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Low-head dams and small hydropower developments are widely distributed in global rivers because of their high cost-efficiency and smooth implementation. However, these installations have great ecological impacts on native fish communities due to the loss of longitudinal river connectivity. Although dam removal and fishway construction are effective measures to mitigate river fragmentation, research on their effects on fish communities remains limited to date. In this study, we investigated fish community dynamics in a mountainous tributary of the Jinsha River-Heishui River following dam removal, fishway construction, and fishway renovation. Our fish sampling data from 2018 to 2023 yielded 6137 fish belonging to 25 species, 12 families, and 3 orders. The native fish community predominantly represented the typical fish fauna of the Qinghai-Tibetan Plateau, primarily including the genus Schizothorax and the families Sisoridae and Nemacheilidae. Representative fish species in the Heishui River demonstrate notable adaptations to cold water, high altitude, and rapid flow environments. After removing the Laomuhe Dam, upstream fish abundance increased sharply, but species richness slightly decreased in the short term. Fishway construction significantly enhanced species richness and abundance and reduced the difference between the upstream and downstream fish communities from the Songxin Dam. However, the subsequent fishway renovation altered the dominance of fish species, with marginal changes in community structure and abundance. Variations in fish community dynamics in the river channel and fishway structure can be attributed to differences in ecological guilds (e.g., flow preference and thermal regime) and environmental factors (e.g., flow discharge, water temperature, and water depth). The present study provides insight into the significance of mitigation measures for the impact of dams on mountainous rivers in southwest China through ecological assessment and guides for decision-making in the conservation and restoration of specific fish communities., Competing Interests: Declaration of competing interest We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: This study aimed to investigate the prognostic significance of stress dynamic myocardial CT perfusion imaging (CTP) and determine whether it has incremental advantages over coronary computed tomography angiography (CTA)-derived parameters., Methods: This prospective multicenter study included patients with suspected coronary artery disease who had undergone coronary CTA and CTP and were followed for 6 years. The endpoint was time-to-first major adverse cardiovascular event (MACE). MACEs included all-cause mortality, cardiac death, nonfatal myocardial infarction, unstable angina, late revascularization, and heart failure or aggravated angina symptoms requiring hospitalization. The CTP-derived absolute myocardial blood flow (MBF), relative MBF ratio (r-MBF), and the CTA-derived fractional flow reserve (CT-FFR) and stenosis were analyzed. The independent predictors, time-to-event distributions and the incremental prognostic value of CTP over CTA-derived parameters were assessed., Results: A total of 226 patients (66.7 ± 11.3 years, 37.6% women) were enrolled with a median follow-up time of 1233 days. Twenty-one patients (9.29%) experienced MACEs and ten patients (4.4%) experienced hard MACEs. The r-MBF was the only independent predictor for MACEs in both all patients (hazard ratio [HR]: 0.82 (0.69-0.97), p = 0.01) and patients available for CTA stenosis (%) and CT-FFR calculation (HR: 0.72 (0.57-0.91), p = 0.006). The incremental prognostic significance of r-MBF over CT-FFR was confirmed after combining with CTA-derived parameters (concordance index: 0.919 vs. 0.811; p < 0.01). Patients with r-MBF < 0.77 were more likely to experience MACEs and have a lower average survival time (1565 vs. 1790 days; p < 0.01) after stent implantation., Conclusions: CTP-assessed r-MBF was independently correlated with MACEs and provided incremental prognostic significance., Key Points: Question The prognostic significance of CT perfusion (CTP) lacks sufficient support from clinical research. Findings In this Chinese population, CTP has the strongest prognostic significance, over CT-Fractional Flow Reserve (FFR) and CTA stenosis in predicting major adverse cardiovascular events (MACEs). Clinical relevance Relative myocardial blood flow ratio had the strongest prognostic value and incremental advantages in predicting MACEs beyond CT-FFR and coronary CTA stenosis quantification, as well as advantages in patients with stents, aiding in personalized diagnosis and treatment plans., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Y.