Enzymatic signal amplification of molecular beacons for sensitive DNA detection

Molecular beacons represent a new family of fluorescent probes for nucleic acids, and have found broad applications in recent years due to their unique advantages over traditional probes. Detection of nucleic acids using molecular beacons has been based on hybridization between target molecules and molecular beacons in a 1:1 stoichiometric ratio. The stoichiometric hybridization, however, puts an intrinsic limitation on detection sensitivity, because one target molecule converts only one beacon molecule to its fluorescent form. To increase the detection sensitivity, a conventional strategy has been target amplification through polymerase chain reaction. Instead of target amplification, here we introduce a scheme of signal amplification, nicking enzyme signal amplification, to increase the detection sensitivity of molecular beacons. The mechanism of the signal amplification lies in target-dependent cleavage of molecular beacons by a DNA nicking enzyme, through which one target DNA can open many beacon molecules, giving rise to amplification of fluorescent signal. Our results indicate that one target DNA leads to cleavage of hundreds of beacon molecules, increasing detection sensitivity by nearly three orders of magnitude. We designed two versions of signal amplification. The basic version, though simple, requires that nicking enzyme recognition sequence be present in the target DNA. The extended version allows detection of target of any sequence by incorporating rolling circle amplification. Moreover, the extended version provides one additional level of signal amplification, bringing the detection limit down to tens of femtomolar, nearly five orders of magnitude lower than that of conventional hybridization assay

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem

Diphenamid degradation via sulfite activation under visible LED using Fe (III) impregnated N-doped TiO2 photocatalyst

Degradation of diphenamid (DPA) was examined by a novel process through sulfite activation by Fe impregnated N-doped TiO2 (FeN-TiO2) under visible LED (Vis LED). The FeN-TiO2 was synthesized using an impregnation method and characterized by various techniques. The mechanism of sulfite activation by FeN-TiO2 under Vis LED was proposed. The reaction mechanisms were further elucidated by investigating the XPS spectra of the catalysts before and after the reaction. Thirty intermediates were identified and twenty-four of them are newly reported. A new pathway was reported for the first time in the DPA studies through the rupture of benzene ring linkage. A higher mineralization degree was achieved using the FeN-TiO2/sulfite/Vis LED process, which is not in accordance with previous reports on sulfite-based processes. The absence of sulfate adducts could provide a rational explanation of the higher mineralization degree during DPA degradation. Based on reusability test, the DPA degradation efficiency increased after successive usage of the catalyst. After the complete degradation of DPA, the leached Fe-ions were found to be negligible and sulfite was completely depleted. Considering several factors such as the cheap source of sulfite (an air pollutant waste from flue-gas desulfurization process), low cost of Fe, negligible leaching of Fe-ions, and high energy efficiency of Vis LED light, the FeN-TiO2/sulfite/Vis LED process could be a practical and green technology for the removal of wastewater contaminants

Influence of graphite on concentration of carbon vacancies in ball-milled TiCx

In this work, the influence of graphite on the ball-milled TiCx was studied. The results show that the lattice parameter of TiCx is increased when TiCx particles are ball-milled with graphite, which indicates a decrease in the concentration of carbon vacancies in the TiCx. It is considered that this decrease in the concentration of carbon vacancies results from the diffusion of carbon atoms from graphite into the TiCx. When the TiCx is ball-milled with more graphite, the effectiveness of the ball-milling is better, and the diffusion process of carbon becomes much easier. Furthermore, besides diffusion into the TiCx, some graphite has transformed into amorphous carbon after the ball-milling

Fully solution-processed phase-pure 3D/2D perovskite bilayer heterojunctions

Combining the superior photovoltaic performance of three-dimensional perovskites and the intrinsic durability of two-dimensional perovskites, the construction of 3D/2D perovskite bilayer heterojunctions is a promising strategy to realize efficient and stable perovskite solar cells, but it is still a challenge to control the phase purity, film thickness, orientation, and crystal structure of 2D perovskites. Now, a solution-processing strategy has overcome this challenge by directly coating a tailored single-crystal 2D perovskite ink on as-prepared 3D perovskite films, resulting in effective, ultra-stable and phase-pure 3D/2D perovskite bilayer heterojunctions

Research on Damage Localization of Steel Truss–Concrete Composite Beam Based on Digital Orthoimage

Most structural health monitoring is carried out for a limited number of key measurement points of a bridge, and incomplete measurement data lead to incomplete mechanical equation inversion results, which is a key problem faced in bridge damage identification. The ability of digital images to holographically describe structural morphology can effectively alleviate the problem of damage identification due to incomplete test data. Based on digital image processing technology, a matrix similarity damage identification method based on a structural digital orthoimage was proposed. Firstly, a steel truss–concrete composite beam specimen with a complex support bar system was designed and fabricated in the laboratory, and the digital orthoimage of the test beam was obtained by the perspective transformation of the original image of the test beam. The body contour of the structure was extracted from the digital orthoimage of the test beam, and wavelet threshold denoising was performed on the lower edge profile to obtain the deflection curves of the structure under different working conditions. The verification results show that the maximum error of the deflection curve is 3.42%, which proves that the digital orthoimage can accurately and completely reflect the deformation of the structure. Finally, based on the digital orthophoto of the test beam, a matrix similarity test before and after the damage was carried out, and the results show that the singularities of the similarity distribution are consistent with the location of the damage; furthermore, the accurate positioning of the damage in different working conditions is achieved

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases