaluate and compare the validities of the Padua Prediction Score and Caprini risk assessment model (RAM) in predicting the risk of VTE in inpatients with AECOPD.

The inpatients with AECOPD were prospectively enrolled from seven medical centers of China between September 2017 and January 2020. Caprini and Padua scores were calculated on admission, and the incidence of 3-month VTE was investigated.

Among the 3277 eligible patients with AECOPD, 128 patients (3.9%) developed VTE within 3 months after admission. The distribution of the study population by the Caprini risk level was as follows high, 53.6%; moderate, 43.0%; and low, 3.5%. The incidence of VTE increased by risk level as high, 6.1%; moderate, 1.5%; and low, 0%. According to the Padua RAM, only 10.9% of the study population was classified as high risk and 89.1% as low risk, with the corresponding incidence of VTE 7.9% and 3.4%, respectively. The Caprini RAM had higher area under curve (AUC) compared with the Padua RAM (0.713  0.021 vs 0.644 ± 0.023, P = 0.029).

The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

The Caprini RAM was superior to the Padua RAM in predicting the risk of VTE in inpatients with AECOPD and might better guide thromboprophylaxis in these patients.

It is still unclear if patients with community-acquired pneumonia (CAP) and coronavirus disease 2019 (COVID-19) have different rate, typology, and impact of thrombosis on survival.

In this multicentre observational cohort study 1.138 patients, hospitalized for CAP (n=559) or COVID-19 (n=579) from 7 clinical centres in Italy, were included in the study. Consecutive adult patients (age ≥18 years) with confirmed COVID-19 related pneumonia, with or without mechanical ventilation, hospitalized from 1st March 2020 to 30 April 2020, were enrolled. Covid-19 was diagnosed based on the WHO interim guidance. Patients were followed-up until discharge or in-hospital death, registering the occurrence of thrombotic events including ischemic/embolic events.

During the in-hospital stay, 11.4% of CAP and 15.5% of COVID-19 patients experienced thrombotic events (p=0.046). In CAP patients all the events were arterial thromboses, while in COVID-19 patients 8.3% were venous and 7.2% arterial thromboses. During the in-hospital follow-up, 3% of CAP patients and 17% of COVID-19 patients died (p<0.001). The highest mortality rate was found among COVID-19 patients with thrombotic events (47.6% vs 13.4% in thrombotic-event free patients; p<0.001). In CAP, 13.8% of patients experiencing thrombotic events died vs. 1.8% of thrombotic event-free ones (p<0.001). A multivariable COX-regression analysis confirmed a higher risk of death in COVID-19 patients with thrombotic events (HR 2.1; 95% CI 1.4-3.3; p<0.001).

Compared with CAP, COVID-19 is characterized by a higher burden of thrombotic events, different thrombosis typology and higher risk of thrombosis-related in-hospital mortality.

Compared with CAP, COVID-19 is characterized by a higher burden of thrombotic events, different thrombosis typology and higher risk of thrombosis-related in-hospital mortality.Classification is one of the major tasks that deep learning is successfully tackling. Categorization is also a fundamental cognitive ability. A well-known perceptual consequence of categorization in humans and other animals, categorical per ception, is notably characterized by a within-category compression and a between-category separation two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on experimental and theoretical results in cognitive science, here we study categorical effects in artificial neural networks. N6-methyladenosine RNA Synthesis chemical We combine a theoretical analysis that makes use of mutual and Fisher information quantities and a series of numerical simulations on networks of increasing complexity. These formal and numerical analyses provide insights into the geometry of the neural representation in deep layers, with expansion of space near category boundaries and contraction far from category boundaries. We investigate categorical representation by using two complementary approaches one mimics experiments in psychophysics and cognitive neuroscience by means of morphed continua between stimuli of different categories, while the other introduces a categoricality index that, for each layer in the network, quantifies the separability of the categories at the neural population level. We show on both shallow and deep neural networks that category learning automatically induces categorical perception. We further show that the deeper a layer, the stronger the categorical effects. As an outcome of our study, we propose a coherent view of the efficacy of different heuristic practices of the dropout regularization technique. More generally, our view, which finds echoes in the neuroscience literature, insists on the differential impact of noise in any given layer depending on the geometry of the neural representation that is being learned, that is, on how this geometry reflects the structure of the categories.Reinforcement learning involves updating estimates of the value of states and actions on the basis of experience. Previous work has shown that in humans, reinforcement learning exhibits a confirmatory bias when the value of a chosen option is being updated, estimates are revised more radically following positive than negative reward prediction errors, but the converse is observed when updating the unchosen option value estimate. Here, we simulate performance on a multi-arm bandit task to examine the consequences of a confirmatory bias for reward harvesting. We report a paradoxical finding that confirmatory biases allow the agent to maximize reward relative to an unbiased updating rule. This principle holds over a wide range of experimental settings and is most influential when decisions are corrupted by noise. We show that this occurs because on average, confirmatory biases lead to overestimating the value of more valuable bandits and underestimating the value of less valuable bandits, rendering decisions overall more robust in the face of noise. Our results show how apparently suboptimal learning rules can in fact be reward maximizing if decisions are made with finite computational precision.We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.Although in conventional models of cortical processing, object recognition and spatial properties are processed separately in ventral and dorsal cortical visual pathways respectively, some recent studies have shown that representations associated with both objects' identity (of shape) and space are present in both visual pathways. However, it is still unclear whether the presence of identity and spatial properties in both pathways have functional roles. In our study, we have tried to answer this question through computational modeling. Our simulation results show that both a model ventral and dorsal pathway, separately trained to do object and spatial recognition, respectively, each actively retained information about both identity and space. In addition, we show that these networks retained different amounts and kinds of identity and spatial information. As a result, our modeling suggests that two separate cortical visual pathways for identity and space (1) actively retain information about both identity and space (2) retain information about identity and space differently and (3) that this differently retained information about identity and space in the two pathways may be necessary to accurately and optimally recognize and localize objects.