Protic Amine Carboxylic Acid Ionic Liquids Additives Regulate α-FAPbI3 Phase Transition for High Efficiency Perovskite Solar Cells.

The α-phase formamidinium lead tri-iodide (α-FAPbI3 ) has become the most promising photovoltaic absorber for perovskite solar cells (PSCs) due to its outstanding semiconductor properties and astonishing high efficiency. However, the incomplete crystallization and phase transition of α-FAPbI3 substantially undermine the performance and stability of PSCs. In this work, a series of the protic amine carboxylic acid ion liquids are introduced as the precursor additives to efficiently regulate the crystal growth and phase transition processes of α-FAPbI3 . The MA2 Pb3 I8 ·2DMSO phase is inhibited in annealing process, which remarkably optimizes the phase transition process of α-FAPbI3 . It is noted that the functional groups of carboxyl and ammonium passivate the undercoordinated lead ions, halide vacancies, and organic vacancies, eliminating the deleterious nonradiative recombination. Consequently, the small-area devices incorporated with 2% methylammonium butyrate (MAB) and 1.5% n-butylammonium formate (BAFa) in perovskite show champion efficiencies of 25.10% and 24.52%, respectively. Furthermore, the large-area modules (5 cm × 5 cm) achieve PCEs of 21.26% and 19.27% for MAB and BAFa additives, indicating the great potential for commercializing large-area PSCs.

Low-Cost Hydroxyacid Potassium Synergists as an Efficient In Situ Defect Passivator for High Performance Tin-Oxide-Based Perovskite Solar Cells.

Perovskite solar cells (PSCs) based on SnO2 electron transport layers have attracted extensive research due to their compelling photovoltaic performance. Herein, we presented an in situ passivation of SnO2 with low-cost hydroxyacid potassium synergist during deposition to optimize the interface carrier extraction and transport for high power conversion efficiency (PCE) and stabilities of PSCs. The orbital overlap of the carboxyl oxygen with the Sn atom alongwith the homogenous nano-particle deposition effectively suppresses the interfacial defects and releases the internal residual strains in the perovskite. Accordingly, a PCE of 24.91 % with a fill factor (FF) up to 0.852 is obtained for in situ passivated devices, which is one of the highest values for SnO2 -based PSCs. Moreover, the unencapsulated device maintained 80 % of its initial PCE at 80 °C over 600 h, 100 % PCE at ambient conditions for 1300 h, and 98 % after one week maximum power point tracking (MPPT) under continuous AM1.5G illumination.

Graph Convolutional Network Discrete Hashing for Cross-Modal Retrieval.

With the rapid development of deep neural networks, cross-modal hashing has made great progress. However, the information of different types of data is asymmetrical, that is to say, if the resolution of an image is high enough, it can reproduce almost 100% of the real-world scenes. However, text usually carries personal emotion and it is not objective enough, so we generally think that the information of image will be much richer than text. Although most of the existing methods unify the semantic feature extraction and hash function learning modules for end-to-end learning, they ignore this issue and do not use information-rich modalities to support information-poor modalities, leading to suboptimal results, although they unify the semantic feature extraction and hash function learning modules for end-to-end learning. Furthermore, previous methods learn hash functions in a relaxed way that causes nontrivial quantization losses. To address these issues, we propose a new method called graph convolutional network (GCN) discrete hashing. This method uses a GCN to bridge the information gap between different types of data. The GCN can represent each label as word embedding, with the embedding regarded as a set of interdependent object classifiers. From these classifiers, we can obtain predicted labels to enhance feature representations across modalities. In addition, we use an efficient discrete optimization strategy to learn the discrete binary codes without relaxation. Extensive experiments conducted on three commonly used datasets demonstrate that our proposed method graph convolutional network-based discrete hashing (GCDH) outperforms the current state-of-the-art cross-modal hashing methods.

Self-Supervised Enhancement for Named Entity Disambiguation via Multimodal Graph Convolution.

Named entity disambiguation (NED) finds the specific meaning of an entity mention in a particular context and links it to a target entity. With the emergence of multimedia, the modalities of content on the Internet have become more diverse, which poses difficulties for traditional NED, and the vast amounts of information make it impossible to manually label every kind of ambiguous data to train a practical NED model. In response to this situation, we present MMGraph, which uses multimodal graph convolution to aggregate visual and contextual language information for accurate entity disambiguation for short texts, and a self-supervised simple triplet network (SimTri) that can learn useful representations in multimodal unlabeled data to enhance the effectiveness of NED models. We evaluated these approaches on a new dataset, MMFi, which contains multimodal supervised data and large amounts of unlabeled data. Our experiments confirm the state-of-the-art performance of MMGraph on two widely used benchmarks and MMFi. SimTri further improves the performance of NED methods. The dataset and code are available at https://github.com/LanceZPF/NNED_MMGraph.