-N.W. Conflict of interest: X.-B.Y. is an employee of Siemens Healthineers Ltd. The remaining authors state that there neither exists a conflict of interest nor that there is financial information to disclose. Statistics and biometry: Wei Han kindly provided statistical advice for this manuscript (Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China). Informed consent: Written informed consent was obtained from all participants in this study. Ethical approval: Institutional Review Board approval was obtained. The Multicenter VALIDITY Trial protocol was approved by each of the institutional ethics committees (Peking Union Medical College Hospital, Fu Wai Hospital, Tianjin Medical University General Hospital, Guangdong Provincial People’s Hospital, and Shanghai General Hospital) and was conducted according to the Declaration of Helsinki. Study subjects or cohorts overlap: No study subjects or cohorts have been previously reported. Methodology: Prospective Diagnostic or prognostic study Multicenter study, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To compare the ability among 5.0T MRI, 3.0T MRI and MDCT in identifying insulinomas and determining the tumor-to-duct relationship., Methods: A consecutive series of patients highly suspected of insulinomas were enrolled between October 2021 and February 2024, who underwent 5.0T MRI preoperatively, as well as 3.0T MRI or MDCT. The subjective and objective image quality, lesion-to-pancreas contrast, clarity of main pancreatic duct (MPD) and tumor-to-duct relationship at 5.0T, 3.0T MRI and MDCT were evaluated by three observers. The correlation between tumor-duct distance and clinically relevant postoperative pancreatic fistula (CR-POPF) risk was analyzed., Results: Forty patients (14 men; mean age, 46.4 ± 16.5 years) with insulinomas were included in this study. 21 of them underwent both 5.0T and 3.0T MRI; and 38 of them underwent 5.0T MRI and MDCT. The intra- and inter-observer agreement of 5.0T MRI were good to excellent. 5.0T showed significantly higher subjective and objective image quality on T1WI and DWI compared to 3.0T (p < 0.05). Lesion-to-pancreas contrast was superior across all sequences at 5.0T compared to 3.0T(p < 0.05). A head-to-head comparison of patients who received both 5.0T and 3.0T MRI demonstrated that tumor detection was superior with 5.0T MRI (5.0T: 100%; 3.0 T: 92.0%, p < 0.05). Feasibility of tumor-to-duct relationship assessment was superior at 5.0T, compared to 3.0T and MDCT (93.2%, 64.0% and 52.3%, respectively, p < 0.05). Tumor-duct distance could predict CR-POPF after enucleation surgery (areas under the ROC curve 0.79, p = 0.01)., Conclusion: 5.0T MRI exhibits certain superiority in detecting insulinomas and assessing tumor-to-duct relationship compared to 3.0T MRI and MDCT., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: To assess the feasibility of 5.0 T magnetic resonance imaging (MRI) in characterizing pancreatic cystic lesions (PCLs), compared with 3.0 T MRI and multidetector computed tomography (MDCT). Methods: Thirty-five patients with PCLs underwent 5.0 T MR alongside 3.0 T MR or MDCT. Two observers measured subjective and objective image quality scores. The consistency of two observers between 5.0 T and 3.0 T was calculated by intraclass correlation coefficients. The characteristics of PCLs and their specific diagnosis, as well as benignity/malignancy, were evaluated across MDCT, 3.0 T, and 5.0 T MRI. Results: The 5.0 T MR demonstrated significantly higher subjective image quality and SNR on T1WI compared to that in 3.0 T MR ( p < 0.05). The 5.0 T MRI identified more cyst lesions than the 3.0 T MRI (40 and 32) and MDCT (82 and 56). The sensitivity, specificity, and accuracy for differentiating benign from malignant lesions with 5.0 T MRI (75%, 100%, and 91.4%, respectively) surpassed those of 3.0 T MRI and MDCT. The accuracy of the specific diagnosis of PCLs at 5.0 T MRI (80%) was superior to 3.0 T MRI and MDCT. Conclusions: 5.0 T MRI exhibits certain superiority in delineating details of PCLs and in clinical diagnostic accuracy, outperforming MDCT and 3.0 T MRI while maintaining sufficient image quality.

