Organized intra-atrial thrombus in growing premature infant

This article reports interesting echocardiographic images of the atrial septum in two growing premature infants related to the previous use of umbilical venous lines in the neonatal period. Complications related to central venous lines are well documented but when they are identified in follow up after several months, their relevance is difficult to establish. Atrial septal findings of organized clot in these asymptomatic infants needs to be correlated with details of neonatal care.peer-reviewe

XGrad: Boosting Gradient-Based Optimizers With Weight Prediction

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update rule of the used optimizer and are then applied to both the forward pass and backward propagation. In this way, during the whole training period, the optimizer always utilizes the gradients w.r.t. the future weights to update the DNN parameters, making the gradient-based optimizer achieve better convergence and generalization compared to the original optimizer without weight prediction. XGrad is rather straightforward to implement yet pretty effective in boosting the convergence of gradient-based optimizers and the accuracy of DNN models. Empirical results concerning the most three popular gradient-based optimizers including SGD with momentum, Adam, and AdamW demonstrate the effectiveness of our proposal. The experimental results validate that XGrad can attain higher model accuracy than the original optimizers when training the DNN models. The code of XGrad will be available at: https://github.com/guanleics/XGrad.Comment: arXiv admin note: text overlap with arXiv:2302.0019

MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks

The field of machine learning (ML) has gained widespread adoption, leading to a significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML tasks (e.g., AutoML) are often time consuming and hard to understand for human developers. In contrast, though human engineers have the incredible ability to understand tasks and reason about solutions, their experience and knowledge are often sparse and difficult to utilize by quantitative approaches. In this paper, we aim to bridge the gap between machine intelligence and human knowledge by introducing a novel framework MLCopilot, which leverages the state-of-the-art LLMs to develop ML solutions for novel tasks. We showcase the possibility of extending the capability of LLMs to comprehend structured inputs and perform thorough reasoning for solving novel ML tasks. And we find that, after some dedicated design, the LLM can (i) observe from the existing experiences of ML tasks and (ii) reason effectively to deliver promising results for new tasks. The solution generated can be used directly to achieve high levels of competitiveness

Direct and negative regulation of the sycO-ypkA-ypoJ operon by cyclic AMP receptor protein (CRP) in Yersinia pestis

<p>Abstract</p> <p>Background</p> <p>Pathogenic yersiniae, including <it>Y. pestis</it>, share a type III secretion system (T3SS) that is composed of a secretion machinery, a set of translocation proteins, a control system, and six Yop effector proteins including YpkA and YopJ. The cyclic AMP receptor protein (CRP), a global regulator, was recently found to regulate the laterally acquired genes (<it>pla </it>and <it>pst</it>) in <it>Y. pestis</it>. The regulation of T3SS components by CRP is unknown.</p> <p>Results</p> <p>The <it>sycO</it>, <it>ypkA </it>and <it>yopJ </it>genes constitute a single operon in <it>Y. pestis</it>. CRP specifically binds to the promoter-proximate region of <it>sycO</it>, and represses the expression of the <it>sycO-ypkA-yopJ </it>operon. A single CRP-dependent promoter is employed for the <it>sycO-ypkA-yopJ </it>operon, but two CRP binding sites (site 1 and site 2) are detected within the promoter region. A CRP box homologue is found in site 1 other than site 2. The determination of CRP-binding sites, transcription start site and core promoter element (-10 and -35 regions) promotes us to depict the structural organization of CRP-dependent promoter, giving a map of CRP-promoter DNA interaction for <it>sycO-ypkA-yopJ</it>.</p> <p>Conclusion</p> <p>The <it>sycO-ypkA-yopJ </it>operon is under the direct and negative regulation of CRP in <it>Y. pestis</it>. The <it>sycO-ypkA-yopJ </it>promoter-proximate regions are extremely conserved in <it>Y. pestis</it>, <it>Y. pseudotuberculosis </it>and <it>Y. enterocolitica</it>. Therefore, data presented here can be generally applied to the above three pathogenic yersiniae.</p