LSCIDMR: Large-Scale Satellite Cloud Image Database for Meteorological Research.

People can infer the weather from clouds. Various weather phenomena are linked inextricably to clouds, which can be observed by meteorological satellites. Thus, cloud images obtained by meteorological satellites can be used to identify different weather phenomena to provide meteorological status and future projections. How to classify and recognize cloud images automatically, especially with deep learning, is an interesting topic. Generally speaking, large-scale training data are essential for deep learning. However, there is no such cloud images database to date. Thus, we propose a large-scale cloud image database for meteorological research (LSCIDMR). To the best of our knowledge, it is the first publicly available satellite cloud image benchmark database for meteorological research, in which weather systems are linked directly with the cloud images. LSCIDMR contains 104 390 high-resolution images, covering 11 classes with two different annotation methods: 1) single-label annotation and 2) multiple-label annotation, called LSCIDMR-S and LSCIDMR-M, respectively. The labels are annotated manually, and we obtain a total of 414 221 multiple labels and 40 625 single labels. Several representative deep learning methods are evaluated on the proposed LSCIDMR, and the results can serve as useful baselines for future research. Furthermore, experimental results demonstrate that it is possible to learn effective deep learning models from a sufficiently large image database for the cloud image classification.

Ultrasound to X-ray synthesis generative attentional network (UXGAN) for adolescent idiopathic scoliosis.

Standing X-ray radiograph with Cobb's method is the gold standard for scoliosis diagnosis. However, radiation hazard restricts its application, especially for close follow-up of adolescent patients. Compared with X-ray, ultrasound imaging has advantages of being radiation-free and real-time. To combine advantages of the above two imaging modalities, an ultrasound to X-ray synthesis generative attentional network (UXGAN) was proposed to synthesize ultrasound images into X-ray-like images. In this network, a cyclically consistent network was adopted and was trained end-to-end. An attention module was added and different residual blocks were designed. The quantitative comparison results demonstrated the superiority of our method to the state-of-the-art CycleGAN methods. We further compared the Cobb angle values measured on synthesized images and the real X-ray images, respectively. A good linear correlation (r = 0.95) was demonstrated between the two methods. The above results proved that the proposed method is of great significance for providing both X-ray images and ultrasound images based on the radiation-free ultrasound scanning.

Siamese Implicit Region Proposal Network With Compound Attention for Visual Tracking.

Recently, siamese-based trackers have achieved significant successes. However, those trackers are restricted by the difficulty of learning consistent feature representation with the object. To address the above challenge, this paper proposes a novel siamese implicit region proposal network with compound attention for visual tracking. First, an implicit region proposal (IRP) module is designed by combining a novel pixel-wise correlation method. This module can aggregate feature information of different regions that are similar to the pre-defined anchor boxes in Region Proposal Network. To this end, the adaptive feature receptive fields then can be obtained by linear fusion of features from different regions. Second, a compound attention module including a channel and non-local attention is raised to assist the IRP module to perform a better perception of the scale and shape of the object. The channel attention is applied for mining the discriminative information of the object to handle the background clutters of the template, while non-local attention is trained to aggregate the contextual information to learn the semantic range of the object. Finally, experimental results demonstrate that the proposed tracker achieves state-of-the-art performance on six challenging benchmark tests, including VOT-2018, VOT-2019, OTB-100, GOT-10k, LaSOT, and TrackingNet. Further, our obtained results demonstrate that the proposed approach can be run at an average speed of 72 FPS in real time.

Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning.

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis. However, due to the complex morphology of atherosclerosis plaques and the lack of well-annotated data, the segmentation of lumen and wall is very challenging. Different from popular deep learning methods, in this paper, we propose an integration segmentation framework by introducing a lightweight prediction model and improved optimal surface graph cuts (OSG), which adopts a simplified flow line sampling and post-reconstructing method to reduce the cost of graph construction. Moreover, a flexibly adaptive smoothing penalty is presented for maintaining the shape of diseased carotid surface. For the experiments, we have collected an MR image dataset from patients with carotid atherosclerosis and evaluated our method by cross-validation. It can reach 89.68%/80.29% of dice coefficients and 0.2480 mm/0.3396 mm of average surface distances on the lumen/wall segmentation, respectively. The experimental results show that our method can generate precise and reliable segmentation of both lumen and wall of diseased carotid artery with a quite small training cost.

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.