Background Cardiac MRI at 5 T has recently become available and potentially improves tissue contrast enhancement at gadolinium chelate-enhanced T1-weighted imaging. Purpose To evaluate the feasibility of 5-T myocardial late gadolinium enhancement (LGE) MRI in assessing myocardial fibrosis by comparing image quality and LGE quantification with reference-standard 3-T myocardial LGE MRI. Materials and Methods Consecutive patients with confirmed myocardial fibrosis on previous 3-T MRI scans between January 2023 and July 2023 prospectively underwent follow-up imaging from August 2023 to November 2023. Each participant underwent follow-up 5-T imaging using an identical dose of contrast agent. Radiologist scoring of image quality using a Likert scale (range, 1-5), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio, and semiautomatic quantitative LGE assessment were obtained and reported as medians and IQRs. Paired Wilcoxon signed rank tests were used to compare characteristics derived at 3-T and 5-T imaging. Results A total of 18 participants (mean age, 49 years ± 17 [SD]; nine male participants) were included, with a mean interval of 6.2 months ± 2.3 between undergoing 3-T and 5-T MRI. Median image quality scores were 4.0 (IQR, 3.0-4.2) at 3 T and 4.0 (IQR, 3.0-4.4) at 5 T ( P = .45). SNR at 5 T was higher than at 3 T (183.7 [IQR, 147.2-255.9] vs 125.8 [IQR, 108.2-171.6], respectively; P = .002). Median CNR at 5 T was higher than at 3 T in normal myocardium (50.8 [IQR, 35.4-67.9] vs 16.5 [IQR, 11.3-24.6], respectively) and pericardial fat (21.4 [IQR, 7.1-29.3] vs -5.0 [IQR, -16.4 to -2.3], respectively) (both P < .001). There was no evidence of a difference in the percentage of LGE quantified between 5 T and 3 T (median, 11.8% [IQR, 7.7%-20.5%] vs 12.6% [IQR, 6.6%-20.4%], respectively; P = .81). Conclusion Myocardial LGE MRI at 5 T was found to be feasible, with no evidence of differences in subjective image quality and myocardial fibrosis quantification compared with 3-T myocardial LGE MRI. Furthermore, with use of identical contrast agent doses, SNRs and CNRs were improved at 5 T. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Czum in this issue.

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn" among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

Purpose: Interferon-stimulated gene 15 (ISG15) deficiency, a rare human inborn error of immunity characterized by susceptibility to Bacillus Calmette-Guerin (BCG) diseases, neuropathic and dermatological manifestations., Methods: The clinical and immunological features of two siblings with ISG15 deficiency combined with asymptomatic myeloperoxidase (MPO) mutations were analyzed, and their pathogenesis, as well as target therapeutic candidates, were explored., Results: The manifestation in patient 2 was skin lesions, while those in patient 1 were intracranial calcification and recurrent pneumonia. Whole-exome identified novel, dual mutations in ISG15 and MPO. PBMCs and B cell lines derived from the patients showed hyper-activated JAK/STAT signaling. Normal neutrophil function excluded pathogenicity caused by the MPO mutation. RNA sequencing identified baricitinib as therapeutic candidate., Conclusions: We report two sibling patients harboring the same novel ISG15 mutation showing diverse clinical features, and one harbored a rare phenotype of pneumonia. These findings expand the clinical spectrum of ISG15 deficiency and identify baricitinib as therapeutic candidate., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest related to the manuscript., (Copyright © 2023. Published by Elsevier Inc.)

