Down-regulation of Survivin enhances sensitivity to BPR0L075 in human cancer cells via caspase-independent mechanisms

Background: BPR0L075 [6-methoxy-3-(3',4',5'-trimethoxy-benzoyl)-1H-indole] is a novel anti-cancer compound. It inhibits tubulin polymerization and induces mitochondrial-dependent apoptosis in various human cancer cells with different multi-drug resistance (MDR) status. Over-expression of an anti-apoptotic molecule, survivin, causes drug-resistance in various cancers. Survivin inhibits apoptosis by interfering caspase-3 and promotes cell growth by stabilizing microtubule networks. Here, we determined the effects of down-regulation of survivin in BPR0L075 (L075) treatment. Methods: Western blot analysis was used to determine the expression level of survivin in L075-untreated/-treated human oral carcinoma KB and nasopharyngeal carcinoma HONE-1 cancer cells. siRNA was used to down-regulate endogenous survivin. MTT cell viability assay, real-time caspase-3 activity assay and immuno-fluorescence microscopy were used to analyze downstream effects. Results: Survivin expression was up-regulated in both KB and HONE-1 cells in response to L075 treatment. Down-regulation of survivin induced hyper-sensitivity to L075 in KB and re-stored sensitivity to L075 in KB-derived L075-resistant KB-L30 cancer cells. At the molecular level, down-regulation of survivin induced changes in microtubule dynamics in both KB and KB-L30 cells. Surprisingly, down-regulation of survivin did not enhance the activity of caspase-3 in L075 therapy. Instead, down-regulation of survivin induced translocation of the apoptosis-inducing factor (AIF) from cytoplasm to nucleus. Conclusion: Down-regulation of survivin improved drug sensitivity to L075 in both KB and L075-resistant KB-L30 cancer cells, possibly through a tubulin-dependent and caspase-independent mechanism. We suggest that combining BPR0L075 and survivin inhibitor may give better clinical outcome than the use of BPR0L075 monotherapy in future clinical trials

Modelling of pulse-like velocity ground motion during the 2018 M_w 6.3 Hualien earthquake, Taiwan

The 2018 February 6 M_w 6.3 Hualien earthquake caused severe localized damage in Hualien City, located 20 km away from the epicentre. The damage was due to strong (>70 cm s⁻¹) and sharp (duration ∼2.5 s) velocity pulses. The observed peak ground-motion velocity in Hualien City symmetrically decays with distance from the nearby Milun fault. Waveforms observed on the opposite sides of the fault show reversed polarity on the vertical and N–S components while the E–W component is almost identical. None of the published finite-fault slip models can explain the spatially highly localized large velocity pulses. In this study, we show that an M_w 5.9 strike-slip subevent on the Milun fault at 2.5 km depth, rupturing from north to south at ∼0.9Vs speed, combined with site effects caused by surficial layers with low S-wave speed, can explain the velocity pulses observed at the dense strong-motion network stations. This subevent contributes only 25 per cent of the total moment of the 2018 Hualien earthquake, suggesting that a small local slip patch near a metropolis can dominate the local hazard. Our result strongly suggests that seismic hazard assessments should consider large ground-motion variabilities caused by directivity and site effects, as observed in the 2018 Hualien earthquake

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

18F-FDG PET/CT-based gross tumor volume definition for radiotherapy in head and neck Cancer: a correlation study between suitable uptake value threshold and tumor parameters

