A nanopore counter for highly sensitive evaluation of DNA methylation and its application in in vitro diagnostics.

DNA methylation has been considered an essential epigenetic biomarker for diagnosing various diseases, such as cancer. A simple and sensitive way for DNA methylation level detection is necessary. Inspired by the label-free and ultra-high sensitivity of solid-state nanopores to double-stranded DNA (dsDNA), we proposed a nanopore counter for evaluating DNA methylation by integrating a dual-restriction endonuclease digestion strategy coupled with polymerase chain reaction (PCR) amplification. Simultaneous application of BstUI/HhaI endonucleases can ensure the full digestion of the unmethylated target DNA but shows no effect on the methylated ones. Therefore, only the methylated DNA remains intact and can trigger the subsequent PCR reaction, producing a large quantity of fixed-length PCR amplicons, which can be directly detected through glassy nanopores. By simply counting the event rate of the translocation signals, the concentration of methylated DNA can be determined to range from 1 aM to 0.1 nM, with the detection limit as low as 0.61 aM. Moreover, a 0.01% DNA methylation level was successfully distinguished. The strategy of using the nanopore counter for highly sensitive DNA methylation evaluation would be a low-cost but reliable alternative in the analysis of DNA methylation.

A novel design of DNA duplex containing programmable sensing sites for nanopore-based length-resolution reading and applications for Pb2+ and cfDNA analysis.

Glass nanopore is an ideal candidate for biosensors due to its unique advantages such as label-free analysis, single-molecule sensitivity, and easy operation. Previous studies have shown that glass nanopores can distinguish different lengths of double-stranded DNA (dsDNA) at the same time with the length-resolution ability. Based on this, we proposed a novel design of a dsDNA block containing a programmable sensing site inside, which can be programmed to respond to different target molecules and cleaved into two smaller DNA blocks. When programming the sensing site with different sequences, for example, programming it as the substrate of GR-5 DNAzyme and CRISPR-Cas12a system, the DNA block could realize Pb2+ and cfDNA detection with the length-resolution ability of the glass nanopore. This strategy achieved a Pb2+ detection range from 0.5 nM to 100 nM, with a detection limit of 0.4 nM, and a BRCA-1 detection range from 1 pM to 10 pM, with a detection limit of 1 pM. The programable sensing site is easy to design and has strong expandability, which gives full play to the advantages of glass nanopore in length-resolution ability for dsDNA, and is expected to become an optional design for biosensing strategy for the glass nanopore as a biosensing platform.

Detection of small-sized DNA fragments in a glassy nanopore by utilization of CRISPR-Cas12a as a converter system.

The fabrication of nanopores with a matched pore size, and the existence of multiple interferents make the reproducible detection of small-sized molecules by means of solid-state nanopores still challenging. A useful method to solve these problems is based on the detection of large DNA nanostructures related to the existence of small-sized targets. In particular, a DNA tetrahedron with a well-defined 3D nanostructure is the ideal candidate for use as a signal transducer. Here, we demonstrate the detection of an L1-encoding gene of HPV18 as a test DNA target sequence in a reaction buffer solution, where long single-stranded DNA linking DNA tetrahedra onto the surface of the magnetic beads is cleaved by a target DNA-activated CRISPR-cas12 system. The DNA tetrahedra are subsequently released and can be detected by the current pulse in a glassy nanopore. This approach has several advantages: (1) one signal transducer can be used to detect different targets; (2) a glassy nanopore with a pore size much larger than the target DNA fragment can boost the tolerance of the contaminants and interferents which often degrade the performance of a nanopore sensor.

A signal on-off strategy based on the digestion of DNA cubes assisted by the CRISPR-Cas12a system for ultrasensitive HBV detection in solid-state nanopores.

Solid-state nanopores have been proven as a powerful platform for label-free single-molecule analysis. However, due to its relatively low resolution and selectivity, developing biosensors with good translocation signals faces two significant challenges: (1) small-sized chemical or biological targets show difficulty in producing recognizable translocation signals because of their weak interaction with the nanopore and (2) protein interferents that widely exist in biological samples or buffers would considerably deteriorate the noise level of the nanopore, submerging the translocation signal. Herein, we demonstrate an effective way to overcome both the challenges. DNA cubes were used as signal transducers that can achieve an ultra-high (>50 : 1) signal-to-noise ratio (SNR) translocation signal, which is maintained even in protein interferent-rich buffers. A sensing strategy was constructed via hepatitis B virus (HBV) target-triggered cleavage of the component elements of the DNA cube with the assistance of the CRISPR-Cas12a technology, which caused a great drop in the translocation rate. The elements to cleave were optimized, and the sensor performance was tested in different protein stabilizer-rich buffers and human serum. Coupling with the polymerase chain reaction (PCR) pre-amplification technology, HBV-positive or -negative classification was achieved with the detection limit reaching 5 aM. It is worth noting that in our method, all reaction buffers were directly used without further optimization, which is of great help for the practical application of solid-state nanopores.

