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Herein, we fabricated a label-free ECL immunosensor for aflatoxin B1 (AFB1) detection. In this system, a small organic aggregation-induced electrochemiluminescence luminophore, 2,5-di-tetraphenylethylene-ylthiazolo [5,4-d] thiazole, was designed, named TPETTZ. Polyaniline-wrapped TiO2 nanoparticles (PANI/TiO2 NPs) complex was synthesized through one-step in situ oxidation polymerization of aniline, and performed excellent electrical conductivity and abundant amino groups. As an ECL accelerator, TiO2 nanoparticles (TiO2 NPs) promoted the oxidation of tri-n-propylamine (TPA) to generate more TPA•; in addition, it also acted as a donor to improve the ECL intensity of TPETTZ (acceptor) through electrochemiluminescence resonance energy transfer (ECL-RET). Encouraged by the above, under the existence of TPA, TPETTZ displayed a strong and continuously stable ECLanode signal due to the introduction of PANI/TiO2 NPs. Therefore, the immunosensor was constructed for AFB1 detection based on the quenching effect of target on the ECL signal, and a linearly decreasing ECL signal was obtained as the increasement of AFB1 in the range of 75 fg/mL to 100 ng/mL, with a lower detection limit of 27.5 fg/mL. Moreover, the as-prepared sensing platform performed a satisfactory anti-interference, stability, and reproducibility, and appeared a good accuracy in walnut sample analysis, presenting a promising application in the future. [ABSTRACT FROM AUTHOR]

In this contribution, a uniformly distributed CuNi active nanoalloys was obtained on the electrode substrate by one-step facile electrodeposition method, and a simple non-enzymatic electrochemical glucose sensor was designed based on the CuNi NAs. The CuNi NAs with high specific surface area was investigated through a series of relevant characterizations, and the nanobimetal substrate electrode performed a satisfactory electrocatalytic activity toward glucose in alkaline solution compared with the pure Cu or Ni nanoparticles modified electrode. Chronoamperometry test showed an acceptable linearity in the range of 0.05–35 μM with the detection limit as low as 0.02 μM. In addition, this sensor demonstrated high sensitivity, stability and selectivity, and showed promising performance in the application of glucose measurement in human blood serum samples. Therefore, the constructed non-enzymatic glucose sensor based on CuNi nanoalloys exhibits a good prospect in food industry in the future. [ABSTRACT FROM AUTHOR]

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor–acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen–antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π − π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21–1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection. [ABSTRACT FROM AUTHOR]

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor–acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen–antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π − π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21–1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection. [ABSTRACT FROM AUTHOR]

In this article, a catalyst-free method for the preparation of orientation-ordered single-crystal Cd 1 − x Zn x S nanorod arrays on graphite substrates has been reported. The morphology and crystal structure of the Cd 1 − x Zn x S nanorod arrays were studied using scanning electron microscopy and transmission electron microscopy. The results showed that the nanorods with 40–100 nm in diameter and 2000 nm in length grew along the [0 0 1] direction of the hexagonal crystalline phase. A vapor–solid (VS) growth mechanism was proposed for the formation of the Cd 1 − x Zn x S nanorod arrays. The photoluminescence characterizations showed a strong blue emission at 420 nm for the Cd 1 − x Zn x S nanorod arrays when the value of x is 0.5. Thus, with a simple CVD technique, the high-density and orientation-ordered nanorod arrays would become a promising candidate in many applications such as building blocks for optoelectronics. [ABSTRACT FROM AUTHOR]

Abstract: A fabrication of a novel simple porous silicon polybasic photonic crystal with symmetrical structure has been reported as a nucleic acid biosensor for detecting antifreeze protein gene in insects (Microdera puntipennis dzhungarica), which would be helpful in the development of some new transgenic plants with tolerance of freezing stress. Compared to various porous silicon-based photonic configurations, porous silicon polytype layered structure is quite easy to prepare and shows more stability; moreover, polybasic photonic crystals with symmetrical structure exhibit interesting optical properties with a sharp resonance in the reflectance spectrum, giving a higher Q factor which causes higher sensitivity for sensing performance. In this experiment, DNA oligonucleotides were immobilized into the porous silicon pores using a standard crosslink chemistry method. The porous silicon polybasic symmetrical structure sensor possesses high specificity in performing controlled experiments with non-complementary DNA. The detection limit was found to be 21.3nM for DNA oligonucleotides. The fabricated multilayered porous silicon-based DNA biosensor has potential commercial applications in clinical chemistry for determination of an antifreeze protein gene or other genes. [Copyright &y& Elsevier]

