Photochromism and single-component white light emission from a metalloviologen complex based on 1,5-naphthyridine.

Metalloviologens, as emerging electron-transfer photochromic compounds, have shown intriguing properties such as radiochromism, photochromism and photoconductance. However, only a limited number of them have been reported so far. Exploration of new metalloviologens is strongly desired. Herein, we report a new solvothermally synthesized metalloviologen complex [CdCl2(ND)2]n (1, ND = 1,5-naphthalenes) that exhibits photochromic and intrinsic white light emission properties. Density functional theory calculation results reveal that the photochromism could be assigned to photoinduced electron transfer from chlorine atoms to ND molecules. The photoinduced charge-separated states are heat/air stable, attributed to the delocalization of ND and strong intermolecular π-π interactions. Besides, complex 1 consistently emits intrinsic white light when excited with 340-370 nm UV light, achieving high color rendering index (CRI) values (82.54-94.04). By adjusting the excitation wavelength, both "warm" and "cold" white light emission can be produced, making it suitable for the application of a white light emitting diode (WLED). Thus, this work demonstrates that the ND-based metalloviologen is not only helpful in producing photochromism, but also beneficial for creating white-light emission.

A novel isophorone-based NIR fluorescent and colormetric probe for Al3+ sensing and its application for living cells and plants imaging.

In this paper, an isophorone-based NIR fluorescent and colormetric probe BDDH for Al3+ was synthesized and characterized, it showed highly selectivity and sensitivity through significant fluorescence enhancement and visible color change towards Al3+. The job plot confirmed that the binding ratio of BDDH with Al3+ was 1:1. Furthermore, the limit of detection (LOD) of Al3+ was determined to be 4.01 × 10-8 M. Moreover, BDDH was successfully applicated in identification of Al3+ in the different water samples, cell imaging in alive MCF-7 cells and plant imaging in soybean roots.

MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation.

BACKGROUND: Fusing the CNN and Transformer in the encoder has recently achieved outstanding performance in medical image segmentation. However, two obvious limitations require addressing: (1) The utilization of Transformer leads to heavy parameters, and its intricate structure demands ample data and resources for training, and (2) most previous research had predominantly focused on enhancing the performance of the feature encoder, with little emphasis placed on the design of the feature decoder. METHODS: To this end, we propose a novel MLP-CNN based dual-path complementary (MC-DC) network for medical image segmentation, which replaces the complex Transformer with a cost-effective Multi-Layer Perceptron (MLP). Specifically, a dual-path complementary (DPC) module is designed to effectively fuse multi-level features from MLP and CNN. To respectively reconstruct global and local information, the dual-path decoder is proposed which is mainly composed of cross-scale global feature fusion (CS-GF) module and cross-scale local feature fusion (CS-LF) module. Moreover, we leverage a simple and efficient segmentation mask feature fusion (SMFF) module to merge the segmentation outcomes generated by the dual-path decoder. RESULTS: Comprehensive experiments were performed on three typical medical image segmentation tasks. For skin lesions segmentation, our MC-DC network achieved 91.69% Dice and 9.52mm ASSD on the ISIC2018 dataset. In addition, the 91.6% Dice and 94.4% Dice were respectively obtained on the Kvasir-SEG dataset and CVC-ClinicDB dataset for polyp segmentation. Moreover, we also conducted experiments on the private COVID-DS36 dataset for lung lesion segmentation. Our MC-DC has achieved 87.6% [87.1%, 88.1%], and 92.3% [91.8%, 92.7%] on ground-glass opacity, interstitial infiltration, and lung consolidation, respectively. CONCLUSIONS: The experimental results indicate that the proposed MC-DC network exhibits exceptional generalization capability and surpasses other state-of-the-art methods in higher results and lower computational complexity.

An optimized optical-flow-based method for quantitative tracking of ultrasound-guided right diaphragm deformation.

