MicroRNAs play a critical role in regulating gene expression by binding to specific target sites within gene transcripts, making the identification of microRNA targets a prominent focus of research. ...Conventional experimental methods for identifying microRNA targets are both time-consuming and expensive, prompting the development of computational tools for target prediction. However, the existing computational tools exhibit limited performance in meeting the demands of practical applications, highlighting the need to improve the performance of microRNA target prediction models.
In this paper, we utilize the most popular natural language processing and computer vision technologies to propose a novel approach, called TEC-miTarget, for microRNA target prediction based on transformer encoder and convolutional neural networks. TEC-miTarget treats RNA sequences as a natural language and encodes them using a transformer encoder, a widely used encoder in natural language processing. It then combines the representations of a pair of microRNA and its candidate target site sequences into a contact map, which is a three-dimensional array similar to a multi-channel image. Therefore, the contact map's features are extracted using a four-layer convolutional neural network, enabling the prediction of interactions between microRNA and its candidate target sites. We applied a series of comparative experiments to demonstrate that TEC-miTarget significantly improves microRNA target prediction, compared with existing state-of-the-art models. Our approach is the first approach to perform comparisons with other approaches at both sequence and transcript levels. Furthermore, it is the first approach compared with both deep learning-based and seed-match-based methods. We first compared TEC-miTarget's performance with approaches at the sequence level, and our approach delivers substantial improvements in performance using the same datasets and evaluation metrics. Moreover, we utilized TEC-miTarget to predict microRNA targets in long mRNA sequences, which involves two steps: selecting candidate target site sequences and applying sequence-level predictions. We finally showed that TEC-miTarget outperforms other approaches at the transcript level, including the popular seed match methods widely used in previous years.
We propose a novel approach for predicting microRNA targets at both sequence and transcript levels, and demonstrate that our approach outperforms other methods based on deep learning or seed match. We also provide our approach as an easy-to-use software, TEC-miTarget, at https://github.com/tingpeng17/TEC-miTarget . Our results provide new perspectives for microRNA target prediction.
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Traditional digital holographic imaging algorithms need multiple iterations to obtain focused reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the problem of ...phase compensation in addition to focusing task. Here, a new method is proposed for fast digital focus, where we use U-type convolutional neural network (U-net) to recover the original phase of microscopic samples. Generated data sets are used to simulate different degrees of defocused image, and verify that the U-net can restore the original phase to a great extent and realize phase compensation at the same time. We apply this method in the construction of real-time off-axis digital holographic microscope and obtain great breakthroughs in imaging speed.
Understanding spatial distribution difference and reaction kinetics of the electrode is vital for enhancing the electrochemical reaction efficiency. Here, we report a total internal reflection ...imaging sensor without background current interference to map local current distribution of the electrode in a vanadium redox flow battery during cyclic voltammetry (CV), enabling mapping of the activity and reversibility distribution with the spatial resolution of a single fiber. Three graphite felts with different activity are compared to verify its feasibility. In long-term cyclic voltammetry, the oxygen evolution reaction is proved to enhance activity distribution, and homogeneity of the electrode and its bubble kinetics with periodic fluctuation is consistent with the cyclic voltammetry curve, enabling the onset oxygen evolution/reduction potential determination. Higher activity and irreversibility distribution of the electrode is found in favor of the oxygen evolution reaction. This sensor has potential to detect in situ, among other processes, electrochemical reactions in flow batteries, water splitting, electrocatalysis and electrochemical corrosion.
A simple and distinctive method for the ultrasensitive detection of Cu2+ and Hg2+ based on surface-enhanced Raman scattering (SERS) using cysteine-functionalized silver nanoparticles (AgNPs) attached ...with Raman-labeling molecules was developed. The glycine residue in a silver nanoparticle-bound cysteine can selectively bind with Cu2+ and Hg2+ and form a stable inner complex. Silver nanoparticles co-functionalized with cysteine and 3,5-Dimethoxy-4-(6′-azobenzotriazolyl)phenol (AgNP conjugates) can be used to detect Cu2+ and Hg2+ based on aggregation-induced SERS of the Raman tags. The addition of SCN− to the analyte can successfully mask Hg2+ and allow for the selective detection of Cu2+. This SERS-based assay showed an unprecedented limit of detection (LOD) of 10pM for Cu2+ and 1pM for Hg2+; these LODs are a few orders of magnitude more sensitive than the typical colorimetric approach based on the aggregation of noble nanoparticles. The analysis of real water samples diluted with pure water was performed and verified this conclusion. We envisage that this SERS-based assay may provide a general and simple approach for the detection of other metal ions of interest, which can be adopted from their corresponding colorimetric assays that have already been developed with significantly improved sensitivity and thus have wide-range applications in many areas.
