A simulation-based network analysis of intervention targets for adolescent depressive and anxiety symptoms.

Although previous research has well explored central and bridge symptoms of mental health problems, little examined whether these symptoms can serve as effective targets for intervention practices. Based on the Ising model, this study constructed a network structure of depressive and anxiety symptoms. The NodeIdentifyR algorithm (NIRA) was used to simulate interventions within this network, examining the effects of alleviating or aggravating specific symptoms on the network's sum scores. In this study, a total of 15,569 participants were recruited from China (50.87 % females, Mage = 13.44; SD = 0.97). The Ising model demonstrated that "sad mood" had the highest expected influence, and "irritability" had the highest bridge expected influence. Alleviating interventions suggested that decreasing the symptom value of "nervousness" resulted in the greatest projected reduction in network symptom activation, which may be a potential target symptom for treatment. Aggravating interventions indicated that elevating the symptom value of "sad mood" had the most projected increase in network activation, which may be a potential target for prevention. Additionally, network structure indices (e.g., central or bridge symptoms) need to be interpreted with more caution as intervention targets, since they may not be exactly the same. These findings enriched the comprehension of the depressive and anxiety network in Chinese adolescents, offering valuable insights for designing effective interventions.

A Thorough Examination of the Variables Affecting the Quantum Efficiency of Radiative Decay of Trichlorotriphenylmethyl Radicals.

Fluorescence quantum efficiency is determined by the competition between radiation and nonradiation processes of the excited states. Understanding the factors affecting the radiation and nonradiative decay rates is of great significance for the design of luminescent materials. The excitation state deactivation mechanisms of singlet and triplet states have been extensively studied, providing a comprehensive understanding of the processes involved in the relaxation of these states. However, research on free radical systems involving doublet states is relatively scarce. Therefore, in this study, radiation and nonradiative decay rates and the mechanism of a series of trichlorotriphenylmethyl-based radicals were investigated theoretically. The results indicate that the relative rotations of electron donor and acceptor, as well as the internal rotations of trichlorotriphenylmethyl moiety, play important roles in energy dissipation through nonradiative channels. The effect of a solid-state environment on the radiation and nonradiative decay rates of radicals was investigated using a combination of quantum mechanics and molecular mechanics methods. The results indicate that the solid-state environment restricts the expansion of the conjugated system in the excited state of radicals, leading to a slight decrease in radiative decay rate. In addition, the solid-state environment reduces the reorganization energy and also affects the adiabatic excitation energy of radicals. The reduction in reorganization energy results in a decrease in nonradiative rate, while the opposite effect is observed for adiabatic excitation energy. The nonradiative rate of radicals in a solid-state environment is thus inflected by a combination of molecular geometric structure relaxation and ground-excited state energy gap.

A bibliometric analysis of research on organizational resilience.

Organizational resilience is a key concept in the study of sustainable corporate growth and indicates an organization's capacity to recover from adversity. It plays a crucial role in responding to uncertain crises. In recent years, academic interest in organizational resilience has increasingly gained prominence. This research uses CiteSpace and VOSviewer to provide a thorough visual analysis of pertinent international literature based on 342 pieces of closely linked literature about organizational resilience. The findings suggest that organizational resilience research is currently experiencing a development phase. Within this field, there is a substantial number of scholars involved, with the most prolific among them including Aleksic Aleksandar, Prayag Girish, and Griffiths Andrew. The networks of collaboration among these authors, nevertheless, are very scattered. Co-citation network research reveals the academics with the biggest sway in the field. Organizational resilience, conservation of resources theory, crisis management, corporate social responsibility, and emergency management are identified as research hotspots within the keyword co-citation network. Furthermore, to determine which countries and regions are the most influential, this study has created a cooperative network among them. China, the United States, and England are the top three nations with articles published. Not only are the highly cited journals respected in the management sector, but they also showcase noteworthy research accomplishments within the field. The purpose of this study is to investigate potential avenues for future research and offer helpful sources for choosing research subjects and developing theoretical frameworks in this area. The analysis is highly valuable as a reference for research on organizational resilience in different settings in the future.