[Preparation and pharmaceutical characterization of Marine-SSL].

OBJECTIVE: To prepare and characterize marine sterically stabilized liposomes (Marine-SSL). METHODS: Liposomes were prepared by ethanol injection technique. An orthogonal test was utilized to optimize the formulation and preparation of Marine-SSL The unencapsulated marine and liposomes were separated by sephadex gel G-50, the encapsulation efficiency was detected by HPLC. The morphological examination of Marine-SSL was performed using transmission electron microscopy. The particle size and Zeta potential of the liposomes were measured. The in vitro release rate of marine from liposomes was tested. RESULTS: The liposomes with spherical or ellipsoidal shape and better stability featured the encapsulation efficiency of (85.39 +/- 1.21)%, the mean partical size of (156 +/- 10) nm, and Zeta potential of (- 39.0 +/- 3.06) mv. The release kinetics in vitro obeyed Higuchi equation. The stability of Marine-SSL was better. CONCLUSION: The selected formulation and preparation technic of Marine-SSL are rational and stable and liposomes feature a sustained release in vitro.

Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images.

Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.

Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images.

BACKGROUND AND OBJECTIVE: Myocardial infarction (MI) is a myocardial anoxic incapacitation caused by severe cardiovascular obstruction that can cause irreversible injury or even death. In medical field, the electrocardiogram (ECG) is a common and effective way to diagnose myocardial infarction, which often requires a wealth of medical knowledge. It is necessary to develop an approach that can detect the MI automatically. METHODS: In this paper, we propose a multi-branch fusion framework for automatic MI screening from 12-lead ECG images, which consists of multi-branch network, feature fusion and classification network. First, we use text detection and position alignment to automatically separate twelve leads from ECG images. Then, those 12 leads are input into the multi-branch network constructed by a shallow neural network to get 12 feature maps. After concatenating those feature maps by depth fusion, classification is explored to judge the given ECG is MI or not. RESULTS: Based on extensive experiments on an ECG image dataset, performances of different combinations of structures are analyzed. The proposed network is compared with other networks and also compared with physicians in the practical use. All the experiments verify that the proposed method is effective for MI screening based on ECG images, which achieves accuracy, sensitivity, specificity and F1-score of 94.73%, 96.41%, 95.94% and 93.79% respectively. CONCLUSIONS: Rather than using the typical one-dimensional electrical ECG signal, this paper gives an effective model to screen MI by analyzing 12-lead ECG images. Extracting and analyzing these 12 leads from their corresponding ECG images is a good attempt in the application of MI screening.

Low Serum Uric Acid Levels Promote Hypertensive Intracerebral Hemorrhage by Disrupting the Smooth Muscle Cell-Elastin Contractile Unit and Upregulating the Erk1/2-MMP Axis.

Intracerebral hemorrhage (ICH) is a catastrophic stroke with high mortality, and the mechanism underlying ICH is largely unknown. Previous studies have shown that high serum uric acid (SUA) levels are an independent risk factor for hypertension, cardiovascular disease (CVD), and ischemic stroke. However, our metabolomics data showed that SUA levels were lower in recurrent intracerebral hemorrhage (R-ICH) patients than in ICH patients, indicating that lower SUA might contribute to ICH. In this study, we confirmed the association between low SUA levels and the risk for recurrence of ICH and for cardiac-cerebral vascular mortality in hypertensive patients. To determine the mechanism by which low SUA effects ICH pathogenesis, we developed the first low SUA mouse model and conducted transcriptome profiling of the cerebrovasculature of ICH mice. When combining these assessments with pathological morphology, we found that low SUA levels led to ICH in mice with angiotensin II (Ang II)-induced hypertension and aggravated the pathological progression of ICH. In vitro, our results showed that p-Erk1/2-MMP axis were involved in the low UA-induce degradation of elastin, and that physiological concentrations of UA and p-Erk1/2-specific inhibitor exerted a protective role. This is the first report describing to the disruption of the smooth muscle cell (SMC)-elastin contractile units in ICH. Most importantly, we revealed that the upregulation of the p-Erk1/2-MMP axis, which promotes the degradation of elastin, plays a vital role in mediating low SUA levels to exacerbate cerebrovascular rupture during the ICH process.

Weighted Mixed-Norm Regularized Regression for Robust Face Identification.

