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The prevalence of autism spectrum disorder (ASD) has risen rapidly in recent decades. Owing to its success across disciplines, the use of artificial intelligence (AI) in the screening of ASD has emerged as a prominent solution. We conducted a bibliometric analysis on AI-powered ASD screening research with a unit of 2090 publications retrieved from Scopus database in the period 2010–2021. Our findings show, among other things, that the annual growth rate of publications was 33.05% and scientific production drastically increased 23-fold from 22 in 2010 to 509 in 2021 with nearly two thirds (1307; 62,54%) of the retrieved documents being published between 2019–2021. The USA was the global leader in terms of scientific output with 730 publications followed by China (255), and India (251). Stanford university, the scientific journal NeuroImage, and Dennis P. Wall were the most globally prolific institution, publication source, and author, respectively. Using VOSviewer's clustering algorithms, keyword and topic analysis identified neuroimaging techniques and genetic research as hot and emerging research trends. Interestingly, three of the top ten prolific authors were women, indicating a significant milestone for gender rebalancing efforts in the AI workforce. The findings will help both experienced and aspiring scientists better understand the structure and current state of knowledge, uncover patterns of collaboration, and identify emerging trends in ASD research using AI. [ABSTRACT FROM AUTHOR]

DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. In this study, a de novo strand assembly algorithm (DBGPS) is developed using de Bruijn graph and greedy path search to meet these challenges. DBGPS shows substantial advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large-scale simulations. Remarkably, 6.8 MB of data is accurately recovered from a severely corrupted sample that has been treated at 70 °C for 70 days. With DBGPS, we are able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g. DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. Here the authors present a strand assembly algorithm (DBGPS) using de Bruijn graph and greedy path search. [ABSTRACT FROM AUTHOR]

Accurate identification of protein-nucleotide binding residues is crucial for the study of drug structure and protein functional annotation. The study of protein-nucleotide binding residues is a typical problem of sample imbalance. The minority class (binding residues) are far less than the majority class (non-binding residues). The traditional machine learning algorithm is not universal for this kind of research, the results will be seriously biased to majority class. To deal with the serious imbalance problem, we propose a new computational method to identify protein-nucleotide binding residues via Graph Regularized k-local Hyperplane Distance Nearest Neighbor (GHKNN). On the training set, we compare the performance of the basic classifier, the ensemble classifier and the single classifier. On the independent test sets, we compare the performance with other existing models. The experimental results prove that our proposed method has higher accuracy in the identification of protein-nucleotide binding residues and is more prominent than other existing models. The data and material are freely available at https://github.com/guofei-tju/GHKNN. [ABSTRACT FROM AUTHOR]

RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is time-consuming and expensive, so the development of an effective computational approach can provide a strategy to solve this issue. In recent years, most of the computational approaches are constructed on protein sequence information, but the protein structure has not been considered. In this paper, we use a novel computational model of RNA-binding residues prediction, using protein sequence and structure information. Our hybrid features are encoded by local sequence and structure feature extraction models. Our predictor is built by employing the Granular Multiple Kernel Support Vector Machine with Repetitive Under-sampling (GMKSVM-RU). In order to evaluate our method, we use fivefold cross-validation on the RBP129, our method achieves better experimental performance with MCC of 0.3367 and accuracy of 88.84%. In order to further evaluate our model, an independent data set (RBP60) is employed, and our method achieves MCC of 0.3921 and accuracy of 87.52%. Above results demonstrate that integrating sequence and structure information is beneficial to improve the prediction ability of RNA-binding residues. [ABSTRACT FROM AUTHOR]

With the emergence of large-scale experimental data on genes and proteins, drug discovery and repositioning will be more difficult in the field of biomedical research. More and more resources are needed for detecting drug–target interactions (DTIs) in the experimental works. The interactions between drugs and targets could been seen as a bipartite network. Many computational methods have been developed to identify DTIs. However, most of them did not integrate multiple information and filter noise or outlier points. In this paper, we develop a fuzzy bipartite local model (FBLM) based on fuzzy least squares support vector machine and multiple kernel learning (MKL) for predicting DTIs. First, multiple kernels are constructed in drug and target spaces, respectively. Then, all corresponding kernels are combined by MKL algorithm in two spaces. Finally, FBLM is employed to identify DTIs. Our proposed approach is tested on four benchmark datasets under three types of cross validation. Comparing with existing outstanding methods, our method is a useful tool for the DTIs prediction. [ABSTRACT FROM AUTHOR]