Ruptured liquid metal microcapsules enabling hybridized silver nanowire networks towards high-performance deformable transparent conductors.

Extensive studies have been carried out on silver nanowires (AgNWs) in view of their impressive conductivity and highly flexible one-dimensional structure. They are seen as a promising choice for producing deformable transparent conductors. Nonetheless, the widespread adoption of AgNW-based transparent conductors is hindered by critical challenges represented by the significant contact resistance at the nanowire junctions and inadequate interfacial adhesion between the nanowires and the substrate. This study presents a novel solution to tackle the aforementioned challenges by capitalizing on liquid metal microcapsules (LMMs). Upon exposure to acid vapor, the encapsulated LMMs rupture, releasing the fluid LM which then forms a metallic overlay and hybridizes with the underlying Ag network. As a result, a transparent conductive film with greatly enhanced electrical and mechanical properties was obtained. The transparent conductor displays negligible resistance variation even after undergoing chemical stability, adhesion, and bending tests, and ultrasonic treatment. This indicates its outstanding adhesion strength to the substrate and mechanical flexibility. The exceptional electrical properties and robust mechanical stability of the transparent conductor position it as an ideal choice for direct integration into flexible touch panels and wearable strain sensors, as evidenced in this study. By resolving the critical challenges in this field, the proposed strategy establishes a compelling roadmap to navigate the development of high-performance AgNW-based transparent conductors, setting a solid foundation for further advancement in the field of deformable electronics.

Solvent-Promoted Catalyst-Free Recycling of Waste Polyester and Polycarbonate Materials.

Polymeric materials are indispensable in our daily lives. However, the generation of vast amounts of waste polymers poses significant environmental and ecological challenges. Instead of resorting to landfilling or incineration, strategies for polymer recycling offer a promising approach to mitigate environmental pollution. Pioneering studies have demonstrated the alcoholysis of waste polyesters and polycarbonates; however, these processes typically require the use of catalysts. Moreover, the development of strategies for catalyst removal and recycling is crucial, particularly in some industrial applications. In contrast, we present a catalyst-free method for the alcoholysis of common polyester and polycarbonate materials into small organic molecules. Certain polar organic solvents exhibit remarkable efficiency in polymer degradation under catalyst-free conditions. Employing these polar solvents, both polymer resins and commercially available products could be effectively degraded via alcoholysis. Our design contributes a straightforward route for recycling waste polymeric materials.

Structural analysis of global mineral governance system from the perspective of country.

The uneven spatial distribution of mineral resources necessitates the construction of mature global mineral governance system to ensure rational allocation of mineral resources. To this end, it is essential to analyze the structure of the global mineral governance system to provide a theoretical basis for the construction. The governance actor is the crucial element in driving the governance process, and country serves as the most central actor. Therefore, clarifying the roles and statuses of different countries in the governance system will be helpful to analyze the structure of the governance system. In the context of advancing globalization, a complex cooperative relationship has been presented between countries based on international organizations. Thus, we establish a national cooperation network based on the principle of the co-existence of countries in international organizations, to quantify these relationships between countries and identify the role and status of different countries, as well as the country communities in the cooperation network, by combining the characteristics of the countries in the network with the actual performance in the organizations. The research findings are as follow: (1) The UK, Germany, France, Sweden, and Canada play pivotal roles in promoting international cooperation as well as leading governance in the governance system. (2) Emerging economies are more actively engaged in these organizations and can promote international cooperation, but lack the capacity to assume leadership roles in governance. (3) The U.S. and China have a stronger ability to lead than to cooperate in the governance system. (4) Most African and South American countries, as well as some European nations, are marginalized in the governance system. (5) Countries with the same needs and similar economic and political conditions belong to the same community: The European countries and the U.S., consumer with a high level of economic and political development but low mineral resource endowment are in the first community. Canada, Australia, and certain African and South American countries, producers with high mineral resource endowments are in the second community. Most African and South American countries with lower levels of economic and political development are in the third group.

Targeting Cyclophilin A and CD147 to Inhibit Replication of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and SARS-CoV-2-Induced Inflammation.