CuO@CoFe Layered Double Hydroxide Core-Shell Heterostructure as an Efficient Water Oxidation Electrocatalyst under Mild Alkaline Conditions.

Designing hierarchical electrocatalysts with superior water oxidation performance is highly desirable for the production of renewable chemical fuels. Here, we report the development of a CuO@CoFe layered double hydroxide core-shell heterostructure supported on Cu foil (CuO@CoFe-LDH/CF) as a highly active catalyst electrode for water oxidation under mild alkaline conditions. In a 0.2 M carbonate buffer solution (pH 11), it only needs a small overpotential of 213 mV to achieve 10 mA cm-2, outperforming all reported electrocatalysts at comparable testing conditions. It also shows outstanding long-term durability.

Eradicating abrupt collapse on single network with dependency groups.

The dependency among nodes has significant effects on the cascading failures of complex networks. Although the prevention of cascading failures on multilayered networks in which the failures of nodes in one layer affect the functioning of nodes in other layers has been widely investigated, the prevention of catastrophic cascade has rarely been addressed to single-layer networks where nodes are grouped and nodes within the same group are dependent on each other. For such networks, we find that it is already enough to prevent abrupt catastrophic collapses by randomly reinforcing a constant density of nodes. More importantly, we give the analytical solutions to the proportion of needed reinforced nodes for three typical networks, i.e., dependent Erdos-Rényi (ER), random regular (RR), and scale-free (SF) networks. Interestingly, the density of reinforced nodes is a constant 0.1756, which holds true for ER networks with group size 2 regardless of average degree, RR, and SF networks with a large average degree. Also, we find the elegant expression of the density with any group size. In addition, we find a hybrid phase transition behavior, which is present in RR and SF networks while absent in ER networks. Our findings might shed some new light on designing more resilient infrastructure networks.

Insertion approach: bolstering the reproducibility of electrochemical signal amplification via DNA superstructures.

For more than a decade, the backfilling approach for the immobilization of DNA probes has been routinely adopted for the construction of functional interfaces; however, reliably reproducing electrochemical signal amplification by this method is a challenge. In this research, we demonstrate that the insertion approach significantly bolsters the reproducibility of electrochemical signal amplification via DNA superstructures. The combination of the backfilling approach and the DNA superstructure formation poses a big challenge to reliably reproducing electrochemical signal amplification. In order to use the detection of Hg(2+) as a prototype of this new strategy, a thymine-rich DNA probe that is specific to mercury ion was applied in this study. The presence of Hg(2+) induces the folding of the DNA probes and inhibits the formation of DNA superstructures. By using electroactive probes ([Ru(NH3)6](3+)) that are electrostatically adsorbed onto the double strands, differential pulse voltammetry (DPV) could quantitatively confirm the presence of Hg(2+). A limit of detection (LOD) and a limit of quantification (LOQ) (LOQ) as low as 0.3 and 9.5 pM, respectively, were achieved. Furthermore, excellent selectivity and real sample analysis demonstrated the promising potential of this approach in future applications.

Acaricidal activity of petroleum ether extracts from Eupatorium adenophorum against the ectoparasitic cattle mite, Chorioptes texanus.

In this study, we evaluated the acaricidal efficacy of extracts obtained from the plant Eupatorium adenophorum against the common cattle mite Chorioptes texanus. The results showed that 95% ethanol extracts at concentrations of 1.0, 0.5, and 0.25 g/mL (w/v) were highly toxic to C. texanus in vitro, killing 100% of mites in 4 h. Similarly, petroleum ether extracts of E. adenophorum resulted in between 80 and 100% mortality of mites in vitro at concentrations of 0.1, 0.05, and 0.025 mL/mL (v/v) within 4 h. In clinical trials, all infected individuals completely recovered after two treatments administered at 7-day intervals and remained disease-free at 60 days posttreatment. The clinical effect of treatment with E. adenophorum petroleum ether extracts was similar to that of treatment with the acaricide fenvalerate. These results indicated that E. adenophorum contains novel potential acaricidal compounds that can effectively control mites in livestock.

Genetic diversity of Echinococcus granulosus in southwest China determined by the mitochondrial NADH dehydrogenase subunit 2 gene.