The low surface activity of graphene oxide (GO) stemming from its large conjugated electronic structure can easily affect the dispersion behavior of GO-based polymer matrices. This significantly undermines the properties of the resulting composite materials. Therefore, in order to increase the GO surface activity for use in polymer-based composites, GO was modified using silane coupling agent which was then doped into polydimethylsiloxane (PDMS) polymer to prepare novel paints by sol-gel reaction strategy. The subsequent novel composite coatings based on PDMS/modified GO (mGO) were finally cured with tetraethoxysilane as the hardening agent in the presence of dibutyltin dilaurate catalyst. The effect of doping mGO into PDMS polymer was systematically studied using infrared spectroscopy, micro-Raman spectroscopy, TEM, SEM, XRD, TGA, mechanical test, thermal conductivity test, and the erosion resistance test. It was concluded that the phase compatibility between GO and PDMS was enhanced due to the new interconnecting chemical bonds brought about by the mGO in the composite. [ABSTRACT FROM AUTHOR]

In this paper, a series of acrylate-polydimethylsiloxane (PDMS) composite latexes was prepared and studied systematically to find the factors that affect their performances. At first, the modified PDMS was synthesized to react with acrylate monomers and participate in free radical polymerization. Then, the modified PDMS was blended with acrylate monomers, and the acrylate-PDMS composite latexes with different formulas were obtained by emulsion polymerization. Because the blending monomers were constrained in the micelle, the two components were interconnected with each other by a covalent bond and the phase compatibility between the two components could be controlled well. Chemical constitution and the morphology of acrylate-PDMS composite latexes were confirmed by using FTIR spectroscopy, TEM, and SEM measurements, respectively. Thermophysics and heat resistance of the dried coatings based on acrylate-PDMS composite latexes were studied using DSC and TGA tests, respectively. Anticorrosion properties of the cured coatings based on acrylate-PDMS composite latexes were confirmed by potentiodynamic polarization test. With low surface tension, good toughness, excellent weather-proof properties, and good high and low temperature stability, the modified PDMS component can improve the performance of the traditional acrylate latex-based waterborne resin effectively, and the prepared acrylate-PDMS composite latexes can be used in heavy-duty anticorrosion applications. [ABSTRACT FROM AUTHOR]

The edit distance under the DCJ model can be computed in linear time for genomes with equal content or with Indels. But it becomes NP-Hard in the presence of duplications, a problem largely unsolved especially when Indels (i.e., insertions and deletions) are considered. In this paper, we compare two mainstream methods to deal with duplications and associate them with Indels: one by deletion, namely DCJ-Indel-Exemplar distance; versus the other by gene matching, namely DCJ-Indel-Matching distance. We design branch-and-bound algorithms with set of optimization methods to compute exact distances for both. Furthermore, median problems are discussed in alignment with both of these distance methods, which are to find a median genome that minimizes distances between itself and three given genomes. Lin-Kernighan heuristic is leveraged and powered up by sub-graph decomposition and search space reduction technologies to handle median computation. A wide range of experiments are conducted on synthetic data sets and real data sets to exhibit pros and cons of these two distance metrics per se, as well as putting them in the median computation scenario. [ABSTRACT FROM AUTHOR]

In this article, a series of hybrid coatings were prepared and characterized. At first, polydimethylsiloxane modified with siliane coupling agent was copolymerized with acrylate monomers by free radical polymerization to get the hybrid resins. Due to the combination of covalent bond, the prepared hybrid resins were uniform and transparent. Then a series of hybrid resins were cured with tetraethoxysilane and dibutyltin dilaurate as the crosslinker and catalyst, respectively. The blends were loaded onto the substrate, such as tin plate, steel panels, or flat glass, and cured by heating to form hybrid coatings. To study the factors on the properties of the prepared hybrid resins and cured coatings, various measurements were carried out, such as FTIR, TGA, DSC, laser optical microscope, water contact angles, pencil scratch hardness, and the electrochemistry test. Thanks to the combination of covalent bond, the two components dispersed uniformly in the prepared hybrid resins and no cracks were observed from the cured hybrid coatings with an optical microscope. With excellent mechanical properties, thermal stability, corrosion resistance, and hydrophobic property, the prepared hybrid coatings can find application in the heavy-duty chemical industry. [ABSTRACT FROM AUTHOR]

