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The doubly salient electromagnetic generator (DSEG) has the advantages of flexible control, high robustness, and low cost. Since multiple stator poles share a common set of field windings, the magnetic circuit of each phase is asymmetrical, which limits the output performance of the DSEG. The three-phase current balance control for the DSEG system was proposed. By investigating the current waveform under the angular position control (APC), a three-phase current prediction method was proposed. The conduction angle of the power switches in the active rectifier was predicted to make the three-phase current balanced. Based on the analysis and experiment, the current waveform was further analyzed, and the effectiveness of the three-phase current balance control was verified. Compared with the APC method, the output power capacity was improved using the three-phase current balance control under a limited winding current density. The maximum potential of the DSEG was explored, and the electromagnetic load capacity of the DSEG was fully released. [ABSTRACT FROM AUTHOR]

Secoeudesma sesquiterpenes lactone A (SESLA) is a sesquiterpene derived from Inula japonica Thunb. and is known to possess many pharmacological properties, e.g. anti-tumor and anti-inflammatory activities. However, the immunomodulatory role of SESLA in gram-positive (G+) bacterial infection is not clear. To set up a G+ bacterial infection model in vitro, we carried out a bacterial mimic (PGN or Pam3CSK4) or Methicillin-resistant Staphylococcus aureus (MRSA) stimulated experiment using macrophages or dendritic cells (DCs). ELISA and qPCR were performed to measure the expression of inflammatory cytokines. Flow cytometry was used to detect the expression of MHC II and co-stimulatory molecules on the surface of DCs. The network pharmacology was used to identify the molecular mechanism and potential targets of SESLA that are predicted to be involved in the MRSA-elicited inflammation. Western blot and dual luciferase reporter assay were adopted to certify possible molecular mechanism of SESLA. This study demonstrated that SESLA treatment significantly reduced the levels of inflammatory cytokines stimulated by PGN, Pam3CSK4 or even MRSA in vitro, and it also reduced PGN-induced expression of MHC II and co-stimulatory molecules on the surface of DCs. Mechanistically, the inhibition of IκBα phosphorylation and the suppression of T cells activation could account for its anti-inflammatory activity. The present study validated the notable anti-inflammatory activity of SESLA and discovered its previously uncharacterized immunoregulatory role and the underlying mechanism in G+ bacterial infections. Overall, SESLA has a potential to be an antibiotic adjuvant for the treatment of G+ bacterial infections. [ABSTRACT FROM AUTHOR]

Optic disc and cup segmentation play an essential step towards automatic retinal diagnose system. The task is very challenging since the boundary between optic disc and cup is weak and the existing segmentation network with cross-entropy loss is hard to inject domain-specific knowledge. To solve the problem, we propose a level set based deep learning method for optic disc and cup segmentation. Particularly, we treat the output of the neural network as a level set and add several constraints to make the predicted level set satisfy some characteristics, such as the length constraint and region constraint. The length term lets the boundary tend to smooth while the region term lets the response inside the predicted area tend to be the same. The region term considers the relationship between pixels inside optic disc or cup while the cross-entropy loss treats the segmentation as a pixel-wise classification without considering the relationship between pixels. We conduct extensive experiments on several datasets including ORIGA and REFUGE and DRISHTI-GS dataset. The experiment results verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this article, the advanced angle control (AAC) for the active rectifier (AR) is proposed to improve the output power of doubly salient electromagnetic generator systems. The AAC AR is an optimization by turning on the switches in advance with an angle based on the angular position control (APC) and pulsewidth modulation (PWM) ARs. The operation principle of the AAC in ARs is different from that in the inverters. The phase inductance variation is analyzed. The inductance flat top at load condition is utilized to accelerate the commutation process and enlarge the phase current. The unified analysis is proposed by summarizing the four states of the conduction modes in the AR. The analysis of the proposed AAC strategies, the conventional APC, and PWM strategies are unified. The output power of AAC APC ARs is significantly improved compared with that of the conventional AR. The copper loss, iron loss, and torque ripple are reduced. The AAC strategies are compared with conventional strategies. The optimized performances of AAC strategies are verified by finite-element analysis and experimental results. [ABSTRACT FROM AUTHOR]

A doubly salient electromagnetic starter/ generator (DSESG) system based on the bidirectional converter (BDC) is proposed, in this article. The BDC is operated as an inverter in starting mode and as an active rectifier (AR) in generation mode. Compared to the conventional DSESG system, which is based on an inverter and a diode rectifier or a discrete inverter/rectifier unit (IRU), the configuration is simplified and the weight is reduced. Since the output voltage is improved by the angular position control (APC), field and phase currents are redistributed. The copper loss optimization control (CLOC) for the AR is proposed. The copper loss is analyzed based on the extreme learning machine (ELM) and the turn-off angles are selected. The copper loss is minimized and the heat is reduced. The copper loss and heat generation are also balanced in the field and phase windings to decrease the temperature of the field windings at low speed. Experimental verifications are conducted on a 9 kW DSESG prototype. The effectiveness of the proposed ELM-based CLOC is verified by simulation and experiments. A lighter and simpler DSESG system with reduced loss is presented. [ABSTRACT FROM AUTHOR]

