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It is highly desirable for liquid crystal elastomer (LCE) based microactuators to activate and actuate in a highly controlled fashion without perturbing the surrounding environment. To reach this goal, in this study, a novel experimental protocol is developed to successfully incorporate gold nanosphere (AuNS) and gold nanorod (AuNR) into polyacrylate based LCE elastomer to fabricate LCE/AuNR and LCE/AuNS micropillars or microactuators. The effect of gold nanoparticle inclusion has been studied by spectroscopy (UVvis-near-infrared), microscopy (transmission electron microscopy), thermal analysis (differential scanning calorimetry and thermogravimetric analysis), and x-ray scattering (wide-angle x-ray scattering and small-angle x-ray scattering). Finite element analysis is performed to examine the feasibility of utilizing the photothermal effect of AuNR/AuNS to enable photothermal actuation of LCE/AuNR and LCE/AuNS micropillars. The comparative experimental studies on the thermal and photothermal actuation behavior of the LCE, LCE/AuNS, and LCE/AuNR micropillar suggested that AuNR is an excellent candidate for developing high-performance LCE actuators with photothermal actuation capability. With inclusion of less than 1 wt% of AuNR, the very high maximum actuation strain (30%) and rapid response (a few seconds) have been achieved in LCE/AuNR micropillar actuators under 635 nm laser irradiation.

These results demonstrate that NaBu exerts an antihypertensive action, likely by suppressing the PRR-mediated intrarenal renin-angiotensin system.

CAPTCHA is now a standard security technology for differentiating between computers and humans, and the most widely deployed schemes are text-based. While many text schemes have been broken, hollow CAPTCHAs have emerged as one of the latest designs, and they have been deployed by major companies such as Yahoo!, Tencent, Sina, China Mobile and Baidu. A main feature of such schemes is to use contour lines to form connected hollow characters with the aim of improving security and usability simultaneously, as it is hard for standard techniques to segment and recognize such connected characters, which are however easy to human eyes. In this paper, we provide the first analysis of hollow CAPTCHAs' robustness. We show that with a simple but novel attack, we can successfully break a whole family of hollow CAPTCHAs, including those deployed by all the major companies. While our attack casts serious doubt on the viability of current designs, we offer lessons and guidelines for designing better hollow CAPTCHAs.

ObjectiveTumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DesignAn interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A ‘tumour risk score (TRS)’ was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.ResultsSurvival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.ConclusionOur deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment.

Key Points• Somatic CDKN1B (p27) mutations were identified in 16% (13/81) of HCL patients and coexist with BRAFV600E mutations.• CDKN1B is the second most common mutated gene in HCL implicating altered cell cycle regulation and/or senescence in HCL.Hairy cell leukemia (HCL) is marked by near 100% mutational frequency of BRAFV600E mutations. Recurrent cooperating genetic events that may contribute to HCL pathogenesis or affect the clinical course of HCL are currently not described. Therefore, we performed whole exome sequencing to explore the mutational landscape of purine analog refractory HCL. In addition to the disease-defining BRAFV600E mutations, we identified mutations in EZH2, ARID1A, and recurrent inactivating mutations of the cell cycle inhibitor CDKN1B (p27). Targeted deep sequencing of CDKN1B in a larger cohort of HCL patients identify deleterious CDKN1B mutations in 16% of patients with HCL (n 5 13 of 81). In 11 of 13 patients the CDKN1B mutation was clonal, implying an early role of CDKN1B mutations in the pathogenesis of HCL. CDKN1B mutations were not found to impact clinical characteristics or outcome in this cohort. These data identify HCL as having the highest frequency of CDKN1B mutations among cancers and identify CDNK1B as the second most common mutated gene in HCL. Moreover, given the known function of CDNK1B, these data suggest a novel role for alterations in regulation of cell cycle and senescence in HCL with CDKN1B mutations. (Blood. 2015;126(8):1005-1008) IntroductionHairy-cell leukemia (HCL) is a rare, mature B-cell malignancy presenting with slow progressing pancytopenia and splenomegaly. Classical HCL is successfully treated with chemotherapy, but eradication of minimal residual disease is rarely achieved. 1 Standard treatment fails in a minority of patients, with a potentially fatal outcome.Gain-of-function mutations of the BRAF serine/threonine protein kinase (BRAFV600E) have been identified in nearly all cases of classical HCL, and mitogen-activated protein kinase signaling is considered the key oncogenic pathway in HCL.2 Chung et al 3 recently identified hematopoietic stem cells as the cell of origin of HCL by demonstrating that hematopoietic stem cells, and subsequently cells along the hematopoietic hierarchy, contain mutated BRAF. Currently, however, no other recurrently mutated genes are known to coexist with BRAFV600E mutations in HCL. It is unclear if BRAFV600E mutations alone are sufficient to induce HCL. Moreover, it is also not known if additional mutations may be acquired in BRAFV600E-mutant HCL cells, resulting in acquired resistance to therapies commonly administered to patients with HCL such as purine analogs. Therefore, we performed whole-exome sequencing (WES) in 3 HCL patients who were refractory to purine analog treatment and received the BRAF inhibitor (BRAFi) vemurafenib followed by recurrence testing of novel mutations in a larger cohort of HCL patients. Study designClinical samples were provided by

Key Points• Low doses of the BRAF inhibitor vemurafenib are highly effective in refractory hairy cell leukemia.• Abrogation of BRAF V600E-induced signaling was consistently seen with 240 mg of vemurafenib twice daily.The activating mutation of the BRAF serine/threonine protein kinase (BRAF V600E) is the key driver mutation in hairy cell leukemia (HCL), suggesting opportunities for therapeutic targeting. We analyzed the course of 21 HCL patients treated with vemurafenib outside of trials with individual dosing regimens (240-1920 mg/d; median treatment duration, 90 days). Vemurafenib treatment improved blood counts in all patients, with platelets, neutrophils, and hemoglobin recovering within 28, 43, and 55 days (median), respectively. Complete remission was achieved in 40% (6/15 of evaluable patients) and median event-free survival was 17 months. Response rate and kinetics of response were independent of vemurafenib dosing. Retreatment with vemurafenib led to similar response patterns (n 5 6). Pharmacodynamic analysis of BRAF V600E downstream targets showed that vemurafenib (480 mg/d) completely abrogated extracellular signal-regulated kinase phosphorylation of hairy cells in vivo. Typical side effects also occurred at low dosing regimens. We observed the development of acute myeloid lymphoma (AML) subtype M6 in 1 patient, and the course suggested disease acceleration triggered by vemurafenib. The phosphatidylinositol 3-kinase hotspot mutation (E545K) was identified in the AML clone, providing a potential novel mechanism for paradoxical BRAF activation. These data provide proof of dependence of HCL on active BRAF signaling. We provide evidence that antitumor and side effects are observed with 480 mg vemurafenib, suggesting that dosing regimens in BRAF-driven cancers could warrant reassessment in trials with implications for cost of cancer care. (Blood. 2016;127(23):2847-2855

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.