Online Streaming Video Super-Resolution With Convolutional Look-Up Table.

Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applying existing video super-resolution methods to online streaming is non-trivial. Existing video codecs and streaming protocols (e.g., WebRTC) dynamically change the video quality both spatially and temporally, which leads to diverse and dynamic degradations. Furthermore, online streaming has a strict requirement for latency that most existing methods are less applicable. As a result, this paper focuses on the rarely exploited problem setting of online streaming video super resolution. To facilitate the research on this problem, a new benchmark dataset named LDV-WebRTC is constructed based on a real-world online streaming system. Leveraging the new benchmark dataset, we propose a novel method specifically for online video streaming, which contains a convolution and Look-Up Table (LUT) hybrid model to achieve better performance-latency trade-off. To tackle the changing degradations, we propose a mixture-of-expert-LUT module, where a set of LUT specialized in different degradations are built and adaptively combined to handle different degradations. Experiments show our method achieves 720P video SR around 100 FPS, while significantly outperforms existing LUT-based methods and offers competitive performance compared to efficient CNN-based methods. Code is available at https://github.com/quzefan/ConvLUT.

Detecting the expression of HRs and BCL2 via IHC can help identify luminal A-like subtypes of triple-positive breast cancers.

BACKGROUND: Triple-positive breast cancer (TPBC) is a tumor that simultaneously expresses estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Luminal A-like TPBC is a special subtype with a favorable prognosis but benefits less from HER2-targeted therapy. However, little is known about how to identify luminal A-like TPBCs. Therefore, our study aims to explore a clinically feasible method to identify luminal A-like TPBCs using immunohistochemical (IHC) markers. METHODS: Our cohort enrolled consecutive 190 patients with early-stage TPBCs diagnosed, treated and followed up in our hospital between 2013 and 2019. Patients whose IHC staining displayed ≥ 50% in both ER and PR scores and B-cell lymphoma 2 (BCL2) positivity were classified as cohort A (n = 64), and the rest were enrolled in cohort B (n = 126). Kaplan-Meier plotter and log-rank test were used to compare the survival difference between cohort A and cohort B and the efficacy of trastuzumab therapy in the two cohorts. RESULTS: The disease-free survival (DFS) of patients in cohort A was significantly better than in cohort B (p = 0.031). In cohort A, there was no statistically significant difference in DFS between patients treated with trastuzumab and those without trastuzumab (p = 0.663). While in cohort B, patients treated with trastuzumab had significantly better DFS than those without trastuzumab (p = 0.032). Multivariate survival analysis showed that cohort A was associated with better DFS(95%CI 1.046-11.776, p = 0.042). CONCLUSION: TPBCs consist of heterogeneous subtypes. Detecting the expression of ER, PR and BCL2 via IHC can help identify luminal A-like TPBCs. This study will enable individualized treatment of TPBCs.

Conditional Hyper-Network for Blind Super-Resolution With Multiple Degradations.

Although the single-image super-resolution (SISR) methods have achieved great success on the single degradation, they still suffer performance drop with multiple degrading effects in real scenarios. Recently, some blind and non-blind models for multiple degradations have been explored. However, these methods usually degrade significantly for distribution shifts between the training and test data. Towards this end, we propose a novel conditional hyper-network framework for super-resolution with multiple degradations (named CMDSR), which helps the SR framework learn how to adapt to changes in the degradation distribution of input. We extract degradation prior at the task-level with the proposed ConditionNet, which will be used to adapt the parameters of the basic SR network (BaseNet). Specifically, the ConditionNet of our framework first learns the degradation prior from a support set, which is composed of a series of degraded image patches from the same task. Then the adaptive BaseNet rapidly shifts its parameters according to the conditional features. Moreover, in order to better extract degradation prior, we propose a task contrastive loss to shorten the inner-task distance and enlarge the cross-task distance between task-level features. Without predefining degradation maps, our blind framework can conduct one single parameter update to yield considerable improvement in SR results. Extensive experiments demonstrate the effectiveness of CMDSR over various blind, and even several non-blind methods. The flexible BaseNet structure also reveals that CMDSR can be a general framework for a large series of SISR models. Our code is available at https://github.com/guanghaoyin/CMDSR.

Epidermal growth factor receptor (EGFR) mutation and p-EGFR expression in resected non-small cell lung cancer.