A DC-link voltage regulation method is proposed based on capacitor energy control (CEC) for the doubly salient electro-magnetic generator (DSEG) system. Instead of tracking the DC-link voltage, the stored energy in the DC-link capacitor is set as the control target in the outer loop. A proportional component that converts the command output current into the command field current is developed between the capacitor energy loop and field current loop. The coefficient in the proportional component is investigated based on the mathematical model of the 12–8 pole DSEG, considering the load and speed variation. The dynamic response of the DSEG system is accelerated and the DC-link voltage variation is reduced with the proposed method. Furthermore, the adaptability to speed variation of the controlled is optimized. Since the DC-link capacitance is an additional parameter, the influences of capacitor degradation are investigated. The conventional voltage control and the CEC are implemented and compared on a DSEG platform. The optimized performance of the proposed method is verified by both finite element analysis (FEA) and experiments. The proposed method provides optimized dynamic performance over the entire speed range, enabling the DSEG system as a competitive option in the aircraft application. [ABSTRACT FROM AUTHOR]

Due to its high power density, the fractional slot concentrated winding permanent magnet synchronous machine (PMSM) is a competitive candidate for the aircraft starter generator (SG) system. However, the short circuit (SC) fault, especially the turn-to-turn SC fault, is the obstruct crux in aviation applications. The turn-to-turn SC current can be restricted to a permissible level with higher reactance and the current injection mitigation method. However, higher self-inductance always leads to lower power density. So far, there have been no numerical criteria for the tradeoff between the two characteristics in machine design. In this article, an analytical model based on flux distribution is used to estimate the SC current. The effectiveness of the d-axis current injection mitigation method is numerically analyzed in the series and parallel connected windings individually. The requirement for PMSMs to survive the turn-to-turn SC fault is derived. The mathematical analysis is verified by finite-element analysis and experiments on a 12-slot, 10-pole PMSM. This article provides theoretical support for designing high-performance permanent magnet starter generator (PMSG) with SC current mitigation ability for the aviation power system. [ABSTRACT FROM AUTHOR]

The doubly salient electromagnetic generator (DSEG) is gaining increasing attention in aircraft high voltage dc generator application due to its simplicity and de-excitation ability. However, the field winding inductance and the dc-link capacitor result in a larger electrical time constant. The dynamic response is decelerated, leading to difficulties in meeting aircraft electrical power standards. Minimum transient point tracking (MTPT) strategy based on the trajectory with minimum field current variation is proposed for the DSEG system with an angular position control (APC) controlled rectifier. Since the dc load current depends on both field current and the turn-off angle, the change in the field current is minimized and the dc load current is varied by controlling the turn-off angle. The trajectory of the optimized turn-off angle is studied to partly or fully fill the gap between the field currents at different loads. By tracking the MTPT trajectory, the change of the field current is decreased during the sudden load changes. The transient process is accelerated and the voltage variation is suppressed. In most operations, the voltage variation can be even neglected under both loaded and unloaded conditions. A 9-kW DSEG system based on controlled rectifier is implemented. The MTPT strategy is verified by experiments. The performances of the diode rectifier, controlled rectifier with fixed turn-off angle, and controlled rectifier with MTPT are compared. The dynamic performance is improved by the proposed method, making the DSEG more competitive in aircraft applications. [ABSTRACT FROM AUTHOR]

Background: Malignant brain tumors have been a serious threat to human health worldwide. This study aims to investigate the role of miR-136-3p in glioma development. Methods: Hematoxylin-eosin staining (H&E) staining was used to determine the pathologic alterations of glioma tissues. Quantitative real-time PCR (qRT-PCR) analysis and GEO2R analysis was performed to examine the expression of miRNAs and genes. Western blot was applied to detect the protein expression. Cell counting kit-8 (CCK-8) and colony formation were used to analyze the glioma cell growth. Trans-well assay was used to determine the cell migration. Annexin V-FITC/PI staining was conducted to determine the cell apoptosis of transfected glioma cells. The dual-luciferase reporter assay was carried out to confirm the binding sites of miR-136-3p on 3′ untranslated regions (3′ UTR) of Kruppel-like factor 7 (KLF7). Tumor-bearing experiment in nude mice was performed to comprehensively investigate the role of miR-136-3p/KLF7 axis in gliomas. Results: Firstly, the results showed that miR-136-3p was decreased in glioma tissues compared with adjacent tissues. Overexpression of miR-136-3p significantly inhibited cell growth of LN-229 and U251 by decreasing expression of Cyclin A1 and PCNA (proliferating cell nuclear antigen), and it suppressed glioma cell migration by downregulating N-cadherin and elevating E-cadherin levels, and it also promotes glioma cell apoptosis by promoting Bcl2-associated X (Bax) expression but suppressing Bcl-2 expression. Furthermore, we observed that KLF7 was a direct target of miR-136-3p, and KLF7 was negatively regulated by miR-136-3p in glioma cells. Finally, overexpression of KLF7 partly blocked miR-136-3p-induced inhibition of tumor growth in vitro and in vivo. Conclusions: Targeting miR-136-3p/KLF7 axis might be a novel manner to counter against gliomas. [ABSTRACT FROM AUTHOR]

Doubly salient electromagnetic machines (DSEMs), which are characterised with fault tolerance, low cost, high reliability, and de‐excitation ability, are gaining more and more attention in safety‐critical and hash environment applications, such as the aircraft generation systems. Nevertheless, the non‐linear and strong coupled characteristics of the flux linkage is the obstruct crux in DSEM modelling. The DSEM model is the critical part of the system model, which is the foundation of theoretical analysis, control strategy developing, and stability analysis. This study is aimed to demonstrate the feasibility of indirect flux linkage measurement method, as well as the effectiveness of the extreme learning machine (ELM)‐based flux linkage modelling method. The basic principles of the indirect measurement are analysed and the measurement processes excluding rotor‐clamping devices are proposed. The ELM is employed to high‐precision flux linkage modelling with high efficiency. A three‐phase 12/8‐pole DSEM is tested to confirm the validity of the proposed modelling method. Both finite element analysis and experimental results are presented, verifying the effectiveness of the indirect flux linkage measurement and the ELM‐based modelling method. [ABSTRACT FROM AUTHOR]

Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through anterior segment optical coherence tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a data-driven approach for automatic AS-OCT structure segmentation, measurement, and screening. Our technique first estimates initial markers in the eye through label transfer from a hand-labeled exemplar data set, whose images are collected over different patients and AS-OCT modalities. These initial markers are then refined by using a graph-based smoothing method that is guided by AS-OCT structural information. These markers facilitate segmentation of major clinical structures, which are used to recover standard clinical parameters. These parameters can be used not only to support clinicians in making anatomical assessments, but also to serve as features for detecting anterior angle closure in automatic glaucoma screening algorithms. Experiments on Visante AS-OCT and Cirrus high-definition-OCT data sets demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper presents an optimal model integration framework to robustly localize the optic cup in fundus images for glaucoma detection. This work is based on the existing superpixel classification approach and makes two major contributions. First, it addresses the issues of classification performance variations due to repeated random selection of training samples, and offers a better localization solution. Second, multiple superpixel resolutions are integrated and unified for better cup boundary adherence. Compared to the state-of-the-art intra-image learning approach, we demonstrate improvements in optic cup localization accuracy with full cup-to-disc ratio range, while incurring only minor increase in computing cost. [ABSTRACT FROM AUTHOR]

Pathological myopia is one of the leading causes of blindness worldwide. The condition is particularly prevalent in Asia. Unlike myopia, pathological myopia is accompanied by degenerative changes in the retina, which if left untreated can lead to irrecoverable vision loss. The accurate diagnosis of pathological myopia will enable timely intervention and facilitate better disease management to slow down the progression of the disease. Current methods of assessment typically consider only one type of data, such as that from retinal imaging. However, different kinds of data, including that of genetic, demographic and clinical information, may contain different and independent information, which can provide different perspectives on the visually observable, genetic or environmental mechanisms for the disease. The combination of these potentially complementary pieces of information can enhance the understanding of the disease, providing a holistic appreciation of the multiple risks factors as well as improving the detection outcomes. In this study, we propose a computer-aided diagnosis framework for Pathological Myopia diagnosis through Biomedical and Image Informatics(PM-BMII). Through the use of multiple kernel learning (MKL) methods, PM-BMII intelligently fuses heterogeneous biomedical information to improve the accuracy of disease diagnosis. Data from 2,258 subjects of a population-based study, in which demographic and clinical information, retinal fundus imaging data and genotyping data were collected, are used to evaluate the proposed framework. The experimental results show that PM-BMII achieves an AUC of 0.888, outperforming the detection results from the use of demographic and clinical information 0.607 (increase , ), genotyping data 0.774 (increase , ) or imaging data 0.852 (increase , ) alone. The accuracy of the results obtained demonstrates the feasibility of using heterogeneous data for improved disease diagnosis through our proposed PM-BMII framework. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of detecting sudden pedestrian crossings to assist drivers in avoiding accidents. This application has two major requirements: to detect crossing pedestrians as early as possible just as they enter the view of the car-mounted camera and to maintain a false alarm rate as low as possible for practical purposes. Although many current sliding-window-based approaches using various features and classification algorithms have been proposed for image-/video-based pedestrian detection, their performance in terms of accuracy and processing speed falls far short of practical application requirements. To address this problem, we propose a three-level coarse-to-fine video-based framework that detects partially visible pedestrians just as they enter the camera view, with low false alarm rate and high speed. The framework is tested on a new collection of high-resolution videos captured from a moving vehicle and yields a performance better than that of state-of-the-art pedestrian detection while running at a frame rate of 55 fps. [ABSTRACT FROM AUTHOR]

Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast. [ABSTRACT FROM AUTHOR]

In the stamping industry, there are certain needs to develop formability technology to process a great amount of (raw) mechanics data obtained from both experiment and mechanics calculation, especially in automotive body manufacturing. Universal formability technology (UFT), based on mathematical plasticity theories, has been developed to meet the needs. UFT includes six sections: (a) forming mode theory; (b) bending process models; (c) universal formability theory; (d) stamping process window; (e) control principles of metal flow tendency; (f) stamping similarity theory. Using UFT, relationships between/among the severities of six types of defects and seven types of stamping variables can be established quantitatively. This has been applied successfully for: (a) product feasibility analysis; (b) material selection; (c) strain-level draw die development; (d) post analysis of finite element analysis results; (e) a stamping engineering expert system; (f) production control. [Copyright &y& Elsevier]

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets. [ABSTRACT FROM AUTHOR]