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease. [ABSTRACT FROM AUTHOR]

Skin cancer is one of the most common malignant tumors worldwide, and early detection is crucial for improving its cure rate. In the field of medical imaging, accurate segmentation of lesion areas within skin images is essential for precise diagnosis and effective treatment. Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. However, challenges such as boundary ambiguity between normal skin and lesion areas, significant variations in the size and shape of lesion areas, and different types of lesions in different samples pose significant obstacles to skin lesion segmentation. Therefore, this study introduces a novel network model called HDS-Net (Hybrid Dynamic Sparse Network), aiming to address the challenges of boundary ambiguity and variations in lesion areas in skin image segmentation. Specifically, the proposed hybrid encoder can effectively extract local feature information and integrate it with global features. Additionally, a dynamic sparse attention mechanism is introduced, mitigating the impact of irrelevant redundancies on segmentation performance by precisely controlling the sparsity ratio. Experimental results on multiple public datasets demonstrate a significant improvement in Dice coefficients, reaching 0.914, 0.857, and 0.898, respectively. [ABSTRACT FROM AUTHOR]

Efficient and rapid auxiliary diagnosis of different grades of lung adenocarcinoma is conducive to helping doctors accelerate individualized diagnosis and treatment processes, thus improving patient prognosis. Currently, there is often a problem of large intra-class differences and small inter-class differences between pathological images of lung adenocarcinoma tissues under different grades. If attention mechanisms such as Coordinate Attention (CA) are directly used for lung adenocarcinoma grading tasks, it is prone to excessive compression of feature information and overlooking the issue of information dependency within the same dimension. Therefore, we propose a Dimension Information Embedding Attention Network (DIEANet) for the task of lung adenocarcinoma grading. Specifically, we combine different pooling methods to automatically select local regions of key growth patterns such as lung adenocarcinoma cells, enhancing the model's focus on local information. Additionally, we employ an interactive fusion approach to concentrate feature information within the same dimension and across dimensions, thereby improving model performance. Extensive experiments have shown that under the condition of maintaining equal computational expenses, the accuracy of DIEANet with ResNet34 as the backbone reaches 88.19%, with an AUC of 96.61%, MCC of 81.71%, and Kappa of 81.16%. Compared to seven other attention mechanisms, it achieves state-of-the-art objective metrics. Additionally, it aligns more closely with the visual attention of pathology experts under subjective visual assessment. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients. [ABSTRACT FROM AUTHOR]

Considering the poor effect of short text classification due to insufficient semantic information mining in the current short text matching methods, a new short text classification method is proposed based on explicit and implicit multiscale weighting semantic information interaction. First, the explicit and implicit representations of short text are obtained by a word vector model (word2vec), convolutional neural networks (CNNs), and long short-term memory (LSTM). Then, a multiscale convolutional neural network obtains the explicit and implicit multiscale weighting semantics information of short text. Finally, the multiscale weighting semantics is fused for more accurate short text classification. The experimental results show that this method is superior to the existing classical short text classification algorithms and two advanced short text classification models on the five short text classification datasets of MR, Subj, TREC, SST1 and SST2 with accuracies of 85.7%, 96.9%, 98.1%, 53.4% and 91.8%, respectively. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients. [ABSTRACT FROM AUTHOR]

Food safety problems are becoming increasingly severe in modern society, and establishing an accurate food safety risk warning and analysis model is of positive significance in avoiding food safety accidents. We propose an algorithmic framework that integrates the analytic hierarchy process based on the entropy weight (AHP-EW) and the autoencoder-recurrent neural network (AE-RNN). Specifically, the AHP-EW method is first used to obtain the weight percentages of each detection index. The comprehensive risk value of the product samples is obtained by weighted summation with the detection data, which is used as the expected output of the AE-RNN network. The AE-RNN network is constructed to predict the comprehensive risk value of unknown products. The detailed risk analysis and control measures are taken based on the risk value. We applied this method to the detection data of a dairy product brand in China for example validation. Compared with the performance of 3 models of the back propagation algorithm (BP), the long short-term memory network (LSTM), and the LSTM based on the attention mechanism (LSTM-Attention), the AE-RNN model has a shorter convergence time, predicts data more accurately. The root mean square error (RMSE) of experimental data is only 0.0018, proving that the model is feasible in practice and helps improve the food safety supervision system in China to avoid food safety incidents. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value. [ABSTRACT FROM AUTHOR]

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy. [ABSTRACT FROM AUTHOR]

Maojian is one of China's traditional famous teas. There are many Maojian-producing areas in China. Because of different producing areas and production processes, different Maojian have different market prices. Many merchants will mix Maojian in different regions for profit, seriously disrupting the healthy tea market. Due to the similar appearance of Maojian produced in different regions, it is impossible to make a quick and objective distinction. It often requires experienced experts to identify them through multiple steps. Therefore, it is of great significance to develop a rapid and accurate method to identify different regions of Maojian to promote the standardization of the Maojian market and the development of detection technology. In this study, we propose a new method based on Near infra-red (NIR) with deep learning algorithms to distinguish different origins of Maojian. In this experiment, the NIR spectral data of Maojian from different origins are combined with the back propagation neural network (BPNN), improved AlexNet, and improved RepSet models for classification. Among them, improved RepSet has the highest accuracy of 99.30%, which is 8.67% and 0.70% higher than BPNN and improved AlexNet, respectively. The overall results show that it is feasible to use NIR and deep learning methods to quickly and accurately identify Maojian from different origins and prove an effective alternative method to discriminate different origins of Maojian. [ABSTRACT FROM AUTHOR]

Simple Summary: Guanine nucleotide-binding protein-like 3-like (GNL3L) is a novel GTP-binding nucleolar protein. In this study, we analyzed the expression, prognosis, and immune roles of GNL3L in pan-cancer from multiple omics analyses. The final results showed that GNL3L is differentially expressed in a variety of cancers, plays a prognostic role, and has good immune value. Moreover, GNL3L may affect the occurrence of cancer through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, we established an esophageal cancer (ESCA) prediction model with strong predictive ability and proved that GNL3L can significantly affect the proliferation ability of esophageal cancer cells through clone formation assays. In conclusion, GNL3L is an important biomarker. Guanine nucleotide-binding protein-like 3-like protein (GNL3L) is a novel, evolutionarily conserved, GTP-binding nucleolar protein. This study aimed to investigate the expression, prognosis, and immune value of GNL3L in pan-cancer from multiple omics analyses. Firstly, the expression and prognostic value of GNL3L in pan-cancer were discussed using the TIMER2 database, the GEPIA database, the cBioportal database, COX regression analysis, and enrichment analysis. The association of GNL3L with tumor mutational burden (TMB), tumor microsatellite instability (MSI), mismatch repair (MMR) genes, and immune cells was then analyzed. Finally, an esophageal cancer (ESCA) prediction model was established, and GNL3L clone formation assays were performed. The final results showed that GNL3L is differentially expressed in the vast majority of cancers, is associated with the prognosis of various cancers, and may affect cancer occurrence through processes such as ribonucleoprotein, ribosomal RNA processing, and cell proliferation. At the same time, it was found that the correlation between GNL3L and TMB, MSI, MMR, and various immune cells is significant. The established ESCA prediction model had a strong predictive ability, and GNL3L could significantly affect the proliferation of esophageal cancer cells. In conclusion, GNL3L may serve as an important prognostic biomarker and play an immunomodulatory role in tumors. [ABSTRACT FROM AUTHOR]

To improve the detection sensitivity of a porous silicon optical biosensor in the real-time detection of biomolecules, a non-spectral porous silicon optical biosensor technology, based on dual-signal light detection, is proposed. Double-light detection is a combination of refractive index change detection and fluorescence change detection. It uses quantum dots to label probe molecules to detect target molecules. In the double-signal-light detection method, the first detection-signal light is the detection light that is reflected from the surface of the porous silicon Bragg mirror. The wavelength of the detection light is the same as the wavelength of the photonic band gap edge of the porous silicon Bragg mirror. CdSe/ZnS quantum dots are used to label the probe DNA and hybridize it with the target DNA molecules in the pores of porous silicon to improve its effective refractive index and enhance the detection-reflection light. The second detection-signal light is fluorescence, which is generated by the quantum dots in the reactant that are excited by light of a certain wavelength. The Bragg mirror structure further enhances the fluorescence signal. A digital microscope is used to simultaneously receive the digital image of two kinds of signal light superimposed on the surface of porous silicon, and the corresponding algorithm is used to calculate the change in the average grey value before and after the hybridization reaction to calculate the concentration of the DNA molecules. The detection limit of the DNA molecules was 0.42 pM. This method can not only detect target DNA by hybridization, but also detect antigen by immune reaction or parallel biochip detection for a porous silicon biosensor. [ABSTRACT FROM AUTHOR]

Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non_Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer. [ABSTRACT FROM AUTHOR]

Fennel contains many antioxidant and antibacterial substances, and it has very important applications in food flavoring and other fields. The kinds and contents of chemical substances in fennel vary from region to region, which can affect the taste and efficacy of the fennel and its derivatives. Therefore, it is of great significance to accurately classify the origin of the fennel. Recently, origin detection methods based on deep networks have shown promising results. However, the existing methods spend a relatively large time cost, a drawback that is fatal for large amounts of data in practical application scenarios. To overcome this limitation, we explore an origin detection method that guarantees faster detection with classification accuracy. This research is the first to use the machine learning algorithm combined with the Fourier transform-near infrared (FT-NIR) spectroscopy to realize the classification and identification of the origin of the fennel. In this experiment, we used Rubberband baseline correction on the FT-NIR spectral data of fennel (Yumen, Gansu and Turpan, Xinjiang), using principal component analysis (PCA) for data dimensionality reduction, and selecting extreme learning machine (ELM), Convolutional Neural Network (CNN), recurrent neural network (RNN), Transformer, generative adversarial networks (GAN) and back propagation neural network (BPNN) classification model of the company realizes the classification of the sample origin. The experimental results show that the classification accuracy of ELM, RNN, Transformer, GAN and BPNN models are above 96%, and the ELM model using the hardlim as the activation function has the best classification effect, with an average accuracy of 100% and a fast classification speed. The average time of 30 experiments is 0.05 s. This research shows the potential of the machine learning algorithm combined with the FT-NIR spectra in the field of food production area classification, and provides an effective means for realizing rapid detection of the food production area, so as to merchants from selling shoddy products as good ones and seeking illegal profits. [ABSTRACT FROM AUTHOR]

Purpose: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious.Methods: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way.Results: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images.Conclusions: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images. [ABSTRACT FROM AUTHOR]

With the development of commodity economy, the emergence of fake and shoddy raisin has seriously harmed the interests of consumers and enterprises. To deal with this problem, a classification method combining near-infrared spectroscopy and pattern recognition algorithms were proposed for adulterated raisins. In this study, the experiment was performed by three kinds of raisins in Xinjiang (Hongxiangfei, Manaiti, Munage). After collecting and normalizing the spectral data, we compared the spectra of three kinds of raisins. Next the principal component analysis (PCA) was preformed to compress the dimension of the spectral data, and then classification models including support vector machine (SVM), multiscale fusion convolutional neural network (MCNN) and improved AlexNet were established to identify raisins. The accuracy of SVM, MCNN, and improved AlexNet is 100%, 92.83%, and 97.78% respectively. This study proves that near-infrared spectroscopy combined with pattern recognition is feasible for the raisin inspection. [ABSTRACT FROM AUTHOR]

Zhejiang Suichang native honey, which is included in the list of China's National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey. [ABSTRACT FROM AUTHOR]

Herein, a novel, convenient, and highly selective electrochemical sensor for determination of nitrite based on a polythiophene-derivative film-modified glassy carbon electrode (GCE) was established. In this work, 2,5-di-thiophen-3-yl-thiazolo[5,4-d]thiazole (DTT), a novel thiophene derivative, was synthesized and used to form an original and excellent polymer film (PolyDTTF) on GCE through one-step electropolymerization for the first time. The modified electrodes were characterized by electron microscopy (SEM), Fourier transform infra-red spectroscopy (FT-IR), UV-visible spectra, Raman spectroscopy, and electrochemical technologies, in which the electrochemical sensor based on PolyDTTF was successfully constructed and demonstrated a significant electrocatalytic effect on nitrite. The influence of pH value, electrodeposition scanning times, scanning speed, and potential on the electrochemical behavior of nitrite were investigated in detail. Furthermore, the nitrite sensor exhibits excellent responses proportional to nitrite concentrations (R2 = 0.9972) over a concentration range of 5.5 × 10−9 ~ 3.5 × 10−5 M with a detection limit (LOD) of 2 nM, and has extremely good anti-interference ability for nitrite detection. This proposed sensor can be used to detect nitrite in actual samples, opening the possibility for applications in the food industry and environmental analysis. [ABSTRACT FROM AUTHOR]

Abstract: Silicon-on-insulator (SOI) wafer is one of the most appealing platforms for optical integrated circuit with the potential to realize high performance Ultra Large Scale Integration (ULSI) and device miniaturization. In this work, based on simulations to obtain appropriate optical properties of a porous silicon microcavity (PSM), we successfully fabricated a highly efficient PSM on SOI wafer by electrochemical etching for DNA detection at optical wavelength 1555.0nm. The narrow resonance peak with a full width at half maximum about 26.0nm in the reflectance spectrum gives a high Q factor which causes high sensitivity for sensing performance. The sensitivity of this sensor is investigated through 19-base pair DNA hybridization in the PSM by surface modification using a standard cross link chemistry method. The red shift of the reflectance spectra shows a good linear relationship with complementary DNA concentration, ranging from 0.625 to 12.500μM, and the detection limit is 43.9nM. This optical PSM on SOI is highly sensitive, fast responsive, easy to fabricate and low-costly, that will broadly benefit to develop a new optical label-free biosensor on SOI wafer and has a great potential for biochips based on integrated optical devices. [Copyright &y& Elsevier]