Lung cancer, specifically non-small cell lung cancer (NSCLC), is a leading cause of mortality worldwide. In China, a dramatic increase in the incidence of NSCLC is expected in the next 20 years (Molina et al. Mayo Clin Proc. 2008;83:584­594). Mutated epidermal growth factor receptor (EGFR) status is a known predictor of response to tyrosine kinase inhibitors (TKIs), and immunohistochemistry may be a less costly way of predicting presence of mutation. In this study, mutation analysis of EGFR in 218 cases of NSCLC was performed. One hundred thirty tissue samples were examined via immunohistochemistry of p-EGFR (Y1045 and Y1068) and correlated with mutation status. Mutations were seen in 29% of patients, and were correlated with female sex, nonsmoking history, and adenocarcinoma histology. Phosphorylation at Y1045 was noted in 52% of cases, but in 71% of cases with EGFR mutation (P = .003). Phosphorylation of Y1068 was seen in 55% of cases but in 73% of cases with EGFR mutation (P = .006). This study correlating EGFR mutation with p-EGFR expression in resected NSCLC is one of the largest to date, although TKI response could not be assessed. The data show that, among Chinese patients, detection of p-1045 and p-1068 expression with immunohistochemistry predicts EGFR mutations. Immunohistochemical analysis of p-EGFR may be useful to predict responses to TKI therapy, although future studies are necessary.

Role of TG2 and TGF-ß1 in the pathogenesis of human breast cancer.

The present study analyzed the role of transforming growth factor-ß1 (TGF-ß1) and tissue transglutaminase (TG2) in breast cancer, as well as their protein levels in MCF-7 cells treated with cisplatin. In addition, the present study investigated the effects of TG2 and TGF-ß1 in MCF-7 cells following TGF-ß1 and TG2 inhibition or TGF-ß1 induction. The protein levels of TG2 and TGF-ß1 in breast cancer tissues and in MCF-7 cells treated with cisplatin, TG2 and TGF-ß1 inhibitors or 10 ng/ml TGF-ß1 were analyzed by immunohistochemical staining, immunofluorescence and western blotting. The results revealed that the expression levels of TG2 and TGF-ß1 in breast cancer tissues were significantly higher compared with those in paracancerous tissues. The fluorescence intensity of TG2 and TGF-ß1 in MCF-7 cells treated with cisplatin was lower compared with that in untreated MCF-7 cells. Using bioinformatics analysis, the present study predicted that TGF-ß1 may be associated with TG2. In addition, the expression levels of TGF-ß1 and TG2 in MCF-7 cells treated with inhibitors of TGF-ß1 and TG2 were lower compared with those in untreated MCF-7 cells. By contrast, the expression levels of TGF-ß1 and TG2 in MCF-7 cells treated with TGF-ß1 were higher compared with those in untreated MCF-7 cells. Therefore, the present study demonstrated that TGF-ß1 and TG2 may serve an important role in breast cancer tissues and in MCF-7 cells. In addition, it was revealed that TG2 and TGF-ß1 may have a synergistic role in MCF-7 cells.

Stress-Induced Functional Alterations in Amygdala: Implications for Neuropsychiatric Diseases.

The amygdala plays a major role in the processing of physiologic and behavioral responses to stress and is characterized by gamma-aminobutyric acid (GABA)-mediated high inhibitory tone under resting state. Human and animal studies showed that stress lead to a hyperactivity of amygdala, which was accompanied by the removal of inhibitory control. However, the contribution of hyperactivity of amygdala to stress-induced neuropsychiatric diseases, such as anxiety and mood disorders, is still dubious. In this review, we will summarize stress-induced various structural and functional alterations in amygdala, including the GABA receptors expression, GABAergic transmission and synaptic plasticity. It may provide new insight on the neuropathologic and neurophysiological mechanisms of neuropsychiatric diseases.

Stress in Regulation of GABA Amygdala System and Relevance to Neuropsychiatric Diseases.

The amygdala is an almond-shaped nucleus located deep and medially within the temporal lobe and is thought to play a crucial role in the regulation of emotional processes. GABAergic neurotransmission inhibits the amygdala and prevents us from generating inappropriate emotional and behavioral responses. Stress may cause the reduction of the GABAergic interneuronal network and the development of neuropsychological diseases. In this review, we summarize the recent evidence investigating the possible mechanisms underlying GABAergic control of the amygdala and its interaction with acute and chronic stress. Taken together, this study may contribute to future progress in finding new approaches to reverse the attenuation of GABAergic neurotransmission induced by stress in the amygdala.

Molecular markers' progress of breast cancer treatment efficacy.

Breast cancer is a famous malignant tumor which is caused by varieties of mutation in multiple genes. In order to detect breast cancer in an earlier time and take appropriate treatment which includes  predicting treatment efficacy, we need a more accurate method of discovering the occurrence of breast cancer. With the development of molecular biology and biological detection technologies continue to emerge, molecular markers of breast cancer have gaining more and more widespread attention, and combining with molecular markers of breast cancer in clinical characteristic of individual treatment for breast cancer has become possible. In this paper, we will focus on the advances about molecular markers associated with treatment efficacy in recent years.