Ricin is a toxic protein derived from castor beans and composed of a cytotoxic A chain and a galactose-binding B chain linked by a disulfide bond, which can inhibit protein synthesis and cause cell death. Owing to its high toxicity, ease of preparation, and lack of medical countermeasures, ricin has been listed as both chemical and biological warfare agents. For homeland security or public safety, the unambiguous, sensitive, and rapid methods for identification and quantification of ricin in complicated matrices are of urgent need. Mass spectrometric analysis, which provides specific and sensitive characterization of protein, can be applied to confirm and quantify ricin. Here, we report a liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) method in which ricin was extracted and enriched from serum by immunocapture using anti-ricin monoclonal antibody 3D74 linked to magnetic beads, then digested by trypsin, and analyzed by LC-ESI-MS/MS. Among 19 distinct peptides observed in LC-quadrupole/time of flight-MS (LC-QTOF-MS), two specific and sensitive peptides, T (VGLPINQR) and T (DNCLTSDSNIR), were chosen, and a highly sensitive determination of ricin was established in LC-triple quadrupole-MS (LC-QqQ-MS) operating in multiple reaction monitoring mode. These specific peptides can definitely distinguish ricin from the homologous protein Ricinus communis agglutinin (RCA120), even though the amino acid sequence homology of the A-chain of ricin and RCA120 is up to ca. 93 % and that of B-chain is ca. 85 %. Furthermore, peptide T was preferred in the quantification of ricin because its sensitivity was at least one order of magnitude higher than that of the peptide T. Combined with immunocapture enrichment, this method provided a limit of detection of ca. 2.5 ng/mL and the limit of quantification was ca. 5 ng/mL of ricin in serum, respectively. Both precision and accuracy of this method were determined and the RSD was less than 15 %. This established method was then applied to measure ricin in serum samples collected from rats exposed to ricin at the dosage of 50 μg/kg in an intravenous injection manner. The results showed that ca. 10 ng/mL of the residual ricin in poisoned rats serum could be detected even at 12 h after exposure. [ABSTRACT FROM AUTHOR]

Aptamers are a series of high-affinity and high-specificity oligoneucleotides (single-stranded DNA or RNA) to the target, usually selected by the combinatorial chemistry SELEX technique (systematic evolution of ligands by exponential enrichment). Aptamers have proved to be one kind of novel functional molecules in life science and chemistry. After being labeled by signaling groups, the aptamer probe can conveniently transfer the characteristics of aptamer-target recognition to a form of high-sensitive signal, and the high-affinity, high-specificity measurements of metal ion, organic molecules, nucleic acid, proteins, or cells become possible. This article summarizes the recent advances of aptamer probes in different sensing fields, with special emphasis on aptamer probes as fluorescent sensors. [ABSTRACT FROM AUTHOR]

Aptamers which specifically recognize targets are selected from random oligonucleotide library using systematic evolution of ligands by exponential enrichment (SELEX). In this paper, capillary electrophoresis (CE) as a separation approach has been introduced to SELEX procedure. The high efficiency of CE gives rise to greatly shorten the selection procedure. The results from enzyme-linked assay and dot blot experiment show that an enrichment pool has been obtained after four rounds selection, which can specifically recognize ricin. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8. [ABSTRACT FROM AUTHOR]

Retinal diseases can be found timely by observing retinal fundus images. So extracting blood vessels from retinal images is an important part because it is the way to show the changes of vessels. However, most of the previous methods based on deep learning cared more about accuracy and ignored the complexity of the model for segmenting retinal vessels, which makes these methods difficult to apply to medical equipment. Besides, due to the great differences in the width of retinal vessels, some methods cannot well-extract all blood vessels at the same time. Based on above limitations, we propose a new lightweight network, called Res2Unet. It applies a multi-scale strategy to extract blood vessels of different widths and integrates the strategy into the channels to greatly reduce parameters and computation resources. Res2Unet also uses channel-attention mechanism to promote the communication between channels and recalibrate the relationship of channel features. Then, we propose two post-processing methods. One called the local threshold method(LTM) uses a lower local threshold to excavate hidden blood vessels in discontinuous blood vessels of the probability maps. The other named weighted correction method (WCM) combines the probability maps of Unet and Res2Unet to remove false positive and false negative samples. On the DRIVE dataset, the Dice, IOU and AUC of our Res2Unet reach 0.8186, 0.6926 and 0.9772, respectively, which are better than that of Unet with 0.8109, 0.6817 and 0.9751. Importantly, the number of parameters of Res2Unet are about one-third of Unet. It means that Res2Unet has less hardware requirements. [ABSTRACT FROM AUTHOR]