Abstract: Anatase titanium dioxide (TiO2) mesoporous microspheres with core–shell and hollow structure were successfully prepared on a large scale by a one-step template-free chemical vapor deposition method. The effects of various reaction conditions on the morphology, composition and structure of the products were investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller (BET) technique and photoluminescence (PL) method. The results indicate that the product near the source was composed of core–shell structure TiO2 microspheres with diameters from 3 to 5μm. With increasing the distance between the source materials and the substrate, the hollow TiO2 spheres with 1–2μm dominant the products. A localized Ostwald ripening can be use to explain the formation of core–shell and hollow structures, and the size of the initial TiO2 solid nanoparticles plays an important role in determining the evacuation manner of the solid in the ripening-induced hollowing process. The surface area of TiO2 hollow microspheres determined by the adsorption isotherms was measured to be 74.67m2/g. X-ray photoelectron spectroscopy (XPS) analysis revealed that the O–H peaks of hollow structures have a chemical shift compared with the core–shell structures. The optical property of the products was also discussed. [Copyright &y& Elsevier]

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. [ABSTRACT FROM AUTHOR]

[Display omitted] • Designed and synthesized two novel pyrene-functionalized Ir-complexes, Pyrene-Ir1 and Pyrene-Ir2 , which demonstrated exceptional coenzyme NADH generation performances and could be easily modified onto carbon nanotube materials by mild π-π conjugated adsorption. • Prepared a novel dual-core drive heterogeneous catalyst Ir@CNT@ADH, and the proposed strategy has significantly improved the mutual inactivation problem between bio-enzymes and iridium complexes. • Novel dual-core drive heterogeneous catalyst Ir@CNT@ADH showed excellent catalytic hydrogeneration performances for various benzaldehyde derivatives, good recyclability. Here, a novel dual-core drive transfer hydrogen heterogeneous catalysts Ir@CNT@ADH were developed by sequentially immobilizing the NADH-dependent alcohol dehydrogenase ADH and high active NADH-regenerated catalyst Pyren-Ir1 on the surface of carbon nanotubes. Two pyrene-functionalized Ir-complex, Pyrene-Ir1 and Pyrene-Ir2 , were synthesized and demonstrated exceptional coenzyme NADH generation performances, ensuring continuous in situ NADH generation in the enzyme-catalyzed process. These complexes could be easily modified onto carbon nanotube materials by mild π-π conjugated adsorption. The catalysts Ir@CNT@ADH showed significantly improved catalytic hydrogenation efficiency in the reduction of benzaldehyde to the corresponding alcohol, compared to the single Pyrene-Ir1 modified carbon nanotube catalyst Ir@CNT and homogeneous catalytic system in the presence of a catalytic amount of NAD+. The yield of benzyl alcohol increased by more than 50 % in all cases. We assume that the designed dual-core drive heterogeneous catalyst strategy could effectively address the mutual inactivation problem of enzyme and Iridium complexes by blocking the contact between enzymes with Iridium complexes and enhancing the in situ regeneration property of coenzyme NADH in the enzyme catalytic system. Furthermore, catalyst Ir@CNT@ADH also demonstrated excellent catalytic hydrogeneration performances for various substituted benzaldehyde with good recycling performance. Therefore, the proposed dual-core drive heterogeneous catalyst strategy holds great potential for the combined industrial application of chemical catalysts and bioenzymes. It is expected to contribute to the development and preparation of higher-value biochemical products. [ABSTRACT FROM AUTHOR]