We evaluated genetic diversity and structure of Echinococcus granulosus by analyzing the complete mitochondrial NADH dehydrogenase subunit 2 (ND2) gene in 51 isolates of E. granulosus sensu stricto metacestodes collected at three locations in this region. We detected 19 haplotypes, which formed a distinct clade with the standard sheep strain (G1). Hence, all 51 isolates were identified as E. granulosus sensu stricto (G1-G3). Genetic relationships among haplotypes were not associated with geographical divisions, and fixation indices (Fst) among sampling localities were low. Hence, regional populations of E. granulosus in the southwest China are not differentiated, as gene flow among them remains high. This information is important for formulating unified region-wide prevention and control measures. We found large negative Fu's Fs and Tajima's D values and a unimodal mismatch distribution, indicating that the population has undergone a demographic expansion. We observed high genetic diversity among the E. granulosus s. s. isolates, indicating that the parasite population in this important bioregion is genetically robust and likely to survive and spread. The data from this study will prove valuable for future studies focusing on improving diagnosis and prevention methods and developing robust control strategies.

Sequence analysis of cytb gene in Echinococcus granulosus from Western China.

Echinococcus granulosus is the causative agent of cystic echinococcosis with medical and veterinary importance in China. Our main objective was to discuss the genotypes and genetic diversity of E. granulosus present in domestic animals and humans in western China. A total of 45 hydatid cyst samples were collected from sheep, humans, and a yak and subjected to an analysis of the sequences of mitochondrial cytochrome b (cytb) gene. The amplified PCR product for all samples was a 1,068 bp band. The phylogenetic analysis showed that all 45 samples were identified as E. granulosus (genotype G1). Ten haplotypes were detected among the samples, with the main haplotype being H1. The haplotype diversity was 0.626, while the nucleotide diversity was 0.001. These results suggested that genetic diversity was low among our samples collected from the west of China based on cytb gene analysis. These findings may provide more information on molecular characteristics of E. granulosus from this Chinese region.

State-of-the-art of lignin-derived carbon nanodots: Preparation, properties, and applications.

Lignin-derived carbon nanodots (LCNs) are nanometer-scale carbon spheres fabricated from naturally abundant lignin. Owing to rich and highly heritable graphene like π-π conjugated structure of lignin, to fabricate LCNs from it not only endows LCNs with on-demand tunable size and optical features, but also further broadens the green and chemical engineering of carbon nanodots. Recently, they have become increasingly popular in sensing, bioimaging, catalysis, anti-counterfeiting, energy storage/conversion, and others. Despite the enormous research efforts put into the ongoing development of lignin value-added utilization, few commercial LCNs are available. To have a deeper understanding of this issue, critical impacts on the preparation, properties, and applications of state-of-the-art LCNs are carefully reviewed and discussed. A concise analysis of their unique advantages, limitations for specific applications, and current challenges and outlook is conducted. We hope that this review will stimulate further advances in the functional material-oriented production of lignin.

Signal-amplification detection of small molecules by use of Mg2+-dependent DNAzyme.

Because small molecules can be beneficial or toxic in biology and the environment, specific and sensitive detection of small molecules is one of the most important objectives of the scientific community. In this study, new signal amplification assays for detection of small molecules based on Mg(2+)-dependent DNAzyme were developed. A cleavable DNA substrate containing a ribonucleotide, the ends of which were labeled with black hole quencher (BHQ) and 6-carboxyfluorescein (FAM), was used for fluorescence detection. When the small molecule of interest is added to the assay solution, the Mg(2+)-dependent DNAzyme is activated, facilitating hybridization between the Mg(2+)-dependent DNAzyme and the DNA substrate. Binding of the substrate to the DNAzyme structure results in hydrolytic cleavage of the substrate in the presence of Mg(2+) ions. The fluorescence signal was amplified by continuous cleavage of the enzyme substrate. Ochratoxin A (OTA) and adenosine triphosphate (ATP) were used as model analytes in these experiments. This method can detect OTA specifically with a detection limit as low as 140 pmol L(-1) and detect ATP specifically with a detection limit as low as 13 nmol L(-1). Moreover, this method is potentially extendable to detection of other small molecules which are able to dissociate the aptamer from the DNAzyme, leading to activation of the DNAzyme.

Identification of neglected cestode Taenia multiceps microRNAs by illumina sequencing and bioinformatic analysis.

BACKGROUND: Worldwide, but especially in developing countries, coenurosis of sheep and other livestock is caused by Taenia multiceps larvae, and zoonotic infections occur in humans. Infections frequently lead to host death, resulting in huge socioeconomic losses. MicroRNAs (miRNAs) have important roles in the post-transcriptional regulation of a large number of animal genes by imperfectly binding target mRNAs. To date, there have been no reports of miRNAs in T. multiceps. RESULTS: In this study, we obtained 12.8 million high quality raw reads from adult T. multiceps small RNA library using Illumina sequencing technology. A total of 796 conserved miRNA families (containing 1,006 miRNAs) from 170,888 unique miRNAs were characterized using miRBase (Release 17.0). Here, we selected three conserved miRNA/miRNA* (antisense strand) duplexes at random and amplified their corresponding precursors using a PCR-based method. Furthermore, 20 candidate novel miRNA precursors were verified by genomic PCR. Among these, six corresponding T. multiceps miRNAs are considered specific for Taeniidae because no homologs were found in other species annotated in miRBase. In addition, 181,077 target sites within T. multiceps transcriptome were predicted for 20 candidate newly miRNAs. CONCLUSIONS: Our large-scale investigation of miRNAs in adult T. multiceps provides a substantial platform for improving our understanding of the molecular regulation of T. multiceps and other cestodes development.

Evaluation acaricidal efficacy of botanical extract from Eupatorium adenophorum against the hard tick Haemaphysalis longicornis (Acari: Ixodidae).

The aim of this study was to evaluate the acaricidal activity of a botanical extract from Eupatorium adenophorum against the hard tick Haemaphysalis longicornis. This could result in developing effective extracts of E. adenophorum as a source of natural, low-toxicity plant-based acaricidal drugs. Adult engorged females of H. longicornis were collected from naturally infected goats. The engorged females were reared in the laboratory and their offspring (larvae and nymphs) were used as test ectoparasites. The toxic effects of botanical extracts from E. adenophorum against larvae and nymphs of H. longicornis were evaluated. The results showed that the extracts with 1.5 and 1.0g/ml (w/v) concentrations were toxic for H. longicornis, comparable to a toxic effect of 2% chlorpyrifos (positive control). The median lethal time (LT50) for larval and nymphal ticks with 1.5g/ml (w/v) concentration of extract were 0.790 (LT99=1.065) and 1.018 (LT99=10.608) hours, respectively, whereas the LT50 of 1.0g/ml (w/v) concentration were 1.445 (LT99=6.047) and 1.313 (LT99=29.932) hours for larval and nymphal ticks, respectively. At a concentration of 1.5g/ml (w/v), an acaricidal effect of 100% was achieved for both larval and nymphal ticks, while a concentration of 1.0g/ml (w/v) resulted in 100% (for larvae) and 93% (for nymphs) within a 6h period. In additional, we found that the relatively low concentration (0.5g/ml) also obtained a good acaricidal effect during the short experimental period, with 2.22 and 2.651h LT50 for larval and nymphal ticks, respectively. These results indicate that E. adenophorum contains potent acaricidal ingredients against the hard tick H. longicornis.

Balancing Constraints and Objectives by Considering Problem Types in Constrained Multiobjective Optimization.

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I; 2) II; and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning.

A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP presumes that the locations of customers and the traveling time among customers are fixed and constant. In real-life cases, however, the traffic conditions and customer requests may change over time. To find the most economic route, the decisions can be made constantly upon the time-point when the salesman completes his service of each customer. This brings in a dynamic version of the traveling salesman problem (DTSP), which takes into account the information of real-time traffic and customer requests. DTSP can be extended to a dynamic pickup and delivery problem (DPDP). In this article, we ameliorate the attention model to make it possible to perceive environmental changes. A deep reinforcement learning algorithm is proposed to solve DTSP and DPDP instances with a size of up to 40 customers in 100 locations. Experiments show that our method can capture the dynamic changes and produce a highly satisfactory solution within a very short time. Compared with other baseline approaches, more than 5% improvements can be observed in many cases.

Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems.

Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by the weight decomposition of objectives. This article proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems. The Pareto front is then built accordingly. Compared with other learning-based methods, our method can greatly shorten the training time of multiple submodels. Due to the rapid and excellent adaptability of the meta-model, more submodels can be derived so as to increase the quality and diversity of the found solutions. The computational experiments on multiobjective traveling salesman problems and multiobjective vehicle routing problems with time windows demonstrate the superiority of our method over most of the learning-based and iteration-based approaches.

TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction.

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.

Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification.

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

Fully Complex-Valued Dendritic Neuron Model.

A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal processing. Yet no complex value representations appear in single neuron architectures. In this article, we first extend DNM from a real-value domain to a complex-valued one. Performance of complex-valued DNM (CDNM) is evaluated through a complex XOR problem, a non-minimum phase equalization problem, and a real-world wind prediction task. Also, a comparative analysis on a set of elementary transcendental functions as an activation function is implemented and preparatory experiments are carried out for determining hyperparameters. The experimental results indicate that the proposed CDNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.