Identification and development of effective therapeutics for coronavirus disease 2019 (COVID-19) are still urgently needed. The CD147-spike interaction is involved in the severe acute respiratory syndrome coronavirus (SARS-CoV)-2 invasion process in addition to angiotensin-converting enzyme 2 (ACE2). Cyclophilin A (CyPA), the extracellular ligand of CD147, has been found to play a role in the infection and replication of coronaviruses. In this study, our results show that CyPA inhibitors such as cyclosporine A (CsA) and STG-175 can suppress the intracellular replication of SARS-CoV-2 by inhibiting the binding of CyPA to the SARS-CoV-2 nucleocapsid C-terminal domain (N-CTD), and the IC50 is 0.23 µM and 0.17 µM, respectively. Due to high homology, CsA also had inhibitory effects on SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), and the IC50 is 3.2 µM and 2.8 µM, respectively. Finally, we generated a formulation of phosphatidylserine (PS)-liposome-CsA for pulmonary drug delivery. These findings provide a scientific basis for identifying CyPA as a potential drug target for the treatment of COVID-19 as well as for the development of broad-spectrum inhibitors for coronavirus via targeting CyPA. Highlights: 1) SARS-CoV-2 infects cells via the binding of its S protein and CD147; 2) binding of SARS-CoV-2 N protein and CyPA is essential for viral replication; 3) CD147 and CyPA are potential therapeutic targets for SARS-CoV-2; and 4) CsA is a potential therapeutic strategy by interrupting CD147/CyPA interactions. SIGNIFICANCE STATEMENT: New severe acute respiratory syndrome coronavirus (SARS-CoV)-2 variants and other pathogenic coronaviruses (CoVs) are continually emerging, and new broad-spectrum anti-CoV therapy is urgently needed. We found that binding sites of cyclophilin A/cyclosporin A (CyPA/CsA) overlap with CyPA/N-CTD (nucleocapsid C-terminal domain), which shows the potential to target CyPA during SARS-CoV-2 infection. Here, we provide new evidence for targeting CyPA in the treatment of coronavirus disease 2019 (COVID-19) as well as the potential of developing CyPA inhibitors for broad-spectrum inhibition of CoVs.

DAUF: A disease-related attentional UNet framework for progressive and stable mild cognitive impairment identification.

Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) plays a significant role in early Alzheimer's disease (AD) diagnosis, which can effectively boost the life quality of patients. Recently, convolutional neural network (CNN)- based methods using structural magnetic resonance imaging (sMRI) images have shown effective for AD identification. However, these CNN-based methods fail to effectively explore the feature extraction of disease-related multi-scale tissues, such as ventricles, hippocampi and cerebral cortex. To address this issue, we propose an end-to-end disease-related attentional UNet framework (DAUF) for identifying pMCI and sMCI, by embedding a devised dual disease-related attention module (D2AM) and a novel tree-structured feature fusion classifier (TFFC). Specifically, D2AM leverages the complementarity between feature maps and attention maps and the complementary features from the encoder and decoder, so as to highlight discriminative semantic and detailed features. Additionally, TFFC is a powerfully joint multi-scale feature fusion and classification head, by employing the homogeneity among multi-scale features, so that the discriminative features of the multi-scale tissues are adequately fused for enhancing classification performance. Finally, extensive experiments demonstrate the superior performance of DAUF, with the effectiveness of D2AM and TFFC on identifying pMCI and sMCI subjects.

Mechanical Properties, Radiation Resistance Performances, and Mechanism Insights of Nitrile Butadiene Rubber Irradiated with High-Dose Gamma Rays.

