No abstract

Very recently, an operator channel was defined by Koetter and Kschischang when they studied random network coding. They also introduced constant dimension codes and demonstrated that these codes can be employed to correct errors and/or erasures over the operator channel. Constant dimension codes are equivalent to the so-called linear authentication codes introduced by Wang, Xing and Safavi-Naini when constructing distributed authentication systems in 2003. In this paper, we study constant dimension codes. It is shown that Steiner structures are optimal constant dimension codes achieving the WangXing-Safavi-Naini bound. Furthermore, we show that constant dimension codes achieve the Wang-Xing-Safavi-Naini bound if and only if they are certain Steiner structures. Then, we derive two Johnson type upper bounds, say I and II, on constant dimension codes. The Johnson type bound II slightly improves on the Wang-Xing-Safavi-Naini bound. Finally, we point out that a family of known Steiner structures is actually a family of optimal constant dimension codes achieving both the Johnson type bounds I and II.

Lack of specific and efficient therapy leads to the high mortality rate of acute lung injury (ALI) and acute respiratory distress (ARDS). Recent evidence implies that angiotensin-converting enzyme (ACE) plays an important role in the pathogenesis of ALI. Pharmaceutical inhibitors of ACE have been used clinically for hypertension but not for ALI/ARDS yet. The objective was to study the effects of ACE inhibition with captopril on severe lung injury induced by oleic acid (OA) in rats. Oleic acid was intravenously injected into Sprague Dawley rats, followed by i.p. administration of captopril or saline control. Lung injury, endothelium damage and related molecules, and disturbance of coagulation were examined in comparison between the treated and the nontreated groups. An OA-induced ALI was featured with thickening of the alveolar septa, alveolar hemorrhage, and infiltration of inflammatory cells. Comparing with the nontreated OA group, the administration of captopril prevented the rats from OA-induced severe lungs injury, with a significantly lower lung injury score, less albumin content and infiltrated cells in the alveoli, decreased wet/dry weight ratio of the lung tissues, and improved lung function (PaO 2 per fraction of inspired oxygen). Captopril also dramatically reduced the expression of intercellular adhesion molecule<1 in the lung tissue and in the circulating endothelial cells in the blood, indicating a protective effect on endothelial cells activation/damage. Moreover, captopril treatment led to a blockage of nuclear factor .B activation in lung tissues and to the recovery of the fibrinolytic disturbance. Thus, our data suggest that the inhibition of ACE with its clinically used inhibitor offers protective effects on ALI/ARDS, implying the potential for therapeutic option.

Androgen receptor (AR) activation is critical for prostate cancer development and progression, including castration-resistance. The nuclear export signal of AR (NESAR) plays an important role in AR intracellular trafficking and proteasome-dependent degradation. Here, we identified the RNA helicase DHX15 as a novel AR co-activator using a yeast mutagenesis screen and revealed that DHX15 regulates AR activity by modulating E3 ligase Siah2-mediated AR ubiquitination independent of its ATPase activity. DHX15 and Siah2 form a complex with AR, through NESAR. DHX15 stabilized Siah2 and enhanced its E3 ubiquitin ligase activity, resulting in AR activation. Importantly, DHX15 was upregulated in prostate cancer specimens and its expression was correlated with Gleason scores and PSA recurrence. Furthermore, DHX15 immunostaining correlated with Siah2. Finally, DHX15 knockdown inhibited the growth of C4-2 prostate tumor xenografts in mice. Collectively, our data argue that DHX15 enhances AR transcriptional activity and contributes to prostate cancer progression through Siah2.

Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.

In recent years, plasma-activated solutions (PASs) have made good progress in the disinfection of medical devices, tooth whitening, and fruit preservation. In this study, we investigated the inactivation efficacy of Newcastle disease virus by PASs. Water, 0.9% NaCl, and 0.3% H2O2 were excited by plasma to obtain the corresponding solutions PAS(H2O), PAS(NaCl), and PAS(H2O2). The complete inactivation of virus after PAS treatment for 30 min was confirmed by the embryo lethality assay (ELA) and hemagglutination (HA) test. Scanning electron microscopy (SEM) results showed that the morphology of the viral particle changed under PAS treatments. The total protein concentration of virus decreased as measured by a Bradford protein assay due to PAS treatment. The nucleic acid integrity assay demonstrated that viral RNA degraded into smaller fragments. Moreover, the physicochemical properties of PASs, including the oxidation-reduction potential (ORP), electrical conductivity, and H2O2 concentration, and electron spin resonance spectra analysis indicated that reactive oxygen and nitrogen species play a major role in the virus inactivation. Therefore, the application of PASs, as an environmentally friendly method, would be a promising alternative strategy in poultry industries.IMPORTANCE Newcastle disease (ND), as an infectious viral disease of avian species, caused significant economic losses to domestic animal and poultry industries. The traditional chemical sanitizers, such as chlorine-based products, are associated with risks of by-product formation with carcinogenic effects and environmental pollution. On the basis of this, plasma-activated water as a green disinfection product is a promising alternative for applications in stock farming and sterilization in hospitals and public places. In this study, we explored the inactivation efficacy of different plasma-activated solutions (PASs) against ND virus (NDV) and the possible underlying mechanisms. Our results demonstrated that reactive oxygen and nitrogen species detected in PASs, including short-lived OH˙ and NO˙ and long-lived H2O2, changed the morphology, destroyed the RNA structure, and degraded the protein of the virus, consequently resulting in virus inactivation. These lay a foundation for the application of PASs to resolve the issues of public health and environmental sanitation.

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces represented by triangular meshes, with large inter-subject and intrasubject variations in vertex number and local connectivity. Hence, there is no consistent neighborhood definition and thus no straightforward convolution/transposed convolution operations for cortical/subcortical surface data. In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid. Accordingly, we develop corresponding operations for convolution, pooling, and transposed convolution for spherical surface data and thus construct spherical CNNs. Specifically, we propose the Spherical U-Net architecture by replacing all operations in the standard U-Net with their spherical operation counterparts. We then apply the Spherical U-Net to two challenging and neuroscientifically important tasks in infant brains: cortical surface parcellation and cortical attribute map development prediction. Both applications demonstrate the competitive performance in the accuracy, computational efficiency, and effectiveness of our proposed Spherical U-Net, in comparison with the state-of-the-art methods.

Increasing multi-site infant neuroimaging datasets are facilitating the research on understanding early brain development with larger sample size and bigger statistical power. However, a joint analysis of cortical properties (e.g., cortical thickness) is unavoidably facing the problem of nonbiological variance introduced by differences in MRI scanners. To address this issue, in this paper, we propose cycle-consistent adversarial networks based on spherical cortical surface to harmonize cortical thickness maps between different scanners. We combine the spherical U-Net and CycleGAN to construct a surface-to-surface CycleGAN (S2SGAN). Specifically, we model the harmonization from scanner X to scanner Y as a surface-to-surface translation task. The first goal of harmonization is to learn a mapping G X : X → Y such that the distribution of surface thickness maps from G X (X) is indistinguishable from Y. Since this mapping is highly under-constrained, with the second goal of harmonization to preserve individual differences, we utilize the inverse mapping G Y : Y → X and the cycle consistency loss to enforce G Y (G X (X)) ≈ X (and vice versa). Furthermore, we incorporate the correlation coefficient loss to guarantee the structure consistency between the original and the generated surface thickness maps. Quantitative evaluation on both synthesized and real infant cortical data demonstrates the superior ability of our method in removing unwanted scanner effects and preserving individual differences simultaneously, compared to the state-of-the-art methods.