Food safety is a global issue; risk prevention and control of food physical and chemical testing (PCT) data are critical. However, existing studies have not considered the risk status of qualified products near the detection limit. Therefore, this paper presents a data-driven paradigm to detect the combined risk of multiple indicators across products in real-world food safety datasets for the first time. The following challenges exist to achieve this goal. (1) In real-world scenarios, neighboring samples are somewhat correlated; however, capturing and quantifying the actual testing sample correlation network remains an open problem in food computing. (2) The low-frequency nature of the unqualified samples leads to label imbalance in the data. (3) Labeling dependency limits the application scenarios. This article proposes a novel self-supervised framework (SUCOLA) to address the abovementioned challenges. That jointly learns the optimal graph topology and also inferences the connections between every product node and its well-designed subgraph, helping to resist label imbalance. We applied it to a real-world sterilized PCT dataset from a province in China. Extensive experiments show that SUCOLA performance is outstandingly better than several baselines of the current graph self-supervision field. Meanwhile, we conducted generalizability experiments on five well-known benchmark datasets. The proposed SUCOLA surpasses the current state-of-the-art by a considerable margin (0.39%–8.7% AUC) on public datasets. Thus, our study provides a new idea for early warning research on PCT data in food computing and fills the gap in the study of structural learning under category imbalance. [ABSTRACT FROM AUTHOR]

To further improve the photoelectrocatalytic activity of TiO 2 through metal-free strategy for wastewater treatment, a new type of graphite carbon/TiO 2 heterojunction nanomeshes (GC/TiO 2 HNMs) was successfully prepared by the facile electrochemical-hydrothermal method. By optimizing the precursor (glucose) concentration and reaction current, the GC/TiO 2 HNMs shows excellent photoelectrocatalytic activity towards methylene blue, rhodamine B, methyl orange and methyl violet, which could degrade the dyes by more than 95% within 120 min. The degradation efficiency of the GC/TiO 2 HNMs did not decrease significantly after being reused for 5 times. Besides, modification of graphite carbon on TiO 2 NMs could increase the photocurrent by more than 5 times and dramatically decrease the ionic resistance. The recombination of photogenerated electron-hole pairs was prevented and the electrons were quickly conducted by graphite carbon, which facilitates the production of •O 2 - and enhances the photoelectrocatalytic activity of the GC/TiO 2 HNMs. Free radical quenching experiments and electron paramagnetic resonance spectroscopy (EPR) verified that •O 2 - played the main role in photoelectrocatalytic dye degradation. [Display omitted] [ABSTRACT FROM AUTHOR]

To further improve the photoelectrocatalytic activity of TiO 2 through metal-free strategy for wastewater treatment, a new type of graphite carbon/TiO 2 heterojunction nanomeshes (GC/TiO 2 HNMs) was successfully prepared by the facile electrochemical-hydrothermal method. By optimizing the precursor (glucose) concentration and reaction current, the GC/TiO 2 HNMs shows excellent photoelectrocatalytic activity towards methylene blue, rhodamine B, methyl orange and methyl violet, which could degrade the dyes by more than 95% within 120 min. The degradation efficiency of the GC/TiO 2 HNMs did not decrease significantly after being reused for 5 times. Besides, modification of graphite carbon on TiO 2 NMs could increase the photocurrent by more than 5 times and dramatically decrease the ionic resistance. The recombination of photogenerated electron-hole pairs was prevented and the electrons were quickly conducted by graphite carbon, which facilitates the production of •O 2 - and enhances the photoelectrocatalytic activity of the GC/TiO 2 HNMs. Free radical quenching experiments and electron paramagnetic resonance spectroscopy (EPR) verified that •O 2 - played the main role in photoelectrocatalytic dye degradation. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, a total of 20 sites of single nucleotide polymorphisms (SNPs) on the serotonin 3 receptor A gene (HTR3A) and B gene (HTR3B) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (GS-SVM), the convolutional neural network (CNN) and the convolutional neural network combined with long and short-term memory (CNN-LSTM) are used to classify and discriminate between alcohol-dependent patients (AD) and the non-alcohol-dependent control group. The results show that 19 SNPs combined with academic qualifications have the best discrimination effect. In the GS-SVM, the area under the receiver operating characteristic (ROC) curve (AUC) is 0.87, the AUC of CNN-LSTM is 0.88, and the performance of the CNN model is the best, with an AUC of 0.92. This study shows that the CNN model can more accurately discriminate AD than the SVM to treat patients in time. [ABSTRACT FROM AUTHOR]