► A simple method for ultrasensitive detection of Cu2+ and Hg2+ based on SERS is developed. ► Extreme low detection limit of 10pM and 1pM is achieved for Cu2+ and Hg2+, respectively. ► Use of SCN− can successfully mask Hg2+ and allows the exclusive detection of Cu2+ ion.
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Mueller matrices can be used as a powerful tool to probe qualitatively the microstructures of biological tissues. Certain transformation processes can provide new sets of parameters which are ...functions of the Mueller matrix elements but represent more explicitly the characteristic features of the sample. In this paper, we take the backscattering Mueller matrices of a group of tissues with distinctive structural properties. Using both experiments and Monte Carlo simulations, we demonstrate qualitatively the characteristic features of Mueller matrices corresponding to different structural and optical properties. We also calculate two sets of transformed polarization parameters using the Mueller matrix transformation (MMT) and Mueller matrix polar decomposition (MMPD) techniques. We demonstrate that the new parameters can separate the effects due to sample orientation and present quantitatively certain characteristic features of these tissues. Finally, we apply the transformed polarization parameters to the unstained human cervix cancerous tissues. Preliminary results show that the transformed polarization parameters can provide characteristic information to distinguish the cancerous and healthy tissues.
Polyamines (PAs) are small aliphatic nitrogenous bases with strong biological activity that participate in plant stress response signaling and the alleviation of damage from stress. Herein, the ...effects of the PA-producing bacterium Bacillus megaterium N3 and PAs on the immobilization of Cd and inhibition of Cd absorption by spinach and the underlying mechanisms were studied. A solution test showed that strain N3 secreted spermine and spermidine in the presence of Cd. Both strain N3 and the PAs (spermine+spermidine) immobilized Cd and increased the pH of the solution. Untargeted metabolomics results showed that strain N3 secreted PAs, N1-acetylspermidine, 3-indolepropionic acid, indole-3-acetaldehyde, cysteinyl-gamma-glutamate, and choline, which correlated with plant growth promotion and Cd immobilization. A pot experiment showed that rhizosphere soil inoculation with strain N3 and PAs improved spinach dry weight and reduced spinach Cd absorption compared with the control. These positive effects were likely due to the increase in rhizosphere soil pH and NH4+-N and PA contents, which can be attributed primarily to Cd immobilization. Moreover, inoculation with strain N3 more effectively inhibited the absorption of Cd by spinach than spraying PAs, mainly because strain N3 enabled a better relative abundance of bacteria (Microvirga, Pedobacter, Bacillus, Brevundimonas, Pseudomonas, Serratia, Devosid, and Aminobacter), that have been reported to have the ability to resist heavy metals and produce PAs. Strain N3 regulated the structure of rhizosphere functional bacterial communities and inhibited Cd uptake by spinach. These results provide a theoretical basis for the prevention of heavy metal absorption by vegetables using PA-producing bacteria.
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•Strain N3 secreted PAs and chelated Cd in solution.•Polyamines and strain N3 immobilized Cd and increased the pH of the solution.•Amino acid metabolism was enrichment in the presence of strain N3 under Cd stress.•Strain N3 and polyamines reduced the Cd content in spinach roots and leaves.•Strain N3 shifted the bacterial community composition of spinach rhizosphere soil.
