Lymphangiography to treat postoperative lymphatic leakage: a technical review.

In addition to imaging the lymphatics and detecting various types of lymphatic leakage, lymphangiography is a therapeutic option for patients with chylothorax, chylous ascites, and lymphatic fistula. Percutaneous thoracic duct embolization, transabdominal catheterization of the cisterna chyli or thoracic duct, and subsequent embolization of the thoracic duct is an alternative to surgical ligation of the thoracic duct. In this pictorial review, we present the detailed technique, clinical applications, and complications of lymphangiography and thoracic duct embolization

Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis and additionally considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries, and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. It allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 0.989~84.995 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure

Bioinspired reversible hydrogel adhesives for wet and underwater surfaces

Stable and reversible adhesion to wet surfaces is challenging owing to water molecules at the contact interface. In this study, we develop a hydrogel-based wet adhesive, which can exhibit strong and reversible adhesion to wet and underwater surfaces as well as to dry surfaces. The remarkable wet adhesion of the hydrogel adhesive is realized based on a synergetic integration of bioinspired microarchitectures and water-friendly and water-absorbing properties of the polymeric hydrogel. Under dry conditions, the microstructured hydrogel adhesive exhibits strong van der Waals interaction-based adhesion, while under underwater conditions, it can maximize capillary adhesion. Consequently, the hydrogel adhesive exhibits remarkable adhesion strengths for dry, moist, and submerged substrates. Maximum normal and shear adhesion strengths of 423 and 384, 492 and 340, and 253 and 21 kPa are achieved with the hydrogel adhesive for dry, moist, and submerged substrates, respectively. Our results demonstrate that strong wet and underwater adhesion can be achieved only with the hydrogel-based adhesive with simple microscale architecture

Does Tumor Size Influence the Diagnostic Accuracy of Ultrasound-Guided Fine-Needle Aspiration Cytology for Thyroid Nodules?

Background. Fine-needle aspiration cytology (FNAC) is diagnostic standard for thyroid nodules. However, the influence of size on FNAC accuracy remains unclear especially in too small or too large thyroid nodules. The objective of this retrospective cohort study was to investigate the effect of nodule size on FNAC accuracy. Methods. All consecutive patients who underwent thyroidectomy for nodules in 2010 were enrolled. FNAC results (according to the Bethesda system) were compared to pathological diagnosis. The nodules were categorized into groups A–E on the basis of maximal diameter on ultrasound (≤0.5, >0.5–1, >1-2, >2–4, and >4 cm, resp.). Results. There were 502 cases with 690 nodules. Overall FNAC sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 95.4%, 98.2%, 99.4%, 86.4%, and 96.0%, respectively. False-negative rates (FNRs) of groups A–E were 3.2%, 5.1%, 1.3%, 13.3%, and 50%, respectively. Accuracy rates of groups A–E were 96.8%, 94.8%, 99%, 94.7%, and 87.5%, respectively. Conclusion. Although accuracy rates of FNAC in thyroid nodules smaller than 0.5 cm are comparable to the other group, thyroid nodules larger than 4 cm with benign cytology carry a higher risk of malignancy, which suggest that those should be considered for intensive follow-up or repeated biopsy

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.Comment: 30 pages, 10 figure