As a characteristic edible fungus with a high nutritional value and medicinal effect, the Bachu mushroom has a broad market. To distinguish among Bachu mushrooms with high value and other fungi effectively and accurately, as well as to explore a universal identification method, this study proposed a method to identify Bachu mushrooms by Fourier Transform Infrared Spectroscopy (FT-IR) combined with machine learning. In this experiment, two kinds of common edible mushrooms, Lentinus edodes and club fungi, were selected and classified with Bachu mushrooms. Due to the different distribution of nutrients in the caps and stalks, the caps and stalks were studied in this experiment. By comparing the average normalized infrared spectra of the caps and stalks of the three types of fungi, we found differences in their infrared spectra, indicating that the latter can be used to classify and identify the three types of fungi. We also used machine learning to process the spectral data. The overall steps of data processing are as follows: use partial least squares (PLS) to extract spectral features, select the appropriate characteristic number, use different classification algorithms for classification, and finally determine the best algorithm according to the classification results. Among them, the basis of selecting the characteristic number was the cumulative variance interpretation rate. To improve the reliability of the experimental results, this study also used the classification results to verify the feasibility. The classification algorithms used in this study were the support vector machine (SVM), backpropagation neural network (BPNN) and k-nearest neighbors (KNN) algorithm. The results showed that the three algorithms achieved good results in the multivariate classification of the caps and stalks data. Moreover, the cumulative variance explanation rate could be used to select the characteristic number. Finally, by comparing the classification results of the three algorithms, the classification effect of KNN was found to be the best. Additionally, the classification results were as follows: according to the caps data classification, the accuracy was 99.06%; according to the stalks data classification, the accuracy was 99.82%. This study showed that infrared spectroscopy combined with a machine learning algorithm has the potential to be applied to identify Bachu mushrooms and the cumulative variance explanation rate can be used to select the characteristic number. This method can also be used to identify other types of edible fungi and has a broad application prospect. [ABSTRACT FROM AUTHOR]

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF. [ABSTRACT FROM AUTHOR]

To improve the detection sensitivity of porous silicon microcavity biosensors, CdSe/ZnS quantum dots are used to label complementary DNA molecules for the refractive index amplification and angular spectrum method for detection. In this method, the TE mode laser is used as the detection light and the light source is changed into a parallel beam by collimating and expanding the beam, which illuminates the PSM surface and receives the reflected light from the PSM surface through the detector. The angle corresponding to the weakest reflected light intensity before and after the biological reaction between probe DNA and complementary DNA of different concentrations labeled by quantum dots was measured by the detector, and the relationship between the angle change before and after the biological reaction and the complementary DNA concentration labeled by quantum dots was obtained. The experimental results show that the angle change increases linearly with increasing complementary DNA concentration. The detection limit of the experiment, as determined by fitting, is approximately 36 pM. The detection limit of this method is approximately 1/300 of that without quantum dot labeling. Our method has a low cost because it does not require the use of a reflectance spectrometer, and it also demonstrates high sensitivity. [ABSTRACT FROM AUTHOR]

Clopidol is one of the most widely used anti-coccidiosis drugs. Its residues in poultry products and the environment pose a serious threat to human health. In this work, microwave-assisted synthesis of magnetic ionic liquid/gold nanoparticles (MIL-Au NPs) as the SERS substrates were first designed for sensitive and reliable determination of clopidol residue in egg samples. The experiment shows that MIL(1-methyl-3-hexyl imidazole ferric tetrachloride ([C6mim]FeCl4)) and microwave play a key role in the dispersion and morphology of Au NPs. Under the optimal conditions, the as-prepared MIL-Au NPs were applied to the SERS detection of clopidol in methanol and egg solution and its detection limits can be as low as to 0.5 μg/kg (equal to 0.5 ppb) in both solutions. The standard curves with regression coefficients of 0.9298 and 0.93496 were constructed in the linear range of 100–1000 ppb and 0.5–50 ppb for clopidol in egg solutions. Moreover, satisfactory recoveries (97.5–103.2%) were obtained for egg samples. The developed SERS method provides a way for quantitation of clopidol and can be applied for the convenient, reliable, and highly sensitive detection of antibiotic residues in food and environment, which has great potential in food safety and biological monitoring. [ABSTRACT FROM AUTHOR]

In this work, a new dual signal light detection method based on porous silicon Bragg mirror (PSBM) and biological labelling with quantum dots (QDs) is proposed for the detection of beta-lactoglobulin (β-lg). The first signal light is a probe light emitted by a laser with wavelength of 633 nm, which enters the PSBM and is reflected from the surface. The wavelength of the probe light is located at the edge of the PSBM band gap, where it has the lowest reflectivity. β-lg antibodies is labelled with CdSe/ZnS QDs and reacts with β-lg molecules have been fixed to the inner wall of the porous silicon pores. Due to the specific binding of biomolecules in PSBM, the refractive index of the device increases, resulting in the enhancement of detection reflected light. The QDs play the role of refractive index amplification. The second signal light is the fluorescence of QDs in immune reactants. QDs produce fluorescence at 630 nm when excited by a short-wavelength laser. The fluorescence signal is further enhanced by PSBM. The superimposed images of two kinds of light on the surface of PSBM are obtained by digital microscope at the same time. By calculating the average grey value change of the image before and after biological reaction, β-lg can be detected with high sensitivity. The detection limit of β-lg was 0.12 ng/mL. The experimental results showed that the PSBM-based dual signal light method could be used to detect the content of cow milk adulterated in β-lg free camel milk. • The wavelengths of both probe light and fluorescence are selected at the band gap edge of porous silicon with Bragg structure. • The two changes of fluorescence and refractive index of the sensor before and after biological reaction are used to detect the target biomolecules. • Quantum dots play two roles in detection, one is to generate fluorescence after excitation and another is to achieve refractive index amplification. • Biological detection is implemented by the average grey value change of the images before and after biological reaction. • Proposed method can be applied to detect β-lactoglobulin. [ABSTRACT FROM AUTHOR]