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Apple leaf diseases typified by small disease spots are generally difficult to detect in images. This study proposes a deep learning model called the feature pyramid networks (FPNs) -inception ...squeeze-and-excitation ResNet (ISResNet)-Faster RCNN (region with convolutional neural network) model to improve the accuracy of detecting apple leaf diseases. Apple leaf diseases were identified, evaluated, and validated by using the FPN-ISResNet-Faster RCNN. The results were compared with those obtained by the single-shot multibox detector (SSD), Faster RCNN, and ISResNet-Faster RCNN. The detection accuracies obtained by using different feature extraction networks, positions and numbers of SE, inception modules, scales of FPN structures, and scales of anchor frames were also compared. The results showed that the values of average precision (AP), and APs with the thresholds of the intersection over union of 0.5 and 0.75 (AP50 and AP75), obtained from the FPN-ISResNet-Faster RCNN were 62.71%, 93.68%, and 70.94%, respectively, which are higher than those of the SSD, VGG-Faster RCNN, GoogleNet-Faster RCNN, ResNet50-Faster RCNN, ResNeXt-Faster RCNN, and ISResNet-Faster RCNN. FPN-ISResNet-Faster RCNN was shown to be able to detect diseases in apple leaves with high accuracy and generalizability.
BackgroundThyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. ...As digital whole slide images (WSIs) develop, deep learning methods for histopathological classification of the thyroid gland (paraffin sections) have achieved outstanding results. Our current study is to clarify whether deep learning assists pathology diagnosis for intraoperative frozen thyroid lesions or not.MethodsWe propose an artificial intelligence-assisted diagnostic system for frozen thyroid lesions that applies prior knowledge in tandem with a dichotomous judgment of whether the lesion is cancerous or not and a quadratic judgment of the type of cancerous lesion to categorize the frozen thyroid lesions into five categories: papillary thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, follicular thyroid tumor, and non-cancerous lesion. We obtained 4409 frozen digital pathology sections (WSI) of thyroid from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) to train and test the model, and the performance was validated by a six-fold cross validation, 101 papillary microcarcinoma sections of thyroid were used to validate the system’s sensitivity, and 1388 WSIs of thyroid were used for the evaluation of the external dataset. The deep learning models were compared in terms of several metrics such as accuracy, F1 score, recall, precision and AUC (Area Under Curve).ResultsWe developed the first deep learning-based frozen thyroid diagnostic classifier for histopathological WSI classification of papillary carcinoma, medullary carcinoma, follicular tumor, anaplastic carcinoma, and non-carcinoma lesion. On test slides, the system had an accuracy of 0.9459, a precision of 0.9475, and an AUC of 0.9955. In the papillary carcinoma test slides, the system was able to accurately predict even lesions as small as 2 mm in diameter. Tested with the acceleration component, the cut processing can be performed in 346.12 s and the visual inference prediction results can be obtained in 98.61 s, thus meeting the time requirements for intraoperative diagnosis. Our study employs a deep learning approach for high-precision classification of intraoperative frozen thyroid lesion distribution in the clinical setting, which has potential clinical implications for assisting pathologists and precision surgery of thyroid lesions.
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Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires ...significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classification and image classification, respectively. This two-branch structure enables the model to learn more general features and thus reduce the risk of overfitting when learning sparsely annotated data. We incorporate the idea of consistency learning and self-training into the semi-supervised training strategy to take advantage of the unlabeled images. Trained on the BCSS dataset with only 25% of the images labeled (five patches for each labeled image), our proposed method achieved competitive performance compared to the fully supervised pixel-wise segmentation models. Experiments demonstrate that the proposed solution has the potential to reduce the burden of labeling histopathological images.
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Currently, the world is facing the problem of bacterial resistance, which threatens public health, and bacterial antimicrobial susceptibility testing (AST) plays an important role in biomedicine, ...dietary safety and aquaculture. Traditional AST methods take a long time, usually 16-24 h, and cannot meet the demand for rapid diagnosis in the clinic, so rapid AST methods are needed to shorten the detection time. In this study, by using an in-house built centrifuge to centrifuge bacteria in a liquid medium onto the inner wall of the bottom surface of a counting plate, and using a phase contrast microscope to track bacterial growth under the effect of different antibiotic concentrations, the results of the minimum inhibitory concentration (MIC) of bacteria under the effect of antibiotics can be obtained in as early as 4 h. We used a combination of
and tigecycline and obtained MIC results that were consistent with those obtained using the gold standard broth micro-dilution method, demonstrating the validity of our method; due to the time advantage, the complete set can be used in the future for point of care and clinical applications, helping physicians to quickly obtain the MIC used to inhibit bacterial growth.
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