Cardiac ischemia, hypoxia, arrhythmias, and heart failure share the common electrophysiological changes featured by the elevation of intracellular Ca2+ (Ca2+ overload) and inhibition of the inward rectifier potassium (IK1) channel. IK1 channel agonists have been considered a new type of anti‐arrhythmia and cardioprotective agents. We predicted using a drug repurposing strategy that tetramisole (Tet), a known anthelminthic agent, was a new IK1 channel agonist. The present study aimed to experimentally identify the above prediction and further demonstrate that Tet has cardioprotective effects. Results of the whole‐cell patch clamp technique showed that Tet at 1–100 μmol/L enhanced IK1 current, hyperpolarized resting potential (RP), and shortened action potential duration (APD) in isolated rat cardiomyocytes, while without effects on other ion channels or transporters. In adult Sprague–Dawley (SD) rats in vivo, Tet showed anti‐arrhythmia and anticardiac remodeling effects, respectively, in the coronary ligation‐induced myocardial infarction model and isoproterenol (Iso, i.p., 3 mg/kg/day, 10 days) infusion‐induced cardiac remodeling model. Tet also showed anticardiomyocyte remodeling effect in Iso (1 μmol/L) infused adult rat ventricular myocytes or cultured H9c2 (2‐1) cardiomyocytes. Tet at 0.54 mg/kg in vivo or 30 μmol/L in vitro showed promising protections on acute ischemic arrhythmias, myocardial hypertrophy, and fibrosis. Molecular docking was performed and identified the selective binding of Tet with Kir2.1. The cardioprotection of Tet was associated with the facilitation of IK1 channel forward trafficking, deactivation of PKA signaling, and inhibition of intracellular calcium overload. Enhancing IK1 may play dual roles in anti‐arrhythmia and antiventricular remodeling mediated by restoration of Ca2+ homeostasis. [ABSTRACT FROM AUTHOR]

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles. [ABSTRACT FROM AUTHOR]

Fei-Yan-Qing-Hua decoction (FYQHD), derived from the renowned formula Ma Xing Shi Gan tang documented in Zhang Zhong Jing's "Treatise on Exogenous Febrile Disease" during the Han Dynasty, has demonstrated notable efficacy in the clinical treatment of pneumonia resulting from bacterial infection. However, its molecular mechanisms underlying the therapeutic effects remains elusive. This study aimed to investigate the protective effects of FYQHD against lipopolysaccharide (LPS) and carbapenem-resistant Klebsiella pneumoniae (CRKP)-induced sepsis in mice and to elucidate its specific mechanism of action. Sepsis models were established in mice through intraperitoneal injection of LPS or CRKP. FYQHD was administered via gavage at low and high doses. Serum cytokines, bacterial load, and pathological damage were assessed using enzyme-linked immunosorbent assay (ELISA), minimal inhibitory concentration (MIC) detection, and hematoxylin and eosin staining (H&E), respectively. In vitro , the immunoregulatory effects of FYQHD on macrophages were investigated through ELISA, MIC, quantitative real-time PCR (Q-PCR), immunofluorescence, Western blot, and a network pharmacological approach. The application of FYQHD in the treatment of LPS or CRKP-induced septic mouse models revealed significant outcomes. FYQHD increased the survival rate of mice exposed to a lethal dose of LPS to 33.3%, prevented hypothermia (with a rise of 3.58 °C), reduced pro-inflammatory variables (including TNF-α, IL-6, and MCP-1), and mitigated tissue damage in LPS or CRKP-induced septic mice. Additionally, FYQHD decreased bacterial load in CRKP-infected mice. In vitro , FYQHD suppressed the expression of inflammatory cytokines in macrophages activated by LPS or HK-CRKP. Mechanistically, FYQHD inhibited the PI3K/AKT/mTOR/4E-BP1 signaling pathway, thereby suppressing the translational level of inflammatory cytokines. Furthermore, it reduced the expression of HMGB1/RAGE, a positive feedback loop in the inflammatory response. Moreover, FYQHD was found to enhance the phagocytic activity of macrophages by upregulating the expression of phagocytic receptors such as CD169 and SR-A1. FYQHD provides protection against bacterial sepsis by concurrently inhibiting the inflammatory response and augmenting the phagocytic ability of immune cells. [Display omitted] • FYQHD protected against Gram-negative bacteria, including CRKP-induced sepsis. • FYQHD inhibited the translation of inflammatory cytokines by inhibiting the PI3K/AKT/mTOR/4E-BP1 pathway. • FYQHD inhibited the amplification of the inflammatory response by HMGB1/RAGE signaling pathway. • FYQHD promoted the phagocytosis by promoting the expression of CD169 and SR-A1. [ABSTRACT FROM AUTHOR]

Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is a prevalent critical respiratory disorder caused mostly by infection and other factors. However, effective drug therapies are currently lacking. Xuanbai Chengqi Decoction (XCD), a traditional Chinese medicine (TCM) prescription, is commonly employed to treat lung diseases. It has been recommended by Chinese health authorities as one of the TCM prescriptions for COVID-19. Nonetheless, its underlying mechanism for the treatment of ALI has not been fully understood. The study aims to investigate the therapeutic effect of XCD on lipopolysaccharide (LPS) -induced ALI in mice and explore its anti-inflammatory mechanism involving pyroptosis. Ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) was employed to identify the active compounds of XCD, and quantitative analysis of the main compounds was conducted. Male C57BL/6J mice were given different doses of XCD (4.5 and 9.0 g/kg/day) or dexamethasone (5 mg/kg/day) by oral gavage for 5 consecutive days. Subsequently, ALI was induced by injecting LPS (20 mg/kg) intraperitoneally 2 h after the last administration, and serum and lung tissues were collected 8 h later. J774A.1 cells were pretreated with different doses of XCD (100, 200, 400 μg/ml) for 12 h, then incubated with LPS (1 μg/ml) for 4 h and ATP (1 mM) for 2 h to induce pyroptosis. Supernatant and cells were collected. Moreover, J774A.1 cells were transfected with an NLRP3 overexpression plasmid for 24 h, followed by subsequent experiments with XCD (400 μg/ml). Lung histopathological changes were evaluated using hematoxylin and eosin (HE) staining. To assess the efficacy of XCD on ALI/ARDS, the levels of inflammatory factors, chemokines, and proteins associated with NLRP3 inflammasome signaling pathway were evaluated. XCD was found to ameliorate lung inflammation injury in ALI mice, and reduce the protein expression of TNF-α, IL-1β, and IL-6 in both mouse serum and J774A.1 cell supernatant. Meanwhile, XCD significantly decreased the mRNA levels of IL-1β, pro-IL-1β, CXCL1, CXCL10, TNF-α, NLRP3, NF-κB P65, and the protein expression of NLRP3, Cleaved-Caspase1, and GSDMD-N in the lung and J774A.1 cells. These effects were consistent with the NLRP3 inhibitor MCC950. Furthermore, overexpression of NLRP3 reversed the anti-inflammatory effect of XCD. The therapeutic mechanism of XCD in ALI treatment may involve alleviating inflammatory responses in lung tissues by inhibiting the activation of the NLRP3 inflammasome-mediated pyroptosis in macrophages. Schematic illustration of XCD's improvement of LPS-induced ALI in mice. LPS induces inflammation via the TLR4/NF-κB pathway, leading to the overproduction of pro-inflammatory cytokines (such as TNF-α, IL-1β, and IL-6) and promoting pro-IL-1β and NLRP3 expressions. Upon stimulation, NLRP3 protein recruits ASC and assembles Pro-Caspase1 into NLRP3 inflammasomes. Cleaved-Caspase1 cleaves pro-IL-1β into mature IL-1β. Additionally, GSDMD is cleaved into GSDMD-N by Cleaved-Caspase1, producing holes in the cell membrane and causing the release of inflammatory factors and the activation of pyroptosis, ultimately resulting in tissue damage. XCD reduces the release of inflammatory factors by inhibiting the expression of NF-κB P65 and the assembly and activation of the NLRP3 inflammasome, thereby improving the inflammatory response and providing protection against ALI. [Display omitted] • ALI/ARDS is a prevalent respiratory critical condition that lacks effective therapeutic drugs. • XCD can improve ALI inflammation. • The chemical composition and main active components of XCD were analyzed by UPLC-MS/MS. • The anti-inflammatory effect of XCD is related to NLRP3 inflammasome and pyroptosis signaling pathway. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening. [ABSTRACT FROM AUTHOR]

An immune optimization algorithm is proposed in this paper based on the immune negative selection. The algorithm NSIOA is motivated by the negative selection mechanism in biological immune recognition. Different from the existing immune optimization methods, NSIOA constantly removes the worst solutions to get the optimal solution. Considering that removal of poor members of a population might lead to the loss of design information that may actually help identify better solutions in the search space, the proposed NSIOA is designed to keep the diversity of antibodies while removing poor members, therefore the algorithm will converge to global optimal solution with high probability. The convergence property and the complexity of the algorithm have also been analyzed. To illustrate the efficiency of the algorithm is used in solving the travel salesman problem. The theoretical analysis and experimental results show that the algorithm is of a strong potential in solving practical problems. [Copyright &y& Elsevier]

Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algorithms have been developed to promote fundus observation on degraded images, high data demands and limited applicability hinder their clinical deployment. To circumvent this bottleneck, a generic fundus image enhancement network (GFE-Net) is developed in this study to robustly correct unknown fundus images without supervised or extra data. Levering image frequency information, self-supervised representation learning is conducted to learn robust structure-aware representations from degraded images. Then with a seamless architecture that couples representation learning and image enhancement, GFE-Net can accurately correct fundus images and meanwhile preserve retinal structures. Comprehensive experiments are implemented to demonstrate the effectiveness and advantages of GFE-Net. Compared with state-of-the-art algorithms, GFE-Net achieves superior performance in data dependency, enhancement performance, deployment efficiency, and scale generalizability. Follow-up fundus image analysis is also facilitated by GFE-Net, whose modules are respectively verified to be effective for image enhancement. [Display omitted] • A generic network (GFE-Net) is proposed for fundus image enhancement. • Robust features are captured by self-supervised learning using frequency information. • Representation learning and image enhancement are coupled to boost the deployments. • Experiments validate the generalizability to unknown degradations in various data. • GFE-Net excels in comparison of image enhancement, efficiency, and generalizability. [ABSTRACT FROM AUTHOR]

Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifier. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labeled database and a large unlabeled database. Various studies have shown that glaucoma can be monitored by analyzing the optic disc and its surroundings, and for that reason, the images used in this paper were automatically cropped around the optic disc. The novelty of this paper is to propose a new retinal image synthesizer and a semi-supervised learning method for glaucoma assessment based on the deep convolutional GANs. In addition, and to the best of our knowledge, this system is trained on an unprecedented number of publicly available images (86926 images). This system, hence, is not only able to generate images synthetically but to provide labels automatically. Synthetic images were qualitatively evaluated using t-SNE plots of features associated with the images and their anatomical consistency was estimated by measuring the proportion of pixels corresponding to the anatomical structures around the optic disc. The resulting image synthesizer is able to generate realistic (cropped) retinal images, and subsequently, the glaucoma classifier is able to classify them into glaucomatous and normal with high accuracy (AUC = 0.9017). The obtained retinal image synthesizer and the glaucoma classifier could then be used to generate an unlimited number of cropped retinal images with glaucoma labels. [ABSTRACT FROM AUTHOR]

Most electric vehicles adopt cooperative braking systems that can blend friction braking torque with regenerative braking torque to achieve higher energy efficiency while maintaining a certain braking performance and driving safety. This paper presented a new cooperative regenerative braking system that contained a fully-decoupled hydraulic braking mechanism based on a modified electric stability control system. The pressure control algorithm and brake force distribution strategy were also discussed. Dynamic models of a front wheel drive electric car equipped with this system and a simulation platform with a driver model and driving cycles were established. Tests to evaluate the braking performance and energy regeneration were simulated and analyzed on this platform and the simulation results showed the feasibility and effectiveness of this system. [ABSTRACT FROM AUTHOR]