Face identification (FI) via regression-based classification has been extensively studied during the recent years. Most vector-based methods achieve appealing performance in handing the noncontiguous pixelwise noises, while some matrix-based regression methods show great potential in dealing with contiguous imagewise noises. However, there is a lack of consideration of the mixture noises case, where both contiguous and noncontiguous noises are jointly contained. In this paper, we propose a weighted mixed-norm regression (WMNR) method to cope with the mixture image corruption. WMNR reveals certain essential characteristics of FI problems and bridges the vector- and matrix-based methods. Particularly, WMNR provides two advantages for both theoretical analysis and practical implementation. First, it generalizes possible distributions of the residuals into a unified feature weighted loss function. Second, it constrains the residual image as low-rank structure that can be quantified with general nonconvex functions and a weight factor. Moreover, a new reweighted alternating direction method of multipliers algorithm is derived for the proposed WMNR model. The algorithm exhibits great computational efficiency since it divides the original optimization problem into certain subproblems with analytical solution or can be implemented in a parallel manner. Extensive experiments on several public face databases demonstrate the advantages of WMNR over the state-of-the-art regression-based approaches. More specifically, the WMNR achieves an appealing tradeoff between identification accuracy and computational efficiency. Compared with the pure vector-based methods, our approach achieves more than 10% performance improvement and saves more than 70% of runtime, especially in severe corruption scenarios. Compared with the pure matrix-based methods, although it requires slightly more computation time, the performance benefits are even larger; up to 20% improvement can be obtained.

Skeletal bone age assessments for young children based on regression convolutional neural networks.

Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back to 1950 were often tedious and suscep-tible to inter-observer variability, and preceding attempts to improve these traditional techniques have inadequately addressed the human expert inter-observer variability so as to significantly refine bone age evaluations. In this paper, an automated and efficient approach with regression convolutional neu-ral network is proposed. This approach automatically exploits the carpal bones as the region of interest (ROI) and performs boundary extraction of carpal bones, then based on the regression convolutional neural network it evaluates the skeletal age from the left hand wrist radiograph of young children. Experiments show that the proposed method achieves an average discrepancy of 2.75 months between clinical and automatic bone age evaluations, and achieves 90.15% accuracy within 6 months from the ground truth for male. Further experimental results with test radiographs assigned an accuracy within 1 year achieved 99.43% accuracy.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Online glocal transfer for automatic figure-ground segmentation.

This paper addresses the problem of automatic figure-ground segmentation, which aims at automatically segmenting out all foreground objects from background. The underlying idea of this approach is to transfer segmentation masks of globally and locally (glocally) similar exemplars into the query image. For this purpose, we propose a novel high-level image representation method named as object-oriented descriptor. Using this descriptor, a set of exemplar images glocally similar to the query image is retrieved. Then, using over-segmented regions of these retrieved exemplars, a discriminative classifier is learned on-the-fly and subsequently used to predict foreground probability for the query image. Finally, the optimal segmentation is obtained by combining the online prediction with typical energy optimization of Markov random field. The proposed approach has been extensively evaluated on three datasets, including Pascal VOC 2010, VOC 2011 segmentation challenges, and iCoseg dataset. Experiments show that the proposed approach outperforms state-of-the-art methods and has the potential to segment large-scale images containing unknown objects, which never appear in the exemplar images.

Similar to spironolactone, oxymatrine is protective in aldosterone-induced cardiomyocyte injury via inhibition of calpain and apoptosis-inducing factor signaling.

Accumulating evidence indicates that oxymatrine (OMT) possesses variously pharmacological properties, especially on the cardiovascular system. We previously demonstrated that activated calpain/apoptosis-inducing factor (AIF)-mediated pathway was the key molecular mechanism in aldosterone (ALD) induces cardiomyocytes apoptosis. In the present study, we extended the experimentation by investigating the effect of OMT on cardiomyocytes exposed to ALD, as compared to spironolactone (Spiro), a classical ALD receptor antagonist. Cardiomyocytes were pre-incubated with OMT, Spiro or vehicle for 1 h, and then, cardiomyocytes were exposed to ALD 24 h. The cell injury was evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and lactate dehydrogenase (LDH) leakage ratio. Apoptosis was determined by terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay, annexin V/PI staining, and relative caspase-3 activity assay. Furthermore, expression of pro-apoptotic proteins including truncated Bid (tBid), calpain and AIF were evaluated by western blot analysis. ALD stimulation increased cardiomyocytes apoptosis, caspase-3 activity and protein expression of calpain, tBid and AIF in the cytosol (p<0.05). Pre-incubated with cardiomyocytes injury and increased caspase-3 activity were significantly attenuated (p<0.05). Furthermore, OMT suppressed ALD-induced high expression of calpain and AIF. And these effects of OMT could be comparable to Spiro. These findings indicated that OMT might be a potential cardioprotective-agent against excessive ALD-induced cardiotoxicity, at least in part, mediated through inhibition of calpain/AIF signaling.