OBJECTIVES: To develop a quantitative analysis method for right diaphragm deformation. This method is based on optical flow and applied to diaphragm ultrasound imaging. METHODS: This study enrolls six healthy subjects and eight patients under mechanical ventilation. Dynamic images with 3-5 breathing cycles were acquired from three directions of right diaphragm by a portable ultrasound system. Filtering and density clustering algorithms are used for denoising Digital Imaging and Communications in Medicine (DICOM) data. An optical flow based method is applied to track movements of the right diaphragm. An improved drift correction algorithm is used to optimize the results. The method can automatically analyze the respiratory cycle, inter-frame/cumulative vertical and horizontal displacements, and strain of the input right diaphragm ultrasound image. RESULTS: The optical-flow-based diaphragm ultrasound image motion tracking algorithm can accurately track the right diaphragm during respiratory motion. There are significant differences in horizontal and vertical displacements in each section (p-values < 0.05 for all). Significant differences are found between healthy subjects and mechanical ventilation patients for both horizontal and vertical displacements in Section III (p-values < 0.05 for both). There is no significant difference in global strain in each section between healthy subjects and mechanical ventilation patients (p-values > 0.05 for all). CONCLUSIONS: The developed method can quantitatively evaluate the inter-frame/cumulative displacement of the diaphragm in both horizontal and vertical directions, as well as the global strain in three different imaging planes. The above indicators can be used to evaluate diaphragmatic dynamics.

Local attention and long-distance interaction of rPPG for deepfake detection.

With the development of generative models, abused Deepfakes have aroused public concerns. As a defense mechanism, face forgery detection methods have been intensively studied. Remote photoplethysmography (rPPG) technology extract heartbeat signal from recorded videos by examining the subtle changes in skin color caused by cardiac activity. Since the face forgery process inevitably disrupts the periodic changes in facial color, rPPG signal proves to be a powerful biological indicator for Deepfake detection. Motivated by the key observation that rPPG signals produce unique rhythmic patterns in terms of different manipulation methods, we regard Deepfake detection also as a source detection task. The Multi-scale Spatial-Temporal PPG map is adopted to further exploit heartbeat signal from multiple facial regions. Moreover, to capture both spatial and temporal inconsistencies, we propose a two-stage network consisting of a Mask-Guided Local Attention module (MLA) to capture unique local patterns of PPG maps, and a Temporal Transformer to interact features of adjacent PPG maps in long distance. Abundant experiments on FaceForensics + + and Celeb-DF datasets prove the superiority of our method over all other rPPG-based approaches. Visualization also demonstrates the effectiveness of the proposed method.

Phosphorus and its solubility in aerosols from continental and marine sources in the sea areas near China: Results from a 40-day cruise mission in late spring.

Accurate assessments of soluble phosphorus (P) in aerosol particles are essential to understand the atmospheric nutrients supply to the marine ecosystem. We quantified total P (TP) and dissolved P (DP) in the aerosol particles collected in the sea areas near China in a cruise mission from May 1 to June 11, 2016. The overall concentrations of TP and DP were 3.5-99.9 ng m-3 and 2.5-27.0 ng m-3, respectively. When the air originating from the desert areas, TP and DP were 28.7-99.9 ng m-3 and 10.8-27.0 ng m-3, respectively, and P solubility was 24.1-54.6 %. When the air influenced mainly by anthropogenic emissions from eastern China, TP and DP were 11.7-12.3 ng m-3 and 5.7-6.3 ng m-3, respectively, and P solubility was 46.0-53.7 %. More than half of the TP and more than 70 % of the DP were from pyrogenic particles, with a considerable DP converted via aerosol acidification after the particles met humid marine air. On average, aerosol acidification promoted the fractional solubility of dissolved inorganic P (DIP) to TP from 22 % to 43 %. When the air originating from the marine areas, TP and DP were 3.5-22.0 ng m-3 and 2.5-8.4 ng m-3, respectively, and P solubility was 34.6-93.6 %. About one-third of the DP was from biological emissions in organic forms (DOP), leading to higher solubility than in the particles from continental sources. These results reveal the dominance of inorganic P in TP and DP from the desert and anthropogenic mineral dust and the significant contribution of organic P from marine sources. The results also indicate the necessity to treat aerosol P carefully according to different sources of the aerosol particles and atmospheric processes the particles experience in assessing aerosol P input to seawater.