• Transform segmentation task from classification to regression by making models learn a level set function. • Design a shape-based loss with a convexity constraint term. • Improve segmentation performance at the boundary with weak contrast. The lens is one of the important refractive media in the eyeball. Abnormality of the nucleus or cortex in the lens can lead to ocular disorders such as cataracts and presbyopia. To achieve an accurate diagnosis, segmentation of these ocular structures from anterior segment optical coherence tomography (AS-OCT) is essential. However, weak-contrast boundaries of the object in the images present a challenge for accurate segmentation. The state-of-the-art (SOTA) methods, such as U-Net, treat segmentation as a binary classification of pixels, which cannot handle pixels on weak-contrast boundaries well. In this paper, we propose to incorporate shape prior into a deep learning framework for accurate nucleus and cortex segmentation. Specifically, we propose to learn a level set function, whose zero-level set represents the object boundary, through a convolutional neural network. Moreover, we design a novel shape-based loss function, where the shape prior knowledge can be naturally embedded into the learning procedure, leading to improvement in performance. We collect a high-quality AS-OCT image dataset with precise annotations to train our model. Abundant experiments are conducted to verify the effectiveness of the proposed framework and the novel shape-based loss. The mean Intersection over Unions (MIoUs) of the proposed method for lens nucleus and cortex segmentation are 0.946 and 0.957, and the mean Euclidean Distance (MED) measure, which can reflect the accuracy of the segmentation boundary, are 6.746 and 2.045 pixels. In addition, the proposed shape-based loss improves the SOTA models on the nucleus and cortex segmentation tasks by an average of 0.0156 and 0.0078 in the MIoU metric and 1.394 and 0.134 pixels in the MED metric. We transform the segmentation from a classification task to a regression task by making the model learn the level set function, and embed shape information in deep learning by designing loss functions. This allows the proposed method to be more efficient in the segmentation of the object with weak-contrast boundaries. We propose to incorporate shape priors into a deep learning framework for accurate nucleus and cortex segmentation from AS-OCT images. Specifically, we propose to learn a level set function, where the zero-level set represents the boundary of the target. Meanwhile, we design a novel shape-based loss function in which additional convex shape prior can be embedded in the learning process, leading to an improvement in performance. The IOUs for nucleus and cortex segmentation are 0.946 and 0.957, while the MED that reflects the accuracy of the boundary are 6.746 and 2.045 pixels. The proposed shape-based loss improves the SOTA model for nucleus and cortex segmentation by an average of 0.0156 and 0.0078 in IOU, and 1.394 and 0.134 pixels in MED. We transform segmentation from classification to regression by making the model learn a level set function, resulting in improved performance at the boundary with weak contrast. [ABSTRACT FROM AUTHOR]

• We design a MCI-graph framework which integrates both non-image information and image information. We use LWCC to extract the feature to avoid the high dimensional features. • We devise various parallel GCN layers using multiple inputs from the random walk embedding theory, which is able to identify the essential MCI information from the GCN graph embedding. • The random walk embeddings on the graph can explore the high order similarity of features. • We fuse the information from the functional networks and structural networks using a MSE-GCN model to improve prediction performance. As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be detected by analyzing the brain connectivity networks. For this reason, we devise a new framework via multi-scale enhanced graph convolutional network (MSE-GCN) for MCI detection, which integrates the structural and functional information from the diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (R-fMRI), respectively. Specifically, both information in the brain connective networks is first integrated based on the local weighted clustering coefficients (LWCC), which is concatenated as the feature vector for representing a population graph's vertice. Simultaneously, the gender and age information in each subject are integrated with the structural and functional features to construct a sparse graph. Then, various parallel graph convolutional network (GCN) layers with multiple inputs are designed from the embedding from random walk embeddings in the GCN to identify the essential MCI graph information. Finally, all GCN layers' outputs are concatenated via the fully connection layer to perform disease detection. The experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method is promising to detect MCI and superior to other competing algorithms, with a mean classification accuracy of 90.39% in the detection tasks. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an incurable neurodegenerative disease. Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer's disease (AD), with approximately 80% of amnestic MCI patients developing AD within 6 years. MCI can be further categorized into two stages (i.e., early MCI (EMCI) and late MCI (LMCI)). To identify EMCI effectively and understand how it changes brain function, the brain functional connectivity network (BFCN) has been widely used. However, the conventional methods mainly focused on detection from a single time-point data, which could not discover the changes during the disease progression without using multi-time points data. Therefore, in this work, we carry out a longitudinal study based on multi-time points data to detect EMCI and validate them on two public datasets. Specifically, we first construct a similarity-constrained group network (SGN) from the resting state functional magnetic resonance imaging (rs-fMRI) data at different time-points, and then use a stacked bidirectional long short term memory (SBi-LSTM) network to extract features for longitudinal analysis. Also, we use a self-attention mechanism to leverage high-level features to further improve the detection accuracy. Evaluated on the public Alzheimer's Disease Neuroimaging Initiative Phase II and III (ADNI-2 and ADNI-3) databases, the proposed method outperforms several state-of-the-art methods. • We devise a similarity-constrained group sparse network (SGN) for multi-center brain functional connectivity network (BFCN) construction, which can learn the similarity among features and reduce the useless features simultaneously. • We design a SBi-LSTM framework to utilize the longitudinal information for EMCI detection. • We explore a self-attention mechanism to find the most discriminative features to improve the detection performance. [ABSTRACT FROM AUTHOR]

