A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

Baseline neutrophil-lymphocyte ratio is associated with survival for infant living donor liver transplantation for biliary atresia.

Living donor liver transplantation (LDLT) in infants for congenital biliary atresia (BA) poses various challenges nowadays. We aim to investigate independent preoperative risk factors for LDLT in infants. We retrospectively analyzed medical records of infant patients who underwent LDLT surgery for BA from 1 July 2014 to 31 December 2016. Cox regression was used to explore risk factors. The Kaplan-Meier method was used to calculate the recipient and graft survival, and subgroup analysis was then applied according to the risk factors. Independent t test or Mann-Whitney U test was applied for comparison of certain factors between survival patients and death. A total of 345 infant LDLT for BA were included in the analysis. In the multivariate Cox-regression model, 3 factors were determined as independent risk factors for recipient and graft survival, there were neutrophil-lymphocyte ratio (NLR), pediatric end-stage liver disease (PELD), and recipient age. The HR (95% CI) of baseline NLR for recipient and graft survival were 1.25 (1.12-1.38) and 1.25 (1.13-1.39), with all P < .0001. Kaplan-Meier curves for NLR using different cut-offs (1.5; 1, 2) suggested that higher baseline NLR was significantly associated with recipient and graft survival. The subgroup analysis indicated that for infants with elevated NLR, the recipient survival was significantly lower when their age >6 months or PELD >20. Our results indicate that infants with higher baseline NLR value may have lower survival rate 3 years after transplantation. Further investigations about broaden the application of pre- and post-transplant NLR to guide nutrition intervention and immunosuppression therapy are necessary.

Hybrid model for efficient prediction of poly(A) signals in human genomic DNA.

Polyadenylation signals (PAS) are found in most protein-coding and some non-coding genes in eukaryotes. Their accurate recognition improves understanding gene regulation mechanisms and recognition of the 3'-end of transcribed gene regions where premature or alternate transcription ends may lead to various diseases. Although different methods and tools for in-silico prediction of genomic signals have been proposed, the correct identification of PAS in genomic DNA remains challenging due to a vast number of non-relevant hexamers identical to PAS hexamers. In this study, we developed a novel method for PAS recognition. The method is implemented in a hybrid PAS recognition model (HybPAS), which is based on deep neural networks (DNNs) and logistic regression models (LRMs). One of such models is developed for each of the 12 most frequent human PAS hexamers. DNN models appeared the best for eight PAS types (including the two most frequent PAS hexamers), while LRM appeared best for the remaining four PAS types. The new models use different combinations of signal processing-based, statistical, and sequence-based features as input. The results obtained on human genomic data show that HybPAS outperforms the well-tuned state-of-the-art Omni-PolyA models, reducing the classification error for different PAS hexamers by up to 57.35% for 10 out of 12 PAS types, with Omni-PolyA models being better for two PAS types. For the most frequent PAS types, 'AATAAA' and 'ATTAAA', HybPAS reduced the error rate by 35.14% and 34.48%, respectively. On average, HybPAS reduces the error by 30.29%. HybPAS is implemented partly in Python and in MATLAB available at https://github.com/EMANG-KAUST/PolyA_Prediction_LRM_DNN.