MESTrans: Multi-scale embedding spatial transformer for medical image segmentation.

BACKGROUND AND OBJECTIVE: Transformers profiting from global information modeling derived from the self-attention mechanism have recently achieved remarkable performance in computer vision. In this study, a novel transformer-based medical image segmentation network called the multi-scale embedding spatial transformer (MESTrans) was proposed for medical image segmentation. METHODS: First, a dataset called COVID-DS36 was created from 4369 computed tomography (CT) images of 36 patients from a partner hospital, of which 18 had COVID-19 and 18 did not. Subsequently, a novel medical image segmentation network was proposed, which introduced a self-attention mechanism to improve the inherent limitation of convolutional neural networks (CNNs) and was capable of adaptively extracting discriminative information in both global and local content. Specifically, based on U-Net, a multi-scale embedding block (MEB) and multi-layer spatial attention transformer (SATrans) structure were designed, which can dynamically adjust the receptive field in accordance with the input content. The spatial relationship between multi-level and multi-scale image patches was modeled, and the global context information was captured effectively. To make the network concentrate on the salient feature region, a feature fusion module (FFM) was established, which performed global learning and soft selection between shallow and deep features, adaptively combining the encoder and decoder features. Four datasets comprising CT images, magnetic resonance (MR) images, and H&E-stained slide images were used to assess the performance of the proposed network. RESULTS: Experiments were performed using four different types of medical image datasets. For the COVID-DS36 dataset, our method achieved a Dice similarity coefficient (DSC) of 81.23%. For the GlaS dataset, 89.95% DSC and 82.39% intersection over union (IoU) were obtained. On the Synapse dataset, the average DSC was 77.48% and the average Hausdorff distance (HD) was 31.69 mm. For the I2CVB dataset, 92.3% DSC and 85.8% IoU were obtained. CONCLUSIONS: The experimental results demonstrate that the proposed model has an excellent generalization ability and outperforms other state-of-the-art methods. It is expected to be a potent tool to assist clinicians in auxiliary diagnosis and to promote the development of medical intelligence technology.

Chain conformation, mucoadhesive properties of fucoidan in the gastrointestinal tract and its effects on the gut microbiota.

Fucoidans are valuable marine polysaccharides with various bioactivities and physicochemical properties. However, its digestive properties, mucoadhesive properties, and bioactivity in the gastrointestinal tract are still unclear. In this study, simulated digestion, fecal fermentation in vitro, and rheology models were utilized to investigate the chain conformation, influence on gut microbiota, and mucin adhesive properties of fucoidan from the sea cucumber Thelenota ananas (Ta-FUC). The results showed that Ta-FUC was nondigestible with a temporary decrease in molecular weight in gastric conditions, accompanied by the chain conformation becoming more flexible. Moreover, Ta-FUC exhibited strong mucin adhesive function in the simulated intestinal environment, with supramolecular disulfide, hydrogen, and hydrophobic interactions in order of intensity. During fermentation, Ta-FUC was degraded by the intestinal flora to produce various short-chain fatty acids and promoted the relative abundance of Bacteroidota and Firmicutes, reducing the proportion of Proteobacteria. Therefore, these results indicate that Ta-FUC could be a potential prebiotic and ingredient for developing targeted delivery systems in the functional food and pharmaceutical industries.

A novel dual-function probe for fluorescent turn-on recognition and differentiation of Al3+ and Ga3+ and its application.

