Novel Method to Reliably Determine the Photon Helicity in B→K_{1}γ.

A sizable right-handed photon polarization in b→sγ is a clear signal for new physics. In this Letter, we point out that the photon helicity in b→sγ can be unambiguously extracted by combining the measurements in B→K_{1}γ and the Cabibbo-favored D→K_{1}e^{+}ν decay. We propose a ratio of up-down asymmetries in D→K_{1}e^{+}ν to quantify the hadronic effects. A method for measuring, in experiment, the involved partial decay widths in the ratio is discussed, and experimental facilities like BESIII, Belle-II and LHCb are likely to measure this ratio. We also give the angular distribution that is useful for extracting the photon polarization in the presence of different kaon resonances.

Scoparone attenuates angiotensin II-induced extracellular matrix remodeling in cardiac fibroblasts.

Scoparone is a biologically active constituent isolated from Artemisia capillaris and possesses a variety of pharmacological activities, such as anti-inflammatory, anti-tumor, anti-allergic and anti-cardiovascular activities. However, there are no studies focusing on the effects of scoparone against cardiac fibrosis. Therefore, the aim of this study was to investigate the effects of scoparone on Angiotensin II (Ang II)-induced extracellular matrix (ECM) remodeling and its possible mechanism in cardiac fibroblasts (CFs). Our results demonstrated that scoparone effectively attenuated CFs proliferation in Ang II-stimulated CFs. Scoparone also prevented the differentiation of CFs to myofibroblasts and ECM proteins (type I collagen and fibronectin) expression in Ang II-stimulated CFs. Furthermore, scoparone prevented Ang II-induced the activation of TGF-ß1/Smad signalling in CFs. Taken together, these studies indicated that scoparone attenuated Ang II-induced ECM remodeling in CFs, at least in part, by inhibiting TGF-ß1/Smad signalling. These findings suggest that scoparone may be used a novel therapeutic agent against cardiac fibrosis.

CP Asymmetries in Charm Decays into Neutral Kaons.

We find a new CP-violation effect in charm decays into neutral kaons, which results from the interference between two tree (Cabibbo-favored and doubly Cabibbo-suppressed) amplitudes with the mixing of final-state mesons. This effect, estimated to be of an order of 10^{-3}, is much larger than the direct CP asymmetries in these decays, but missed in the literature. It can be revealed by measuring the difference of the time-dependent CP asymmetries in the D^{+}→π^{+}K_{S}^{0} and D_{s}^{+}→K^{+}K_{S}^{0} modes, which are accessible at the LHCb and Belle II experiments. If confirmed, the new effect has to be taken into account, as the above direct CP asymmetries are used to search for new physics.

MnasNet-SimAM: An Improved Deep Learning Model for the Identification of Common Wheat Diseases in Complex Real-Field Environments.

Deep learning approaches have been widely applied for agricultural disease detection. However, considerable challenges still exist, such as low recognition accuracy in complex backgrounds and high misjudgment rates for similar diseases. This study aimed to address these challenges through the detection of six prevalent wheat diseases and healthy wheat in images captured in a complex natural context, evaluating the recognition performance of five lightweight convolutional networks. A novel model, named MnasNet-SimAM, was developed by combining transfer learning and an attention mechanism. The results reveal that the five lightweight convolutional neural networks can recognize the six different wheat diseases with an accuracy of more than 90%. The MnasNet-SimAM model attained an accuracy of 95.14%, which is 1.7% better than that of the original model, while only increasing the model's parameter size by 0.01 MB. Additionally, the MnasNet-SimAM model reached an accuracy of 91.20% on the public Wheat Fungi Diseases data set, proving its excellent generalization capacity. These findings reveal that the proposed model can satisfy the requirements for rapid and accurate wheat disease detection.

Linear Fuzzy Information-Granule-Based Fuzzy C-Means Algorithm for Clustering Time Series.

This article aims to design a trend-oriented-granulation-based fuzzy C -means (FCM) algorithm that can cluster a group of time series at an abstract (granular) level. To achieve a better trend-oriented granulation of a time series, l1 trend filtering is firstly carried out to result in segments which are then optimized by the proposed segment merging algorithm. By constructing a linear fuzzy information granule (LFIG) on each segment, a granular time series which well reflects the linear trend characteristic of the original time series is produced. With the novel designed distance that can well measure the trend similarity of two LFIGs, the distance between two granular time series is calculated by the modified dynamic time warping (DTW) algorithm. Based on this distance, the LFIG-based FCM algorithm is developed for clustering time series. In this algorithm, cluster prototypes are iteratively updated by the specifically designed granule splitting and merging algorithm, which allows the lengths of prototypes to change in the process of iteration. This overcomes the serious drawback of the existing approaches, where the lengths of prototypes cannot be changed. Experimental studies demonstrate the superior performance of the proposed algorithm in clustering time series with different shapes or trends.

Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting.

A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, causal relationships among concepts of the FCM remain unchanged. However, causal relationships may change in time. Therefore, we propose a new learning method for training an FCM resulting in an adaptive FCM which consists of several sub-FCMs. It can select different sub-FCMs at different moments. In an active processing scenario, in which we deal with a large-scale time series with new data being continuously generated, a forecasting model built on the old data should be updated when the new data arrive. Furthermore, retraining an FCM from scratch entails increasing computing overhead that will become a serious obstacle in many practical scenarios. To overcome the above-mentioned shortcomings, this study offers an original design setting in which the FCM is updated by knowledge-guidance learning mechanism for the first time. Compared with the existing classical forecasting models, the proposed model shows higher accuracy and efficiency. Its increased performance is demonstrated through a series of reported experimental studies.