In this paper, carbon quantum dot-labelled β-lactoglobulin antibodies were used for refractive index magnification, and β-lactoglobulin was detected by angle spectroscopy. In this method, the detection light is provided by a He-Ne laser whose central wavelength is the same as that of the porous silicon microcavity device, and the light source was changed to a parallel beam to illuminate the porous silicon microcavity' surface by collimating beam expansion, and the reflected light was received on the porous silicon microcavity' surface by a detector. The angle corresponding to the smallest luminous intensity before and after the onset of immune response was measured by a detector for different concentrations of β-lactoglobulin antigen and carbon quantum dot-labelled β-lactoglobulin antibodies, and the relationship between the variation in angle before and after the immune response was obtained for different concentrations of the β-lactoglobulin antigen. The results of the experiment present that the angle variations changed linearly with increasing β-lactoglobulin antigen concentration before and after the immune response. The limit of detection of β-lactoglobulin by this method was 0.73 μg/L, indicating that the method can be used to detect β-lactoglobulin quickly and conveniently at low cost. [ABSTRACT FROM AUTHOR]

As a rapid and non-destructive biological serum detection method, SERS technology was widely used in the screening and medical diagnosis of various diseases by combining the analysis of serum SERS spectrum and multivariate statistical algorithm. Because of the high complexity of serum components and the variability of SERS spectra, which often resulted in the phenomenon that the SERS spectrum of the same biological serum was significantly different due to the different test conditions. In this experiment, through the dilution treatment of the serum and the systematic test of the serum of all concentration gradients with lasers of wavelength of 785, 633 and 532 nm, the most suitable conditions for detecting the serum were investigated. The experimental results showed that only when the serum is diluted to low concentration (10 ppm), the SERS spectrum with high reproducibility and stability could be obtained, furthermore, the low concentration serum had weak tolerance to laser, and 532 nm laser was not suitable for serum detection. In this paper, a set of test scheme for obtaining highly stable serum SERS spectra was established by using high-performance gold nanoparticles (Au NPs) as the active substrate of SERS. Through comparative analysis of SERS spectrum of serum of normal people and cervical cancer, the reliability of the established low-concentration serum test program was verified, as well as its great potential advantages in disease screening and diagnosis. [Display omitted] • The reason of the variability of serum SERS spectrum was verified. • A low-concentration serum SERS detection scheme was established. • High reproducibility serum SERS spectrum can be obtained by using this scheme. • The scheme provides guarantee for the reliability of SERS technology in medical diagnosis. [ABSTRACT FROM AUTHOR]

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 (C5) is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features. [ABSTRACT FROM AUTHOR]

In this paper, we embed two types of attention modules in the dilated fully convolutional network (FCN) to solve biomedical image segmentation tasks efficiently and accurately. Different from previous work on image segmentation through multiscale feature fusion, we propose the fully convolutional attention network (FCANet) to aggregate contextual information at long-range and short-range distances. Specifically, we add two types of attention modules, the spatial attention module and the channel attention module, to the Res2Net network, which has a dilated strategy. The features of each location are aggregated through the spatial attention module, so that similar features promote each other in space size. At the same time, the channel attention module treats each channel of the feature map as a feature detector and emphasizes the channel dependency between any two channel maps. Finally, we weight the sum of the output features of the two types of attention modules to retain the feature information of the long-range and short-range distances, to further improve the representation of the features and make the biomedical image segmentation more accurate. In particular, we verify that the proposed attention module can seamlessly connect to any end-to-end network with minimal overhead. We perform comprehensive experiments on three public biomedical image segmentation datasets, i.e., the Chest X-ray collection, the Kaggle 2018 data science bowl and the Herlev dataset. The experimental results show that FCANet can improve the segmentation effect of biomedical images. The source code models are available at https://github.com/luhongchun/FCANet. [ABSTRACT FROM AUTHOR]