Growing evidence indicates that silibinin (SLB), a main component extracted from Chinese herb Silybum marianum , can effectively antagonize doxorubicin (DOX) induced myocardial injury (DIMI), but the specific molecular mechanism is still unelucidated. Herein, DOX induced human AC16 cardiomyocyte injury model and Network Pharmacology are used to predict and verify the potential mechanism. The analysis results of the core PPI network of SLB against DIMI show that JAK/STAT signaling pathway and autophagy are significantly enriched. Molecular docking results indicate that SLB has stronger binding ability to signaling key proteins IL6ST, JAK2 and STAT3 (affinity ≤ −7.0 kcal/mol). The detection results of pathway activation and autophagy level demonstrate that SLB significantly alleviates DOX induced IL6ST/JAK2/STAT3 signaling pathway inhibition and autophagy inhibition, reduces the death rate of cardiomyocytes. This protective effect of SLB is eliminated when key pathway proteins (IL6ST, JAK2, STAT3) are knocked down or autophagy is inhibited (3-MA or Beclin1 knockdown). These results suggest that the regulation of IL6ST/JAK2/STAT3 signaling pathway and autophagy may be important mechanism for SLB's protective effect on DOX injured cardiomyocytes. Further experimental results prove that knockdown of IL6ST, JAK2 and STAT3 eliminate the mitochondrial ROS scavenging effect and autophagy promoting effect of SLB. In sum, SLB can decrease the mitochondrial ROS and restore autophagy to antagonize DOX-induced cardiomyocyte injury by activating IL6ST/JAK2/STAT3 signaling pathway. [Display omitted] [ABSTRACT FROM AUTHOR]

In traditional Chinese medicine (TCM), Zuogui Wan (ZGW) is a classical prescription for senile disorders and delay aging. Modern studies show that ZGW promotes central nerve cell regeneration, prevents and cures osteoporosis, enhances the body's antioxidant capacity, regulates the body's immune function, and promotes mesenchymal stem cells (MSCs) proliferation. Aim of the study: It has been shown that MSCs aging is closely associated with organism's aging and age-related disorders. The study aimed to define the effects of ZGW on the aging bone marrow mesenchymal stem cells (BMSCs) and to identify the mechanisms of ZGW delaying BMSCs senescence. Materials and methods: Network pharmacology analysis combined with GEO data mining, molecular docking and experimental validation were used to evaluate the mechanisms by which ZGW delays MSCs senescence (MSCS). LC-MS was used for quality control analysis of ZGW. Results: PPI network analysis revealed that EGF, TNF, JUN, MMPs, IL-6, MAPK8, and MYC are components of the core PPI network. GO and KEGG analyses revealed that oxidative stress, regulation of response to DNA damage stimuli, and Wnt signaling were significantly enriched. GEO database validation also indicated that Wnt signaling closely correlated with MSCs aging. Molecular docking analysis of the top-13 active components in the "ZGW-Targets-MSCS" network indicated that most components have strong affinity for key proteins in Wnt signaling, suggesting that modulation of Wnt signaling is an important mechanism of ZGW activity against MSCS. Further experimental validation found that ZGW indeed regulates Wnt signaling and suppresses the expression of age-related factors to enhance cell proliferation, ameliorate DNA damage, and reduce senescence-related secretory phenotype (SASP) secretion, thereby maintaining multidirectional differentiation of rat BMSCs. Similar results were obtained using the Wnt inhibitor, XAV-939. Conclusions: Together, our data show that ZGW slows BMSCs aging by suppressing Wnt signaling. [Display omitted] • ZGW can delay the aging of BMSCs. • Network pharmacology was used to analyze the mechanisms of ZGW delaying MSCs aging. • GEO database validation indicated that Wnt signaling influenced MSCs aging. • ZGW may slow BMSCs senescence by suppressing Wnt/β-catenin signaling. [ABSTRACT FROM AUTHOR]

• We develop an RIR block by incorporating the prior clinical knowledge, which can construct the dependencies among channels and adaptively recalibrate the relative importance of feature map region information, like top-half and bottom-half regions. • We construct an RIR-Net to accurately discriminate opacity information for classifying NC into three severity levels, built on multiple Residual-RIR modules to emphasize meaningful regional feature representations and alleviate the gradient vanishing problem. The results show that our RIR-Net outperforms strong baselines and previous state-of-the-art methods on a clinical AS-OCT dataset and the other two OCT datasets. We also demonstrate that the uneven opacity distributions in top half- and bottom half- regions affect NC classification results on AS-OCT images by using CNNs experimentally, which is consistent with clinical research. • This paper not only uses the CAM technique to localize what and where RIR-Net focuses on but also analyzes attention weights of the RIR block in-depth, examining the validity of our method in adjusting the relative importance of feature map regions. [Display omitted] Nuclear cataract (NC) is a leading eye disease for blindness and vision impairment globally. Accurate and objective NC grading/classification is essential for clinically early intervention and cataract surgery planning. Anterior segment optical coherence tomography (AS-OCT) images are capable of capturing the nucleus region clearly and measuring the opacity of NC quantitatively. Recently, clinical research has suggested that the opacity correlation and repeatability between NC severity levels and the average nucleus density on AS-OCT images is high with the interclass and intraclass analysis. Moreover, clinical research has suggested that opacity distribution is uneven on the nucleus region, indicating that the opacities from different nucleus regions may play different roles in NC diagnosis. Motivated by the clinical priors, this paper proposes a simple yet effective region-based integration-and-recalibration attention (RIR), which integrates multiple feature map region representations and recalibrates the weights of each region via softmax attention adaptively. This region recalibration strategy enables the network to focus on high contribution region representations and suppress less useful ones. We combine the RIR block with the residual block to form a Residual-RIR module, and then a sequence of Residual-RIR modules are stacked to a deep network named region-based integration-and-recalibration network (RIR-Net), to predict NC severity levels automatically. The experiments on a clinical AS-OCT image dataset and two OCT datasets demonstrate that our method outperforms strong baselines and previous state-of-the-art methods. Furthermore, attention weight visualization analysis and ablation studies verify the capability of our RIR-Net for adjusting the relative importance of different regions in feature maps dynamically, agreeing with the clinical research. [ABSTRACT FROM AUTHOR]