The radiation effect of materials is very important and directly related to the safety and reliability of nuclear reactors. Polymer materials, one of the indispensable materials in nuclear power equipment, must withstand the ordeal of high-energy ionizing rays. In this work, through screening different γ-ray dose irradiation conditions, we systematically and comprehensively study the changes in the structure and properties of nitrile butadiene rubber (NBR) before and after γ-ray static irradiation at a high dose rate, and master the rule and mechanism of the γ-ray static irradiation effect of these polymer materials. The mapping relationship between the macroscopic properties, microstructure, and irradiation dose of NBR is accurately characterized. With an increase in total irradiation dose, the C=C double bond reaction occurs, and the C≡N bond, C=C, and C=O participate in the hyper crosslinking reaction. The glass transition temperature (Tg) increases with the cumulative irradiation amount. With the increased total irradiation amount, the degree of rubber cross-linking increases, causing an increased crystallinity and decomposition temperature. A growing amount of gamma irradiation causes the mechanical properties of the rubber to degrade simultaneously, increasing the shore hardness while decreasing the tensile strength and ultimate elongation at break. When the cumulative amount reaches 1 MGy, the ultimate elongation at break decreases significantly. A cumulative dose of radiation resistance of 4 MGy can be achieved by the samples. This work can provide theoretical and experimental support for the long-term stability of nitrile butadiene rubber and its derivatives in nuclear radiation fields and space radiation conditions.

Switching Anti-Windup Synthesis for Linear Systems With Asymmetric Actuator Saturation.

This article proposes a switching anti-windup strategy for linear, time-invariant (LTI) systems subject to asymmetric actuator saturation and L2 -disturbances, the core idea behind which is to make full use of the available range of control input space by switching among multiple anti-windup gains. The asymmetrically saturated LTI system is converted to a switched system with symmetrically saturated subsystems, and a dwell time switching rule is presented to govern the switching between different antiwindup gains. Based on multiple Lyapunov functions, we derive sufficient conditions for guaranteeing the regional stability and weighted L2 performance of the closed-loop system. The switching anti-windup synthesis that designs a separate anti-windup gain for each subsystem is cast as a convex optimization problem. In comparison with the design of a single anti-windup gain, our method can induce less conservative results since the asymmetric character of the saturation constraint is fully utilized in the switching anti-windup design. Two numerical examples, and an application to aeroengine control (the experiments are conducted on a semiphysical test bench), demonstrate the superiority and practicality of the proposed scheme.

Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities.

OBJECTIVE: Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs. METHODS: We develop (1) a web-based tool that permits manual annotations of P, P', QRS, R', S', T, T', U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs. RESULTS: Over the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks. CONCLUSION: Our method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.

Recent progress in Chinese fusion research based on superconducting tokamak configuration.

Fusion energy is a promising source of clean energy, which could solve energy shortages and environmental pollution. Research into controlled fusion energy has been ongoing for over half a century. China has created a clear roadmap for magnetic confinement fusion development, where superconducting tokamaks will be used in commercial fusion reactors. The Experimental Advanced Superconducting Tokamak (EAST) is the world's first fully superconducting tokamak with upper and lower divertors, which aims at long-pulse, steady-state, H-mode operation, and 101-s H-mode discharge had been achieved. In 2007, China joined the International Thermonuclear Experimental Reactor (ITER) and became one of its seven members. Thirteen procurement packages are undertaken by China, covering superconducting magnets, power supplies, plasma-facing components (PFCs), diagnostics, etc. To bridge the gap between the ITER and fusion demonstration power plants (DEMOs), China is planning to build the Chinese Fusion Engineering Testing Reactor (CFETR) to demonstrate related technologies and physics models. The engineering design of the CFETR was completed in 2020, and Comprehensive Research Facilities for Fusion Technology (CRAFT) are being constructed to explore the key technologies used in the CFETR.

Sodium-Based Cylindrical Plasmonic Waveguides in the Near-Infrared.

Subwavelength optical field confinement and low-loss propagation are of great significance for compact photonic integration. However, the field confinement capability of plasmonic devices is always accompanied by the inherent Ohmic loss. Although recent studies have shown that sodium (Na) exhibits lower loss than noble metals in the near-infrared band, the field confinement ability has not been adequately assessed. Meanwhile, the high chemical reactivity of Na should be regulated for practical application. Two dielectric-coated Na nanowires, consisting of cylindrical Na nanowires with one or two dielectric layers as claddings, are proposed and investigated in this paper. Based on finite element calculations, we thoroughly study the modal fields and low-loss propagation properties of dielectric-coated Na nanowires. The results demonstrate that Na exhibits lower loss and stronger field confinement than the typical plasmonic material silver. These findings indicate the performance of plasmonic devices can be considerably improved by employing the metal Na compared with devices using noble metals, which may promote the applications in subwavelength photonic devices.

