We describe our experience with 20 patients undergoing 1-stage correction of an anterior urethral stricture using a buccal mucosa patch graft. This technique was used for treatment of short strictures (1 to 2 cm.) that usually required a 2 to 4 cm. repair, making excision and end-to-end anastomosis impractical. Results were excellent in 18 patients, while 2 required revision for recurrent stricture. Urethrocutaneous fistulas and diverticulas were not encountered in our series. The buccal mucosa patch graft is hairless and, therefore, it can tolerate trauma and infection adequately. This technique represents a reasonable alternative when penile skin cannot be used or endoscopic manipulation is not indicated.

Electroencephalogram (EEG) is an effective indicator for the detection of driver fatigue. Due to the significant differences in EEG signals across subjects, and difficulty in collecting sufficient EEG samples for analysis during driving, detecting fatigue across subjects through using EEG signals remains a challenge. EasyTL is a kind of transfer-learning model, which has demonstrated better performance in the field of image recognition, but not yet been applied in cross-subject EEG-based applications. In this paper, we propose an improved EasyTL-based classifier, the InstanceEasyTL, to perform EEG-based analysis for cross-subject fatigue mental-state detection. Experimental results show that InstanceEasyTL not only requires less EEG data, but also obtains better performance in accuracy and robustness than EasyTL, as well as existing machine-learning models such as Support Vector Machine (SVM), Transfer Component Analysis (TCA), Geodesic Flow Kernel (GFK), and Domain-adversarial Neural Networks (DANN), etc.

Fatigued driving is one of the main causes of traffic accidents. The electroencephalogram (EEG)-based mental state analysis method is an effective and objective way of detecting fatigue. However, as EEG shows significant differences across subjects, effectively “transfering” the EEG analysis model of the existing subjects to the EEG signals of other subjects is still a challenge. Domain-Adversarial Neural Network (DANN) has excellent performance in transfer learning, especially in the fields of document analysis and image recognition, but has not been applied directly in EEG-based cross-subject fatigue detection. In this paper, we present a DANN-based model, Generative-DANN (GDANN), which combines Generative Adversarial Networks (GAN) to enhance the ability by addressing the issue of different distribution of EEG across subjects. The comparative results show that in the analysis of cross-subject tasks, GDANN has a higher average accuracy of 91.63% in fatigue detection across subjects than those of traditional classification models, which is expected to have much broader application prospects in practical brain–computer interaction (BCI).

Hepatic cancer stem cells (HCSCs) are considered as main players for the hepatocellular carcinoma (HCC) initiation, metastasis, drug resistance and recurrence. There is a growing evidence supporting the down-regulated miRNAs in HCSCs as key suppressors for the stemness traits, but still more details are vague about how these miRNAs modulate the HCC development. To uncover some of these miRNA regulatory aspects in HCSC, we compiled 15 down-regulated miRNA and their validated and predicted up-regulated targets in HCSC. The targets were enriched for several cancer cell stemness hallmarks and CSC pre-metastatic niche, which support these miRNAs role in suppression of HCSCs neoplastic transformation. Further, we constructed miRNA-Transcription factor (TF) regulatory networks, which provided new insights on the role of the proposed miRNA-TF co-regulation in the cancer stemness axis and its cross talk with the surrounding microenvironment. Our analysis revealed HCSC important hubs as candidate regulators for targeting hepatic cancer stemness such as, miR-148a, miR-214, E2F family, MYC and SLC7A5. Finally, we proposed a possible model for miRNA and TF co-regulation of HCSC signaling pathways. Our study identified an HCSC signature and set bridges between the reported results to give guide for future validation of HCC therapeutic strategies avoiding drug resistance.

DNA microarrays allow simultaneous measurements of expression levels for a large number of genes across a number of different experimental conditions (samples). The algorithms for mining association rules are used to reveal biologically relevant associations between different genes under different experimental samples. This paper presents a new column-enumeration based method algorithm (abbreviated by MCR-Miner) for mining maximal high confidence association rules for up/down-expressed genes. MCR-Miner algorithm uses an efficient maximal association rules tree data structure (abbreviated by MAR-Tree). MAR-tree enumerates (lists) all genes with their binary representations, the binary representation of a gene saves the status (normal, up, and downexpressed) of a gene in all experiments. The binary representation has many advantages, scan the dataset only once, the measurements of confidences for association rules are made in one step, and it makes MCR-Miner algorithm easily finds all maximal high confidence association rules. In the experimental results on a real microarray datasets, MCR-Miner algorithm attained very promising results and outperformed other counterparts.

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Frequent itemset mining (FIM) is the crucial task in mining association rules that finds all frequent k-itemsets in the transaction dataset from which all association rules are extracted. In the big-data era, the datasets are huge and rapidly expanding, so adding new transactions as time advances results in periodic changes in correlations and frequent itemsets present in the dataset. Re-mining the updated dataset is impractical and costly. This problem is solved via incremental frequent itemset mining. Numerous researchers view the new transactions as a distinct dataset (partition) that may be mined to obtain all of its frequent item sets. The extracted local frequent itemsets are then combined to create a collection of global candidates, where it is possible to estimate the support count of the combined candidates to avoid re-scanning the dataset. However, these works are hampered by the growth of a huge number of candidates, and the support count estimation is still imprecise. In this paper, the Closed Candidates-based Incremental Frequent Itemset Mining approach, or CC-IFIM, has been proposed to decrease candidate generation and improve the accuracy of the global frequent itemsets that are retrieved. The proposed approach is able to prune several produced candidates in earlier steps before performing any further computations. To improve the accuracy of the computation of the support count of the produced candidates, the similarity between partitions has been evaluated using just the local closed candidates rather than all candidates. The experimental findings demonstrated that the CC-IFIM approach is superior to its competitors in terms of efficiency and accuracy.