3-D object recognition has successfully become an appealing research topic in the real world. However, most existing recognition models unreasonably assume that the categories of 3-D objects cannot change over time in the real world. This unrealistic assumption may result in significant performance degradation for them to learn new classes of 3-D objects consecutively due to the catastrophic forgetting on old learned classes. Moreover, they cannot explore which 3-D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3-D objects. To tackle the above challenges, we develop a novel Incremental 3-D Object Recognition Network (i.e., InOR-Net), which could recognize new classes of 3-D objects continuously by overcoming the catastrophic forgetting on old classes. Specifically, category-guided geometric reasoning is proposed to reason local geometric structures with distinctive 3-D characteristics of each class by leveraging intrinsic category information. We then propose a novel critic-induced geometric attention mechanism to distinguish which 3-D geometric characteristics within each class are beneficial to overcome the catastrophic forgetting on old classes of 3-D objects while preventing the negative influence of useless 3-D characteristics. In addition, a dual adaptive fairness compensations' strategy is designed to overcome the forgetting brought by class imbalance by compensating biased weights and predictions of the classifier. Comparison experiments verify the state-of-the-art performance of the proposed InOR-Net model on several public point cloud datasets.

BACH2-related immunodeficiency and autoimmunity (BRIDA) is an inborn error of immunity, newly reported in 2017, presenting with symptoms of immunoglobulin deficiency and ongoing colitis. Studies using a mouse model have demonstrated that BACH2 deficiency predisposes individuals to systemic lupus erythematosus (SLE); however, no BACH2 deficiency has been reported in SLE patients. Here we describe a patient with BRIDA presenting with early-onset SLE, juvenile dermatomyositis, and IgA deficiency. Whole exome sequencing analysis of the patient and her parents revealed a novel heterozygous point mutation in BACH2, c.G1727T, resulting in substitution of a highly conserved arginine with leucine (R576L), which is predicted to be deleterious, in the patient and her father. Reduced BACH2 expression and deficient transcriptional repression of the BACH2 target, BLIMP1, were detected in PBMCs or lymphoblastoid cell lines of our patient. Notably, extreme reduction of memory B cells was detected in the patient's father, although he had no obvious symptoms. SLE symptoms and recurrent fever were relieved by treatment with prednisone combined with tofacitinib. Thus, we present the second report of BRIDA and demonstrate that BACH2 may be a monogenic cause of SLE., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The dedicator of cytokinesis 2(DOCK2) protein, an atypical guanine nucleotide exchange factor (GEFs), is a member of the DOCKA protein subfamily. DOCK2 protein deficiency is characterized by early-onset lymphopenia, recurrent infections, and lymphocyte dysfunction, which was classified as combined immune deficiency with neutrophil abnormalities as well. The only cure is hematopoietic stem cell transplantation. Here, we report two patients harboring four novel DOCK2 mutations associated with recurrent infections including live attenuated vaccine-related infections. The patient's condition was partially alleviated by symptomatic treatment or intravenous immunoglobulin. We also confirmed defects in thymic T cell output and T cell proliferation, as well as aberrant skewing of T/B cell subset TCR-Vβ repertoires. In addition, we noted neutrophil defects, the weakening of actin polymerization, and BCR internalization under TCR/BCR activation. Finally, we found that the DOCK2 protein affected antibody affinity although with normal total serum immunoglobulin. The results reported herein expand the clinical phenotype, the pathogenic DOCK2 mutation database, and the immune characteristics of DOCK2-deficient patients., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Patients with DEX (deficiency in ELF4, X-linked) were recently reported by our team and others, and cases are very limited worldwide. Our knowledge of this new disease is currently preliminary. In this study, we described 5 more cases presenting mainly with oral ulcer, inflammatory bowel disease-like symptoms, fever of unknown origin, anemia, or systemic lupus erythematosus. Whole exome sequencing identified potential pathogenic ELF4 variants in all cases. The pathogenicity