High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

Blockchain technology is a sustainable technology that offers a high level of security for many industrial applications. Blockchain has numerous benefits, such as decentralisation, immutability and tamper-proofing. Blockchain is composed of two processes, namely, mining (the process of adding a new block or transaction to the global public ledger created by the previous block) and validation (the process of validating the new block added). Several consensus protocols have been introduced to validate blockchain transactions, Proof-of-Work (PoW) and Proof-of-Stake (PoS), which are crucial to cryptocurrencies, such as Bitcoin. However, these consensus protocols are vulnerable to double-spending attacks. Amongst these attacks, the 51% attack is the most prominent because it involves forking a blockchain to conduct double spending. Many attempts have been made to solve this issue, and examples include delayed proof-of-work (PoW) and several Byzantine fault tolerance mechanisms. These attempts, however, suffer from delay issues and unsorted block sequences. This study proposes a hybrid algorithm that combines PoS and PoW mechanisms to provide a fair mining reward to the miner/validator by conducting forking to combine PoW and PoS consensuses. As demonstrated by the experimental results, the proposed algorithm can reduce the possibility of intruders performing double mining because it requires achieving 100% dominance in the network, which is impossible.

No abstract

Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position’s reactivity and exhibits interesting behavior on neighboring bases in the sequence.

Table of contentsA1 Hope and despair in the current treatment of nasopharyngeal cancerIB TanI1 NPC international incidence and risk factorsEllen T ChangI2 Familial nasopharyngeal carcinoma and the use of biomarkersChien-Jen Chen, Wan-Lun Hsu, Yin-Chu ChienI3 Genetic susceptibility risk factors for sporadic and familial NPC: recent findingsAllan HildesheimI5 Genetic and environmental risk factors for nasopharyngeal cancer in Southeast AsiaJames D McKay, Valerie Gaborieau, Mohamed Arifin Bin Kaderi, Dewajani Purnomosari, Catherine Voegele, Florence LeCalvez-Kelm, Graham Byrnes, Paul Brennan, Beena DeviI6 Characterization of the NPC methylome identifies aberrant epigenetic disruption of key signaling pathways and EBV-induced gene methylationLi L, Zhang Y, Fan Y, Sun K, Du Z, Sun H, Chan AT, Tsao SW, Zeng YX, Tao QI7 Tumor exosomes and translational research in NPCPierre Busson, Claire Lhuillier, Olivier Morales, Dhafer Mrizak, Aurore Gelin, Nikiforos Kapetanakis, Nadira DelhemI8 Host manipulations of the Epstein-Barr virus EBNA1 proteinSheila Mansouri, Jennifer Cao, Anup Vaidya, and Lori FrappierI9 Somatic genetic changes in EBV-associated nasopharyngeal carcinomaLo Kwok WaiI10 Preliminary screening results for nasopharyngeal carcinoma with ELISA-based EBV antibodies in Southern ChinaSui-Hong Chen, Jin-lin Du, Ming-Fang Ji, Qi-Hong Huang, Qing Liu, Su-Mei CaoI11 EBV array platform to screen for EBV antibodies associated with NPC and other EBV-associated disordersDenise L. Doolan, Anna Coghill, Jason Mulvenna, Carla Proietti, Lea Lekieffre, Jeffrey Bethony, and Allan HildesheimI12 The nasopharyngeal carcinoma awareness program in IndonesiaRenske Fles, Sagung Rai Indrasari, Camelia Herdini, Santi Martini, Atoillah Isfandiari, Achmad Rhomdoni, Marlinda Adham, Ika Mayangsari, Erik van Werkhoven, Maarten Wildeman, Bambang Hariwiyanto, Bambang Hermani, Widodo Ario Kentjono, Sofia Mubarika Haryana, Marjanka Schmidt, IB TanI13 Current advances and future direction in nasopharyngeal cancer managementBrian O’SullivanI14 Management of juvenile nasopharyngeal cancerEnis OzyarI15 Global pattern of nasopharyngeal cancer: correlation of outcome with access to radiotherapyAnne WM LeeI16 The predictive/prognostic biomarker for nasopharyngeal carcinomaMu-Sheng ZengI17 Effect of HLA and KIR polymorphism on NPC riskXiaojiang Gao, Minzhong Tang, Pat Martin, Yi Zeng, Mary CarringtonI18 Exploring the Association between Potentially Neutralizing Antibodies against EBV Infection and Nasopharyngeal CarcinomaAnna E Coghill, Wei Bu, Hanh Nguyen, Wan-Lun Hsu, Kelly J Yu, Pei-Jen Lou, Cheng-Ping Wang, Chien-Jen Chen, Allan Hildesheim, Jeffrey I CohenI19 Advances in MR imaging in NPCAnn D KingO1 Epstein-Barr virus seromarkers and risk of nasopharyngeal carcinoma: the gene-environment interaction study on nasopharyngeal carcinoma in TaiwanYin-Chu Chien, Wan-Lun Hsu, Kelly J Yu, Tseng-Cheng Chen, Ching-Yuan Lin, Yung-An Tsou, Yi-Shing Leu, Li-Jen Laio, Yen-Liang Chang, Cheng-Ping Wang, Chun-Hun Hua, Ming-Shiang Wu, Chu-Hsing Kate Hsiao, Jehn-Chuan ...