Edited by Tamas Dalmay

We employed an α-hemolysin (α-HL) nanopore as a single-molecule tool to investigate the effects of initial structure on the amyloidosis process. The differences in the initial structure of two β-amyloid (Aβ) peptides (Aβ25-35 and Aβ35-25) could be distinguished in real-time due to their characteristic blockades. More importantly, the distinct aggregate dynamics for these two kinds of Aβ fragments can be readily analyzed by monitoring the blockade frequency over time.

Recent advances in lung cancer biology presuppose their inflammatory origin. Thus, CRP is regarded to play a key role in the development of lung cancer. Nevertheless, this interesting hypothesis and the role of inflammation in tumor biology remain complex and incompletely sure. Meanwhile, the association between CRP and risk of lung cancer was not stable in many published results. This study was conducted to evaluate the association between serum CRP and SNPs in the aspect of lung cancer risks, in order to assess its possible diagnostic and prognostic importance. We conducted a case-control study of 96 patients newly diagnosed of lung cancer and 124 controls in this research. Controls were individuals matched to lung cancer cases on age, gender and tobacco use. In order to increase the statistical power, never smokers were matched to patients by using a 3:1 ratio, whereas former and current smokers were matched equal to the patients. CRP concentrations were measured using a chemiluminescent immunoassay, and SNPs were assessed at five loci within the CRP gene (rs1417938, rs1800947, rs1205, rs2808630 and rs3093077) as part of a Golden Gate assay. Logistic regression was used to calculate OR and 95 % CI for lung cancer. CRP concentrations tended to be in positive association with lung cancer risk in our research (Q4 vs Q1: OR = 2.11, 95 % CI, 1.66-2.91, p trend < 0.01). Although CRP SNPs were related to CRP levels, they were not associated with lung cancer risk. In combined analyses, we observed a significant interaction (p (interaction) = 0.02) that positive associations were suggestive in younger (Q4 vs Q1: OR = 1.65, 95 % CI, 1.02-2.67, p trend = 0.18) and older individuals (Q4 vs Q1: OR = 2.66, 95 % CI, 1.45-3.98 p trend = 0.42). The risks of lung cancer were higher with elevated CRP levels among former smokers and current smokers. High levels of CRP were associated with increasing lung cancer risk, suggesting that CRP could be used as surrogate biomarker of angiogenesis and prognosis in lung cancer.

The nanopore technique employs a nanoscale cavity to electrochemically confine individual molecules, achieving ultrasensitive single-molecule analysis based on evaluating the amplitude and duration of the ionic current. However, each nanopore sensing interface has its own intrinsic sensing ability, which does not always efficiently generate distinctive blockade currents for multiple analytes. Therefore, analytes that differ at only a single site often exhibit similar blockade currents or durations in nanopore experiments, which often produces serious overlap in the resulting statistical graphs. To improve the sensing ability of nanopores, herein we propose a novel shapelet-based machine learning approach to discriminate mixed analytes that exhibit nearly identical blockade current amplitudes and durations. DNA oligomers with a single-nucleotide difference, 5′-AAAA-3′ and 5′-GAAA-3′, are employed as model analytes that are difficult to identify in aerolysin nanopores at 100 mV. First, a set of the most informative and discriminative segments are learned from the time-series data set of blockade current signals using the learning time-series shapelets (LTS) algorithm. Then, the shapelet-transformed representation of the signals is obtained by calculating the minimum distance between the shapelets and the original signals. A simple logistic classifier is used to identify the two types of DNA oligomers in accordance with the corresponding shapelet-transformed representation. Finally, an evaluation is performed on the validation data set to show that our approach can achieve a high F 1 score of 0.933. In comparison with the conventional statistical methods for the analysis of duration and residual current, the shapelet-transformed representation provides clearly discriminated distributions for multiple analytes. Taking advantage of the robust LTS algorithm, one could anticipate the real-time analysis of nanopore events for the direct identification and quantification of multiple biomolecules in a complex real sample (e.g., serum) without labels and time-consuming mutagenesis.

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A systematic evaluation of the performance of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau is conducted using data from the Cloud-to-Ground Lightning Location System (CGLLS) developed by the State Grid Corporation of China for 2013–15 and lightning data from the satellite-based Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) for 2014–15. The average spatial location separation magnitudes in the midsouthern Tibetan Plateau (MSTP) region between matched WWLLN and CGLLS strokes and over the whole Tibetan Plateau between matched WWLLN and LIS flashes were 9.97 and 10.93 km, respectively. The detection efficiency (DE) of the WWLLN rose markedly with increasing stroke peak current, and the mean stroke peak currents of positive and negative cloud-to-ground (CG) lightning detected by the WWLLN in the MSTP region were 62.43 and −56.74 kA, respectively. The duration, area, and radiance of the LIS flashes that were also detected by the WWLLN were 1.27, 2.65, and 4.38 times those not detected by the WWLLN. The DE of the WWLLN in the MSTP region was 9.37% for CG lightning and 2.58% for total lightning. Over the Tibetan Plateau, the DE of the WWLLN for total lightning was 2.03%. In the MSTP region, the CG flash data made up 71.98% of all WWLLN flash data. Based on the abovementioned results, the ratio of intracloud (IC) lightning to CG lightning in the MSTP region was estimated to be 4.05.

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