Steric hindrance effect on the excited-state proton transfer process: TDDFT study on the fluorescent sensing mechanism of a fluoride sensor.

An understanding of the excited-state process and the sensing mechanism for specific anions can be helpful for the design and synthesis of fluorescent sensors in analytical chemistry and biotechnology. Here, we theoretically investigated the fluorescent response mechanism of a reported acylhydrazone-based fluorescent sensor (Soft Matter, 2019, 15, 6690) for fluoride recognition using the time-dependent density functional theory approach. At the M06/TZVP/SCM level, the vertical excitation energies, which were calculated based on the ground state and first singlet-state geometries of the sensor molecule, agreed well with the experimental ultraviolet-visible and fluorescence spectra. Therefore, the time-dependent density functional theory method was considered reasonable and effective. According to the frontier orbital analysis and an excited-state potential energy scan, we proposed an excited-state proton transfer mechanism for the sensor-fluorine complex, where the steric hindrance leads to a high potential barrier. The excited-state proton transfer process facilitates sensor molecule deprotonation, alleviates its steric hindrance effect and expands its conjugated system. As a result, the fluorescence emission band of the sensor molecule was red-shifted significantly with the addition of fluoride anion. Based on this fluorescence difference, the sensor could be used for fluoride anion identification. This work provides a strategy to study sensor-analyte interactions in the excited state and offers an approach to tune the fluorescence emission wavelength of sensor molecules in anionic environments.

Utilizing image and caption information for biomedical document classification.

MOTIVATION: Biomedical research findings are typically disseminated through publications. To simplify access to domain-specific knowledge while supporting the research community, several biomedical databases devote significant effort to manual curation of the literature-a labor intensive process. The first step toward biocuration requires identifying articles relevant to the specific area on which the database focuses. Thus, automatically identifying publications relevant to a specific topic within a large volume of publications is an important task toward expediting the biocuration process and, in turn, biomedical research. Current methods focus on textual contents, typically extracted from the title-and-abstract. Notably, images and captions are often used in publications to convey pivotal evidence about processes, experiments and results. RESULTS: We present a new document classification scheme, using both image and caption information, in addition to titles-and-abstracts. To use the image information, we introduce a new image representation, namely Figure-word, based on class labels of subfigures. We use word embeddings for representing captions and titles-and-abstracts. To utilize all three types of information, we introduce two information integration methods. The first combines Figure-words and textual features obtained from captions and titles-and-abstracts into a single larger vector for document representation; the second employs a meta-classification scheme. Our experiments and results demonstrate the usefulness of the newly proposed Figure-words for representing images. Moreover, the results showcase the value of Figure-words, captions and titles-and-abstracts in providing complementary information for document classification; these three sources of information when combined, lead to an overall improved classification performance. AVAILABILITY AND IMPLEMENTATION: Source code and the list of PMIDs of the publications in our datasets are available upon request.

Epidemiologic Characteristics, Transmission Chain, and Risk Factors of Severe Infection of COVID-19 in Tianjin, a Representative Municipality City of China.

This study was performed to describe the epidemiologic characteristics of coronavirus disease 2019 (COVID-19) and explore risk factors for severe infection. Data of all 131 confirmed cases in Tianjin before February 20 were collected. By February 20, a total of 14/16 districts reported COVID-19 cases, with Baodi district reporting the most cases (n = 56). A total of 22 (16.8%) cases had a Wuhan-related exposure. Fever was the most common symptom (82.4%). The median duration of symptom onset to treatment was [1.0 (0.0-4.0) days], the duration of symptom onset to isolation [2.0 (0.0-6.0) days], and the duration of symptom onset to diagnosis [5.0 (2.0-8.0) days]. The analysis of the transmission chain showed two cluster infections with 62 cases infected. Transmission from a family member constituted 42%, usually at the end of transmission chain. Compared with patients with non-severe infections, patients with severe infections were more likely to be male (46.2 vs. 77.3%, P = 0.009) and had a Wuhan-related exposure (14.0 vs. 40.9%, P = 0.004). Multivariate logistic regression showed that male (OR 3.913, 95% CI 1.206, 12.696; P = 0.023) was an independent risk factor for severe infection. This study provides evidence on the epidemic of COVID-19 by analyzing the epidemiological characteristics of confirmed cases in Tianjin. Self-quarantine at an outbreak's early stage, especially for those with high-risk exposures, is conducive to prevent the transmission of infection. Further investigation is needed to confirm the risk factors for severe COVID-19 infection and investigate the mechanisms involved.

