I-DNAN6mA: Accurate Identification of DNA N6-Methyladenine Sites Using the Base-Pairing Map and Deep Learning.

The recent discovery of numerous DNA N6-methyladenine (6mA) sites has transformed our perception about the roles of 6mA in living organisms. However, our ability to understand them is hampered by our inability to identify 6mA sites rapidly and cost-efficiently by existing experimental methods. Developing a novel method to quickly and accurately identify 6mA sites is critical for speeding up the progress of its function detection and understanding. In this study, we propose a novel computational method, called I-DNAN6mA, to identify 6mA sites and complement experimental methods well, by leveraging the base-pairing rules and a well-designed three-stage deep learning model with pairwise inputs. The performance of our proposed method is benchmarked and evaluated on four species, i.e., Arabidopsis thaliana, Drosophila melanogaster, Rice, and Rosaceae. The experimental results demonstrate that I-DNAN6mA achieves area under the receiver operating characteristic curve values of 0.967, 0.963, 0.947, 0.976, and 0.990, accuracies of 91.5, 92.7, 88.2, 0.938, and 96.2%, and Mathew's correlation coefficient values of 0.855, 0.831, 0.763, 0.877, and 0.924 on five benchmark data sets, respectively, and outperforms several existing state-of-the-art methods. To our knowledge, I-DNAN6mA is the first approach to identify 6mA sites using a novel image-like representation of DNA sequences and a deep learning model with pairwise inputs. I-DNAN6mA is expected to be useful for locating functional regions of DNA.

Ultrathin Polymer Nanofibrils for Solar-Blind Deep Ultraviolet Light Photodetectors Application.

Solar-blind deep ultraviolet photodetectors (DUVPDs) based on conventional inorganic ultrawide bandgap semiconductors (UWBS) have shown promising application in various civil and military fields and yet they can hardly be used in wearable optoelectronic devices and systems for lack of mechanical flexibility. In this study, we report a non-UWBS solar-blind DUVPD by designing ultrathin polymer nanofibrils with a virtual ultrawide bandgap, which was obtained by grafting P3HT with PHA via a polymerization process. Optoelectronic analysis reveals that the P3HT-b-PHA nanofibrils are sensitive to DUV light with a wavelength of 254 nm but are virtually blind to both 365 nm and other visible light illuminations. The responsivity is 120 A/W with an external quantum efficiency of up to 49700%, implying a large photoconductive gain in the photoresponse process. The observed solar-blind DUV photoresponse is associated with the resonant mode due to the leakage mode of the ultrathin polymer nanofibrils. Moreover, a flexible image sensor composed of 10 × 10 pixels can also be fabricated to illustrate their capability for image sensing application. These results signify that the present ultrathin P3HT-b-PHA nanofibrils are promising building blocks for assembly of low-cost, flexible, and high-performance solar-blind DUVPDs.

The relationship between SPARC expression in primary tumor and metastatic lymph node of resected pancreatic cancer patients and patients' survival.

BACKGROUND: Previous researches in pancreatic cancer demonstrated a negative correlation between secreted protein acidic and rich in cysteine (SPARC) expression in primary tumor and survival, but not for SPARC expression in lymph node. In the present study, we aimed to evaluate the SPARC expression in various types of tissues and its impact on patients' prognosis. METHODS: The expression of SPARC was examined by immunohistochemistry in resected pancreatic cancer specimens. Kaplan-Meier analyses and Cox proportional hazards regression were applied to assess the mortality risk. RESULTS: A total of 222 tissue samples from 73 patients were collected to evaluate the SPARC expression, which included 73 paired primary tumor and adjacent normal tissues, 38 paired metastatic and normal lymph nodes. The proportion of positive SPARC expression in metastatic lymph node was high (32/38), whereas in normal lymph node it was negative (0/38). Positive SPARC expression in primary tumor cells was associated with a significantly decreased overall survival (P=0.007) and disease-free survival (P=0.003), whereas in other types of tissues it did not show a predictive role for prognosis. Univariate and multivariate analyses both confirmed this significance. CONCLUSION: SPARC can serve a dual function role as both predictor for prognosis and potentially biomarker for lymph node metastasis in resected pancreatic cancer patients.

Ense-i6mA: Identification of DNA N6-methyl-adenine Sites Using XGB-RFE Feature Se-lection and Ensemble Machine Learning.

DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, K-mer nucleotide frequency, and K-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, to our knowledge, it is the first time that eXtreme gradient boosting coupled with recursive feature elimination is applied to 6mA sites prediction domain to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.

TMPRSS4 facilitates epithelial-mesenchymal transition of hepatocellular carcinoma and is a predictive marker for poor prognosis of patients after curative resection.

TMPRSS4 (Transmembrane protease serine 4) is up-regulated in a broad spectrum of cancers. However, little is known about the biological effects of TMPRSS4 on hepatocellular carcinoma (HCC) and the related mechanisms. In the present study, we found that overexpression of TMPRSS4 significantly promoted the invasion, migration, adhesion and metastasis of HCC. Further more, TMPRSS4 induced EMT of HCC, which was mediated via snail and slug as a result of Raf/MEK/ERK1/2 activation, and inhibition of ERK1/2 activation by its inhibitor was associated with reduced cell invasion and reversion of EMT. In addition, we demonstrated that TMPRSS4 remarkably suppressed the expression of RECK, an inhibitor of angiogenesis, and drastically induced tumor angiogenesis and growth. More important, in clinical HCC specimens, TMPRSS4 expression was significantly correlated with tumor staging and was inversely correlated with E-cadherin and RECKS expression. Expression of TMPRSS4 is significantly associated with HCC progression and is an independent prognostic factor for postoperative worse survival and recurrence. In conclusion, TMPRSS4 functions as a positive regulator of Raf/MEK/ERK1/2 pathway and promotes HCC progression by inducing EMT and angiogenesis. The increase of TMPRSS4 expression may be a key event for HCC progression and may be regarded as a potential prognostic marker for HCC.