Urban PM2.5 concentration prediction via attention-based CNN–LSTM.

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the effects of featured states at different times in the past on future PM2.5 concentration, and most fail to effectively simulate the temporal and spatial dependencies of PM2.5 concentration at the same time. With this consideration, we propose a deep learning-based method, AC-LSTM, which comprises a one-dimensional convolutional neural network (CNN), long short-term memory (LSTM) network, and attention-based network, for urban PM2.5 concentration prediction. Instead of only using air pollutant concentrations, we also add meteorological data and the PM2.5 concentrations of adjacent air quality monitoring stations as the input to our AC-LSTM. Hence, the spatiotemporal correlation and interdependence of multivariate air quality-related time-series data are learned by the CNN-LSTM network in AC-LSTM. The attention mechanism is applied to capture the importance degrees of the effects of featured states at different times in the past on future PM2.5 concentration. The attention-based layer can automatically weigh the past feature states to improve prediction accuracy. In addition, we predict the PM2.5 concentrations over the next 24 h by using air quality data in Taiyuan city, China, and compare it with six baseline methods. To compare the overall performance of each method, the mean absolute error (MAE), root-mean-square error (RMSE), and coecient of determination (R2) are applied to the experiments in this paper. The experimental results indicate that our method is capable of dealing with PM2.5 concentration prediction with the highest performance

Security and privacy in smart cities: challenges and opportunities

Smart cities are expected to improve the quality of daily life, promote sustainable development, and improve the functionality of urban systems. Now that many smart systems have been implemented, security and privacy issues have become a major challenge that requires effective countermeasures. However, traditional cybersecurity protection strategies cannot be applied directly to these intelligent applications because of the heterogeneity, scalability, and dynamic characteristics of smart cities. Furthermore, it is necessary to be aware of security and privacy threats when designing and implementing new mechanisms or systems. Motivated by these factors, we survey the current situations of smart cities with respect to security and privacy to provide an overview of both the academic and industrial fields and to pave the way for further exploration. Specifically, this survey begins with an overview of smart cities to provide an integrated context for readers. Then, we discuss the privacy and security issues in current smart applications along with the corresponding requirements for building a stable and secure smart city. In the next step, we summarize the existing protection technologies. Finally, we present open research challenges and identify some future research directions

Decreased BECN1 mRNA Expression in Human Breast Cancer is Associated With Estrogen Receptor-Negative Subtypes and Poor Prognosis

AbstractBoth BRCA1 and Beclin 1 (BECN1) are tumor suppressor genes, which are in close proximity on the human chromosome 17q21 breast cancer tumor susceptibility locus and are often concurrently deleted. However, their importance in sporadic human breast cancer is not known. To interrogate the effects of BECN1 and BRCA1 in breast cancer, we studied their mRNA expression patterns in breast cancer patients from two large datasets: The Cancer Genome Atlas (TCGA) (n=1067) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (n=1992). In both datasets, low expression of BECN1 was more common in HER2-enriched and basal-like (mostly triple-negative) breast cancers compared to luminal A/B intrinsic tumor subtypes, and was also strongly associated with TP53 mutations and advanced tumor grade. In contrast, there was no significant association between low BRCA1 expression and HER2-enriched or basal-like subtypes, TP53 mutations or tumor grade. In addition, low expression of BECN1 (but not low BRCA1) was associated with poor prognosis, and BECN1 (but not BRCA1) expression was an independent predictor of survival. These findings suggest that decreased mRNA expression of the autophagy gene BECN1 may contribute to the pathogenesis and progression of HER2-enriched, basal-like, and TP53 mutant breast cancers

Inhibitor selectivity between aldo–keto reductase superfamily members AKR1B10 and AKR1B1: Role of Trp112 (Trp111)

AbstractThe antineoplastic target aldo–keto reductase family member 1B10 (AKR1B10) and the critical polyol pathway enzyme aldose reductase (AKR1B1) share high structural similarity. Crystal structures reported here reveal a surprising Trp112 native conformation stabilized by a specific Gln114-centered hydrogen bond network in the AKR1B10 holoenzyme, and suggest that AKR1B1 inhibitors could retain their binding affinities toward AKR1B10 by inducing Trp112 flip to result in an “AKR1B1-like” active site in AKR1B10, while selective AKR1B10 inhibitors can take advantage of the broader active site of AKR1B10 provided by the native Trp112 side-chain orientation

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs

Comprehensive functional characterization of cancer–testis antigens defines obligate participation in multiple hallmarks of cancer