Comprehensive Analysis Revealed that CDKN2A is a Biomarker for Immune Infiltrates in Multiple Cancers.

The CDKN2A (cyclin dependent kinase inhibitor 2A/multiple tumor suppressor 1) gene, also known as the P16 gene, encodes multiple tumor suppressor 1 (MTS1), which belongs to the INK4 family. In tumor tissue, CDKN2A has a high expression level compared with normal tissue and reflects prognosis in tumor patients. Our research targeted the analysis of CDKN2A expression in 33 tumors and clinical parameters, patient prognosis and tumor immunity roles. The CDKN2A expression level was significantly correlated with the tumor mutation burden (TMB) in 10 tumors, and the expression of CDKN2A was also correlated with MSI (microsatellite instability) in 10 tumors. CDKN2A expression was associated with infiltrating lymphocyte (TIL) levels in 22 pancancers, thus suggesting that CDKN2A expression is associated with tumor immunity. Enrichment analysis indicated that CDKN2A expression was involved in natural killer cell-mediated cytotoxicity pathways, antigen processing and presentation, olfactory transduction pathways, and regulation of the autophagy pathway in multiple cancers. CDKN2A was significantly associated with several immune cell infiltrates in pantumors. CDKN2A may serve as a promising prognostic biomarker and is associated with immune infiltrates across cancers.

[Nerve remodeling in a canine model of atrial fibrillation induced by 48 hours right atrial pacing].

OBJECTIVE: To evaluate the nerve remodeling induced by 48 hours right atrial pacing in a canine model. METHODS: Rapid right atrial pacing (600 beats/min) was performed in 6 mongrel dogs of either sex for 48 hours to induce sustained atrial fibrillation (AF). Six dogs without pacing served as controls. Cardiac nerves were immunocytochemically stained using anti-growth-associated protein 43 (GAP43) and anti-choline acetyltransferase (CHAT) antibodies to compare nerve sprouting and pneumogastric nerve remodeling between the 2 groups. RESULTS: In dogs with AF, the GAP43-positive and CHAT-positive nerve densities in the left atrium, left auricular appendage, right atrium and right auricular appendage were significantly higher than in control animals (all P < 0.05). Moreover, nerve density was significantly higher in the right atrium than in the left atrium in dogs with AF. Microscopic examinations revealed an inhomogeneous distribution of cardiac nerves. CONCLUSION: Significant nerve sprouting and pneumogastric nerve remodeling were evidenced in the right and left atrium in a canine model of sustained AF induced by 48 hours right atrial pacing.

Long non-coding RNA DRHC inhibits the proliferation of cancer cells in triple negative breast cancer by downregulating long non-coding RNA HOTAIR.

Long non-coding RNA (lncRNA) downregulated in hepatocellular carcinoma (DRHC) is a tumor suppressor in liver cancer. However, the role of this lncRNA in breast cancer has not been investigated. The present study revealed that lncRNA DRHC was downregulated and lncRNA Hox transcript antisense RNA (HOTAIR) was upregulated in tumor tissues compared with adjacent healthy tissues in patients with triple negative breast cancer (TNBC). Expression levels of lncRNA DRHC and lncRNA HOTAIR were negatively correlated in tumor tissues but not in adjacent healthy tissues. The lncRNA DRHC expression level was correlated with tumor size but not tumor metastasis. In vitro overexpression of lncRNA DRHC in TNBC cell lines resulted in decreased expression of lncRNA HOTAIR; however, lncRNA HOTAIR overexpression did not affect the expression level of lncRNA DRHC. Overexpression of lncRNA DRHC inhibited, while overexpression of lncRNA HOTAIR promoted the proliferation of the TNBC cell lines. In addition, lncRNA HOTAIR overexpression attenuated the inhibitory effects of lncRNA DRHC overexpression on cancer cell proliferation. The results obtained in the current study suggested that lncRNA DRHC may inhibit the proliferation of TNBC cells by downregulating the expression of lncRNA HOTAIR.

DH334, a beta-carboline anti-cancer drug, inhibits the CDK activity of budding yeast.

The beta-carboline alkaloids present in medicinal plants, such as Peganum harmala and Eurycoma longifolia, have recently drawn attention due to their antitumor activities. Further mechanistic studies indicate that beta-carboline derivatives inhibit DNA topoisomerases and interfere with DNA synthesis. Moreover, some beta-carboline compounds are specific inhibitors of cyclin dependent kinases (CDKs). In this study we used budding yeast as a model system to investigate the antitumor mechanism of beta-carboline drugs. We found that DH334, a beta-carboline derivative, inhibits the growth of budding yeast. Strikingly, deletion of SIC1, which encodes the budding yeast CDK inhibitor, results in resistance to DH334. In contrast, yeast cells defective for Sic1 degradation exhibit morepronounced sensitivity to DH334. The presence of DH334 causes accumulation of yeast cells in G(1) phase, indicating that DH334 blocks cell cycle initiation. We further demonstrated that DH334 inhibits CDK activity as indicated by the decreased phosphorylation of a CDK substrate. All these data suggest that the inhibition of CDK contributes to the toxicity of beta-carboline derivatives to budding yeast. DH334 also inhibits the kinase activity of Cdk2/CyclinA in vitro. Therefore, we speculate that the antitumor activity of beta-carboline drugs could be attributable to their inhibition of CDK.