In this study, a novel dual-function probe BMP based on benzothiazole was easily synthesized and characterized through common optical technique. In the system consisting of DMF/H2O (v/v, 2/3), probe BMP showed azure and blue-green to Al3+ and Ga3+, respectively. Besides, the binding ratios of BMP to Al3+ and Ga3+ were determined as 1:1, which confirmed by Job's plot. Furthermore, for Al3+ and Ga3+, the limit of detection (LOD) was determined to be 1.51 × 10-6 M and 4.28 × 10-6 M, respectively. Moreover, it was worth noting that BMP showed good performances in paper colorimetry, cell phone colorimetric identification and cell imaging.

Multi-scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images.

PURPOSE: The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI. METHODS: First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM. RESULTS: For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm. CONCLUSIONS: The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.

PURPOSE: Early detection is significant to reduce lung cancer-related death. Computer-aided detection system (CADs) can help radiologists to make an early diagnosis. In this paper, we propose a novel 3D gray density coding feature (3D GDC) and fuse it with extracted geometric features. The fusion feature and random forest are used for benign-malignant pulmonary nodule classification on Chest CT. METHODS: First, a dictionary model is created to acquire codebook. It is used to obtain feature descriptors and includes 3D block database (BD) and distance matrix clustering centers. 3D BD is balanced and randomly selecting from benign and malignant pulmonary nodules of training data. Clustering centers is got by clustering the distance matrix, which is the distance between every two blocks in 3D BD. Then, feature descriptor is obtained by coding the pulmonary nodule with codebook, and 3D GDC feature is the result of histogram statistics on feature descriptor. Second, geometric features are extracted for fusion feature. Finally, random forest is performed for benign-malignant pulmonary nodule classification with fusion feature of the 3D gray density coding feature and the geometric features. RESULTS: We verify the effectiveness of our method on the public LIDC-IDRI dataset and the private ZSHD dataset. For LIDC-IDRI dataset, compared with other state-of-the-art methods, we achieve more satisfactory results with 93.17 ± 1.94% for accuracy and 97.53 ± 1.62% for AUC. As for private ZSHD dataset, it contains a total of 238 lung nodules from 203 patients. The accuracy and AUC achieved by our method are 90.0% and 93.15%. CONCLUSIONS: The results show that our method can provide doctors with more accurate results of benign-malignant pulmonary nodule classification for auxiliary diagnosis, and our method is more interpretable than 3D CNN methods, which can provide doctors with more auxiliary information.

NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.

Neuropeptides acting as signaling molecules in the nervous system of various animals play crucial roles in a wide range of physiological functions and hormone regulation behaviors. Neuropeptides offer many opportunities for the discovery of new drugs and targets for the treatment of neurological diseases. In recent years, there have been several data-driven computational predictors of various types of bioactive peptides, but the relevant work about neuropeptides is little at present. In this work, we developed an interpretable stacking model, named NeuroPpred-Fuse, for the prediction of neuropeptides through fusing a variety of sequence-derived features and feature selection methods. Specifically, we used six types of sequence-derived features to encode the peptide sequences and then combined them. In the first layer, we ensembled three base classifiers and four feature selection algorithms, which select non-redundant important features complementarily. In the second layer, the output of the first layer was merged and fed into logistic regression (LR) classifier to train the model. Moreover, we analyzed the selected features and explained the feasibility of the selected features. Experimental results show that our model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming the state-of-the-art models. In addition, we exhibited the distribution of selected features by these tree models and compared the results on the training set to that on the test set. These results fully showed that our model has a certain generalization ability. Therefore, we expect that our model would provide important advances in the discovery of neuropeptides as new drugs for the treatment of neurological diseases.

A highly selective colorimetric and fluorescent turn-on chemosensor for Al(3+) based on naphthalimide derivative.

A new chemosensor L based on the naphthalimide moiety was synthesized and characterized. L exhibited the high selectivity and sensitivity for Al(3+) in CH3OH, along with colorimetric and fluorometric dual-signaling responses based on the joint contribution of the ICT and CHEF processes. A 1:1 stoichiometry for the L-Al(3+) complex was formed with an association constant of 7.6×10(4)M(-1), and the limit of detection for Al(3+) was determined as 6.9µM. In addition, L was successfully applied to the determination of Al(3+) in real water samples.