<p>Abstract</p> <p>Background</p> <p>To define a suitable threshold setting for gross tumor volume (GTV) when using ¹⁸Fluoro-deoxyglucose positron emission tomography and computed tomogram (PET/CT) for radiotherapy planning in head and neck cancer (HNC).</p> <p>Methods</p> <p>Fifteen HNC patients prospectively received PET/CT simulation for their radiation treatment planning. Biological target volume (BTV) was derived from PET/CT-based GTV of the primary tumor. The BTVs were defined as the isodensity volumes when adjusting different percentage of the maximal standardized uptake value (SUVmax), excluding any artifact from surrounding normal tissues. CT-based primary GTV (C-pGTV) that had been previously defined by radiation oncologists was compared with the BTV. Suitable threshold level (sTL) could be determined when BTV value and its morphology using a certain threshold level was observed to be the best fitness of the C-pGTV. Suitable standardized uptake value (sSUV) was calculated as the sTL multiplied by the SUVmax.</p> <p>Results</p> <p>Our result demonstrated no single sTL or sSUV method could achieve an optimized volumetric match with the C-pGTV. The sTL was 13% to 27% (mean, 19%), whereas the sSUV was 1.64 to 3.98 (mean, 2.46). The sTL was inversely correlated with the SUVmax [sTL = -0.1004 Ln (SUVmax) + 0.4464; R²= 0.81]. The sSUV showed a linear correlation with the SUVmax (sSUV = 0.0842 SUVmax + 1.248; R²= 0.89). The sTL was not associated with the value of C-pGTVs.</p> <p>Conclusion</p> <p>In PET/CT-based BTV for HNC, a suitable threshold or SUV level can be established by correlating with SUVmax rather than using a fixed threshold.</p

On-Call Duty Effects on Sleep-State Physiological Stability in Male Medical Interns

Background: On-call duty among medical interns is characterized by sleep deprivation and stressful working conditions, both of which alter cardiac autonomic modulation. We hypothesized that sleep stability decreased in medical interns during on-call duty. We used cardiopulmonary-coupling (CPC) analysis to test our hypothesis. Methods: We used electrocardiogram (ECG)-based CPC analysis to quantify physiological parameters of sleep stability in 13 medical interns during on-call and on-call duty-free periods. There were ten 33.5-h on-call duty shifts per month for interns, each followed by 2 on-call duty-free days, over 3 months. Measurements during sleep were collected before, during, and after an on-call shift. Measurements were repeated 3 months later during an on-call duty-free period. Results: The medical interns had significantly reduced stable sleep, and displayed increased latency to the first epoch of stable sleep during the on-call night shift, compared to the pre-call and on-call duty-free nights. Interns also had significantly increased rapid-eye-movement (REM) sleep during the on-call night shift, compared to the pre-call and on-call duty-free nights. Conclusion: Medical interns suffer disrupted sleep stability and continuity during on-call night shifts. The ECG-based CPC analysis provides a straightforward means to quantify sleep quality and stability in medical staff performing shift work under stressful conditions

RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning

Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. Several algorithms have been developed and obtained considerable success. However, most existing methods have unpleasant performance in the hazy scenario due to poor visibility. Though some strategies are possible to resolve this problem, they still have room to be improved due to the limited performance in real-world scenarios and the lack of real-world clear ground truth. Thus, to resolve this problem, inspired by CycleGAN, we construct a training paradigm called \textbf{RVSL} which integrates ReID and domain transformation techniques. The network is trained on semi-supervised fashion and does not require to employ the ID labels and the corresponding clear ground truths to learn hazy vehicle ReID mission in the real-world haze scenes. To further constrain the unsupervised learning process effectively, several losses are developed. Experimental results on synthetic and real-world datasets indicate that the proposed method can achieve state-of-the-art performance on hazy vehicle ReID problems. It is worth mentioning that although the proposed method is trained without real-world label information, it can achieve competitive performance compared to existing supervised methods trained on complete label information.Comment: Accepted by ECCV 202

Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise

Recently, quantum classifiers have been known to be vulnerable to adversarial attacks, where quantum classifiers are fooled by imperceptible noises to have misclassification. In this paper, we propose one first theoretical study that utilizing the added quantum random rotation noise can improve the robustness of quantum classifiers against adversarial attacks. We connect the definition of differential privacy and demonstrate the quantum classifier trained with the natural presence of additive noise is differentially private. Lastly, we derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples supported by experimental results.Comment: Submitted to IEEE ICASSP 202