Integrating image caption information into biomedical document classification in support of biocuration.

Gathering information from the scientific literature is essential for biomedical research, as much knowledge is conveyed through publications. However, the large and rapidly increasing publication rate makes it impractical for researchers to quickly identify all and only those documents related to their interest. As such, automated biomedical document classification attracts much interest. Such classification is critical in the curation of biological databases, because biocurators must scan through a vast number of articles to identify pertinent information within documents most relevant to the database. This is a slow, labor-intensive process that can benefit from effective automation.We present a document classification scheme aiming to identify papers containing information relevant to a specific topic, among a large collection of articles, for supporting the biocuration classification task. Our framework is based on a meta-classification scheme we have introduced before; here we incorporate into it features gathered from figure captions, in addition to those obtained from titles and abstracts. We trained and tested our classifier over a large imbalanced dataset, originally curated by the Gene Expression Database (GXD). GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource. As part of the MGI literature classification pipeline, GXD curators identify MGI-selected papers that are relevant for GXD. The dataset consists of ~60 000 documents (5469 labeled as relevant; 52 866 as irrelevant), gathered throughout 2012-2016, in which each document is represented by the text of its title, abstract and figure captions. Our classifier attains precision 0.698, recall 0.784, f-measure 0.738 and Matthews correlation coefficient 0.711, demonstrating that the proposed framework effectively addresses the high imbalance in the GXD classification task. Moreover, our classifier's performance is significantly improved by utilizing information from image captions compared to using titles and abstracts alone; this observation clearly demonstrates that image captions provide substantial information for supporting biomedical document classification and curation.Database URL.

Figure and caption extraction from biomedical documents.

MOTIVATION: Figures and captions convey essential information in biomedical documents. As such, there is a growing interest in mining published biomedical figures and in utilizing their respective captions as a source of knowledge. Notably, an essential step underlying such mining is the extraction of figures and captions from publications. While several PDF parsing tools that extract information from such documents are publicly available, they attempt to identify images by analyzing the PDF encoding and structure and the complex graphical objects embedded within. As such, they often incorrectly identify figures and captions in scientific publications, whose structure is often non-trivial. The extraction of figures, captions and figure-caption pairs from biomedical publications is thus neither well-studied nor yet well-addressed. RESULTS: We introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike existing methods, we first separate between text and graphical contents, and then utilize layout information to effectively detect and extract figures and captions. We generate files containing the figures and their associated captions and provide those as output to the end-user.We test our system both over a public dataset of computer science documents previously used by others, and over two newly collected sets of publications focusing on the biomedical domain. Our experiments and results comparing PDFigCapX to other state-of-the-art systems show a significant improvement in performance, and demonstrate the effectiveness and robustness of our approach. AVAILABILITY AND IMPLEMENTATION: Our system is publicly available for use at: https://www.eecis.udel.edu/~compbio/PDFigCapX. The two new datasets are available at: https://www.eecis.udel.edu/~compbio/PDFigCapX/Downloads.

Compound image segmentation of published biomedical figures.

Motivation: Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images. Results: In this article, we develop a new compound image segmentation system, FigSplit, which is based on Connected Component Analysis. To overcome shortcomings typically manifested by existing methods, we develop a quality assessment step for evaluating and modifying segmentations. Two methods are proposed to re-segment the images if the initial segmentation is inaccurate. Experimental results show the effectiveness of our method compared with other methods. Availability and implementation: The system is publicly available for use at: https://www.eecis.udel.edu/~compbio/FigSplit. The code is available upon request. Contact: shatkay@udel.edu. Supplementary information: Supplementary data are available online at Bioinformatics.