• This work is the first attempt to classify anterior chamber angles into open, appositional- and synechial- angle-closure, by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. • We introduced a Multi-Sequence Deep Network (MSDN), which learns to identify discriminative representations from a sequence of AS-OCT images, especially with a view to improving performance in separating appositional-angle from its occludable angle forms. • We have constructed a AS-OCT dataset for which the AS-OCT of each eye were acquired under both dark and bright illumination conditions. We take advantage of the resulting changes in pupil size to simulate the pressure of the goniolens, which can push the angle open and help determine the true angle configuration. Precise characterization and analysis of anterior chamber angle (ACA) are of great importance in facilitating clinical examination and diagnosis of angle-closure disease. Currently, the gold standard for diagnostic angle assessment is observation of ACA by gonioscopy. However, gonioscopy requires direct contact between the gonioscope and patients' eye, which is uncomfortable for patients and may deform the ACA, leading to false results. To this end, in this paper, we explore a potential way for grading ACAs into open-, appositional- and synechial angles by Anterior Segment Optical Coherence Tomography (AS-OCT), rather than the conventional gonioscopic examination. The proposed classification schema can be beneficial to clinicians who seek to better understand the progression of the spectrum of angle-closure disease types, so as to further assist the assessment and required treatment at different stages of angle-closure disease. To be more specific, we first use an image alignment method to generate sequences of AS-OCT images. The ACA region is then localized automatically by segmenting an important biomarker - the iris - as this is a primary structural cue in identifying angle-closure disease. Finally, the AS-OCT images acquired in both dark and bright illumination conditions are fed into our Multi-Sequence Deep Network (MSDN) architecture, in which a convolutional neural network (CNN) module is applied to extract feature representations, and a novel ConvLSTM-TC module is employed to study the spatial state of these representations. In addition, a novel time-weighted cross-entropy loss (TC) is proposed to optimize the output of the ConvLSTM, and the extracted features are further aggregated for the purposes of classification. The proposed method is evaluated across 66 eyes, which include 1584 AS-OCT sequences, and a total of 16,896 images. The experimental results show that the proposed method outperforms existing state-of-the-art methods in applicability, effectiveness, and accuracy. [ABSTRACT FROM AUTHOR]

• We propose a novel generative adversarial network for skin lesion segmentation. • We construct a UNet-SCDC module as a generator model to produce segmentation masks. • A dual discrimination strategy is proposed to improve the recognition. Skin lesion segmentation from dermoscopy images is a fundamental yet challenging task in the computer-aided skin diagnosis system due to the large variations in terms of their views and scales of lesion areas. We propose a novel and effective generative adversarial network (GAN) to meet these challenges. Specifically, this network architecture integrates two modules: a skip connection and dense convolution U-Net (UNet-SCDC) based segmentation module and a dual discrimination (DD) module. While the UNet-SCDC module uses dense dilated convolution blocks to generate a deep representation that preserves fine-grained information, the DD module makes use of two discriminators to jointly decide whether the input of the discriminators is real or fake. While one discriminator, with a traditional adversarial loss, focuses on the differences at the boundaries of the generated segmentation masks and the ground truths, the other examines the contextual environment of target object in the original image using a conditional discriminative loss. We integrate these two modules and train the proposed GAN in an end-to-end manner. The proposed GAN is evaluated on the public International Skin Imaging Collaboration (ISIC) Skin Lesion Challenge Datasets of 2017 and 2018. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods. Image, graphical abstract [ABSTRACT FROM AUTHOR]

• A network estimation method can automatically calibrate data quality and integrate modula prior. • A multi-task feature learning method is developed using multi-modal data. • A joint non-convex regularizer is designed for subspace learning. • Our method has achieved good automatic diagnosis and classification performance. Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Radiation-induced abscopal effect (RIAE) may influence radiotherapy efficiency. However, it is unknown whether RIAE triggers abnormal genetic consequence. We present a novel evidence that, when mice were given fractionated irradiation on right thorax, the ultrastructure of blood-testis barrier was damaged in company with apoptosis induction in testes, and the sperm number and vitality were drastically decreased so that both the fertility and the survival of their offspring were reduced. Protein microarray assay and hormone detection showed that some cytokines especially TNF-α, TGF-β and estradiol in the serum of irradiated mice increased to higher levels in consistent with abscopal damage, and this conditioned serum had toxic effect on TM4 cells in vitro. When the mice were fed with cimetidine, the above abscopal responses were significantly attenuated. This study demonstrates in the first time that the thoracic irradiation (Th-IR) induces structural and functional damage in the distal testes and further cause fertility decline of irradiated male mice, which may have important implications in the strategy development of radiotherapy in avoiding abnormal genetic consequence. [ABSTRACT FROM AUTHOR]