Tumours frequently activate genes whose expression is otherwise biased to the testis, collectively known as cancer–testis antigens (CTAs). The extent to which CTA expression represents epiphenomena or confers tumorigenic traits is unknown. In this study, to address this, we implemented a multidimensional functional genomics approach that incorporates 7 different phenotypic assays in 11 distinct disease settings. We identify 26 CTAs that are essential for tumor cell viability and/or are pathological drivers of HIF, WNT or TGFβ signalling. In particular, we discover that Foetal and Adult Testis Expressed 1 (FATE1) is a key survival factor in multiple oncogenic backgrounds. FATE1 prevents the accumulation of the stress-sensing BH3-only protein, BCL-2-Interacting Killer (BIK), thereby permitting viability in the presence of toxic stimuli. Furthermore, ZNF165 promotes TGFβ signalling by directly suppressing the expression of negative feedback regulatory pathways. This action is essential for the survival of triple negative breast cancer cells in vitro and in vivo. Thus, CTAs make significant direct contributions to tumour biology

Taxane-Platin-Resistant Lung Cancers Co-develop Hypersensitivity to JumonjiC Demethylase Inhibitors

Although non-small cell lung cancer (NSCLC) patients benefit from standard taxane-platin chemotherapy, many relapse, developing drug resistance. We established preclinical taxane-platin-chemoresistance models and identified a 35-gene resistance signature, which was associated with poor recurrence-free survival in neoadjuvant-treated NSCLC patients and included upregulation of the JumonjiC lysine demethylase KDM3B. In fact, multi-drug-resistant cells progressively increased the expression of many JumonjiC demethylases, had altered histone methylation, and, importantly, showed hypersensitivity to JumonjiC inhibitors in vitro and in vivo. Increasing taxane-platin resistance in progressive cell line series was accompanied by progressive sensitization to JIB-04 and GSK-J4. These JumonjiC inhibitors partly reversed deregulated transcriptional programs, prevented the emergence of drug-tolerant colonies from chemo-naive cells, and synergized with standard chemotherapy in vitro and in vivo. Our findings reveal JumonjiC inhibitors as promising therapies for targeting taxane-platin-chemoresistant NSCLCs.Fil: Dalvi, Maithili P.. University of Texas. Southwestern Medical Center; Estados UnidosFil: Wang, Lei. University of Texas. Southwestern Medical Center; Estados UnidosFil: Zhong, Rui. University of Texas. Southwestern Medical Center; Estados UnidosFil: Kollipara, Rahul K.. University of Texas. Southwestern Medical Center; Estados UnidosFil: Park, Hyunsil. University of Texas. Southwestern Medical Center; Estados UnidosFil: Bayo Fina, Juan Miguel. University of Texas. Southwestern Medical Center; Estados Unidos. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; ArgentinaFil: Yenerall, Paul. University of Texas. Southwestern Medical Center; Estados UnidosFil: Zhou, Yunyun. University of Texas. Southwestern Medical Center; Estados UnidosFil: Timmons, Brenda C.. University of Texas. Southwestern Medical Center; Estados UnidosFil: Rodriguez Canales, Jaime. University of Texas; Estados UnidosFil: Behrens, Carmen. Md Anderson Cancer Center; Estados UnidosFil: Mino, Barbara. University of Texas; Estados UnidosFil: Villalobos, Pamela. University of Texas; Estados UnidosFil: Parra, Edwin R.. University of Texas; Estados UnidosFil: Suraokar, Milind. University of Texas; Estados UnidosFil: Pataer, Apar. University of Texas; Estados UnidosFil: Swisher, Stephen G.. University of Texas; Estados UnidosFil: Kalhor, Neda. University of Texas; Estados UnidosFil: Bhanu, Natarajan V.. University of Pennsylvania; Estados UnidosFil: Garcia, Benjamin A.. University of Pennsylvania; Estados UnidosFil: Heymach, John V.. University of Texas; Estados UnidosFil: Coombes, Kevin. University of Texas; Estados UnidosFil: Xie, Yang. University of Texas. Southwestern Medical Center; Estados UnidosFil: Girard, Luc. University of Texas. Southwestern Medical Center; Estados UnidosFil: Gazdar, Adi F.. University of Texas. Southwestern Medical Center; Estados UnidosFil: Kittler, Ralf. University of Texas. Southwestern Medical Center; Estados UnidosFil: Wistuba, Ignacio I.. University of Texas; Estados UnidosFil: Minna, John D.. University of Texas. Southwestern Medical Center; Estados UnidosFil: Martinez, Elisabeth D.. University of Texas. Southwestern Medical Center; Estados Unido

Mining Important Nodes in Directed Weighted Complex Networks

In complex networks, mining important nodes has been a matter of concern by scholars. In recent years, scholars have focused on mining important nodes in undirected unweighted complex networks. But most of the methods are not applicable to directed weighted complex networks. Therefore, this paper proposes a Two-Way-PageRank method based on PageRank for further discussion of mining important nodes in directed weighted complex networks. We have mainly considered the frequency of contact between nodes and the length of time of contact between nodes. We have considered the source of the nodes (in-degree) and the whereabouts of the nodes (out-degree) simultaneously. We have given node important performance indicators. Through numerical examples, we analyze the impact of variation of some parameters on node important performance indicators. Finally, the paper has verified the accuracy and validity of the method through empirical network data