Autologous repair of an internal carotid artery aneurysm by resection, caliber reduction, and external mesh tube reinforcement in a 9-year-old boy

Extracranial internal carotid artery aneurysms in children are rare, with a reported incidence of 0.5% to 1.9% in internal carotid artery aneurysm operations compared with all carotid operations in adult patients. We report a case of surgical reconstruction of an extracranial internal carotid artery aneurysm in a 9-year-old boy. Our patient complained of episodic neck pain on the left site under the mastoid process for the last year. The child was otherwise healthy. Autologous reconstruction without graft interposition was planned. Surgical repair was performed by resection of the main body of the aneurysm and restoration of the arterial continuity with end-to-end anastomosis. Because nondilated proximal and distal vessels could not be approximated, the most distal end of the aneurysm was tapered over a mandril. To prevent redilation, a tubular polyester external stent was fitted around the diseased segment

CodeGrid: A Grid Representation of Code

Code representation is a key step in the application of AI in software engineering. Generic NLP representations are effective but do not exploit all the rich structure inherent to code. Recent work has focused on extracting abstract syntax trees (AST) and integrating their structural information into code representations.These AST-enhanced representations advanced the state of the art and accelerated new applications of AI to software engineering. ASTs, however, neglect important aspects of code structure, notably control and data flow, leaving some potentially relevant code signal unexploited. For example, purely image-based representations perform nearly as well as AST-based representations, despite the fact that they must learn to even recognize tokens, let alone their semantics. This result, from prior work, is strong evidence that these new code representations can still be improved; it also raises the question of just what signal image-based approaches are exploiting. We answer this question. We show that code is spatial and exploit this fact to propose , a new representation that embeds tokens into a grid that preserves code layout. Unlike some of the existing state of the art, is agnostic to the downstream task: whether that task is generation or classification, can complement the learning algorithm with spatial signal. For example, we show that CNNs, which are inherently spatially-aware models, can exploit outputs to effectively tackle fundamental software engineering tasks, such as code classification, code clone detection and vulnerability detection. PixelCNN leverages 's grid representations to achieve code completion. Through extensive experiments, we validate our spatial code hypothesis, quantifying model performance as we vary the degree to which the representation preserves the grid. To demonstrate its generality, we show that augments models, improving their performance on a range of tasks, On clone detection, improves ASTNN's performance by 3.3% F1 score

Hyperthyroidism as a reversible cause of right ventricular overload and congestive heart failure

We describe a case of severe congestive heart failure and right ventricular overload associated with overt hyperthyroidism, completely reversed with antithyroid therapy in a few week. It represents a very unusual presentation of overt hyperthyroidism because of the severity of right heart failure. The impressive right ventricular volume overload made mandatory to perform transesophageal echo and angio-TC examination to exclude the coexistence of ASD or anomalous pulmonary venous return. Only a few cases of reversible right heart failure, with or without pulmonary hypertension, have been reported worldwide. In our case the most striking feature has been the normalization of the cardiovascular findings after six weeks of tiamazole therapy

Prostate Cancer Foundation Hormone-Sensitive Prostate Cancer Biomarker Working Group Meeting Summary.

Androgen deprivation therapy remains the backbone therapy for the treatment of metastatic hormone-sensitive prostate cancer (mHSPC). In recent years, several treatments, including docetaxel, abiraterone + prednisone, enzalutamide, and apalutamide, have each been shown to demonstrate survival benefit when used upfront along with androgen deprivation therapy. However, treatment selection for an individual patient remains a challenge. There is no high level clinical evidence for treatment selection among these choices based on biological drivers of clinical disease. In August 2020, the Prostate Cancer Foundation convened a working group to meet and discuss biomarkers for hormone-sensitive prostate cancer, the proceedings of which are summarized here. This meeting covered the state of clinical and biological evidence for systemic therapies in the mHSPC space, with emphasis on charting a course for the generation, interrogation, and clinical implementation of biomarkers for treatment selection

Integrating climate change mitigation and adaptation in agriculture and forestry: opportunities and trade-offs

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.International audienceAlthough many activities can jointly contribute to the climate change strategies of adaptation and mitigation, climate policies have generally treated these strategies separately. In recent years, there has been a growing interest shown by practitioners in agriculture, forestry, and landscape management in the links between the two strategies. This review explores the opportunities and trade-offs when managing landscapes for both climate change mitigation and adaptation; different conceptua-lizations of the links between adaptation and mitigation are highlighted. Under a first conceptualization of 'joint outcomes,' several reviewed studies analyze how activities without climatic objectives deliver joint adaptation and mitigation outcomes. In a second conceptualization of 'unintended side effects,' the focus is on how activities aimed at only one climate objective—either adaptation or mitigation—can deliver outcomes for the other objective. A third conceptualization of 'joint objectives' highlights that associating both adaptation and mitigation objectives in a climate-related activity can influence its outcomes because of multiple possible interactions. The review reveals a diversity of reasons for mainstreaming adaptation and mitigation separately or jointly in landscape management. The three broad conceptualizations of the links between adaptation and mitigation suggest different implications for climate policy mainstreaming and integration

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Poor Reproducibility of Allergic Rhinitis SNP Associations

10.1371/journal.pone.0053975PLoS ONE81

SEIS: Insight's Seismic Experiment for Internal Structure of Mars.

By the end of 2018, 42 years after the landing of the two Viking seismometers on Mars, InSight will deploy onto Mars' surface the SEIS (Seismic Experiment for Internal Structure) instrument; a six-axes seismometer equipped with both a long-period three-axes Very Broad Band (VBB) instrument and a three-axes short-period (SP) instrument. These six sensors will cover a broad range of the seismic bandwidth, from 0.01 Hz to 50 Hz, with possible extension to longer periods. Data will be transmitted in the form of three continuous VBB components at 2 sample per second (sps), an estimation of the short period energy content from the SP at 1 sps and a continuous compound VBB/SP vertical axis at 10 sps. The continuous streams will be augmented by requested event data with sample rates from 20 to 100 sps. SEIS will improve upon the existing resolution of Viking's Mars seismic monitoring by a factor of ∼ 2500 at 1 Hz and ∼ 200 000 at 0.1 Hz. An additional major improvement is that, contrary to Viking, the seismometers will be deployed via a robotic arm directly onto Mars' surface and will be protected against temperature and wind by highly efficient thermal and wind shielding. Based on existing knowledge of Mars, it is reasonable to infer a moment magnitude detection threshold of M w ∼ 3 at 40 ∘ epicentral distance and a potential to detect several tens of quakes and about five impacts per year. In this paper, we first describe the science goals of the experiment and the rationale used to define its requirements. We then provide a detailed description of the hardware, from the sensors to the deployment system and associated performance, including transfer functions of the seismic sensors and temperature sensors. We conclude by describing the experiment ground segment, including data processing services, outreach and education networks and provide a description of the format to be used for future data distribution.Electronic supplementary materialThe online version of this article (10.1007/s11214-018-0574-6) contains supplementary material, which is available to authorized users