of these variants was confirmed by the detection of ELF4 expression in peripheral blood mononuclear cells from patients and utilizing a simple IFN-b luciferase reporter assay, as previously reported. Our findings significantly contribute to the current understanding of DEX., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Chronic granulomatosis disease (CGD) is a rare inborn error of immunity, characterized by phagocytic respiratory outbreak dysfunction. Mutations causing CGD occur in CYBB on the X chromosome and in the autosomal genes CYBA, NCF1, NCF2, NCF4, RAC2, and CYBC1. Nevertheless, some patients are clinically diagnosed with CGD, due to abnormal respiratory outbursts, while the pathogenic gene mutation is unidentified. Here, we report a patient with CGD who first presented with Bacillus Calmette-Guérin disease and had recurrent pneumonia. He was diagnosed with CGD by nitro blue tetrazolium and respiratory burst tests. Detailed assessment of neutrophil activity revealed that patient neutrophils were almost entirely nonfunctional. Sanger sequencing detected a 6-kb insertion of a LINE-1 transposable element in the third intron of CYBB, leading to abnormal splicing and pseudoexon insertion, as well as introduction of a premature termination codon, resulting in predicted protein truncation. Clonal analysis demonstrated that the patient had somatic mosaicism, and the phagocytes were almost all variant CYBB, while the mosaicism rate of PBMC was about 65%. Finally, deep RNA sequencing and gp91 phox expression analysis confirmed the pathogenicity of the mutation. In conclusion, we demonstrate that insertion of a LINE-1 transposon in a CYBB intron was responsible for CGD in our patient. Intron LINE-1 transposon element insertion should be examined in CGD patients without any known disease-causing gene mutation, in addition to identification of new genes., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Mutations in signal transducer and activator of transcription 1 (STAT1) cause a broad spectrum of disease phenotypes. Heterozygous STAT1 loss-of-function (LOF) mutations cause Mendelian susceptibility to mycobacterial diseases (MSMD) infection, which is attributable to impaired IFN-γ signaling. The identification of novel mutations may extend the phenotypes associated with autosomal dominant (AD) STAT1 deficiency., Methods: Five patients with heterozygous STAT1 variations were recruited and their clinical and immunologic phenotypes were analyzed, with particular reference to JAK-STAT1 signaling pathways., Results: Four, heterozygous STAT1 deficiency mutations were identified, three of which were novel mutations. Two of the mutations were previously unreported mRNA splicing mutations in AD STAT1-deficient patients. Patients with heterozygous STAT1 deficiency suffered not only mycobacterial infection, but also intracellular non-mycobacterial bacterial infection and congenital multiple malformations. AD-LOF mutation impaired IFN-γ-mediated STAT1 phosphorylation, gamma-activated sequence (GAS), and IFN-stimulated response element (ISRE) transcription activity and IFN-induced gene expression to different extents, which might account for the diverse clinical manifestations observed in these patients., Conclusion: The infectious disease susceptibility and phenotypic spectrum of patients with AD STAT1-LOF are broader than simply MSMD. The susceptibility to infections and immunological deficiency phenotypes, observed in AD-LOF patients, confirms the importance of STAT1 in host-pathogen interaction and immunity. However, variability in the nature and extent of these phenotypes suggests that functional analysis is required to identify accurately novel, heterozygous STAT1 mutations, associated with pathogenicity. Aberrant splice of STAT1 RNA could result in AD-LOF for STAT1 signaling which need more cases for confirmation., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Object clustering has received considerable research attention most recently. However, 1) most existing object clustering methods utilize visual information while ignoring important tactile modality, which would inevitably lead to model performance degradation and 2) simply concatenating visual and tactile information via multiview clustering method can make complementary information to not be fully explored, since there are many differences between vision and touch. To address these issues, we put forward a graph-based visual-tactile fused object clustering framework with two modules: 1) a modality-specific representation learning module M R and 2) a unified affinity graph learning module M U . Specifically, M R focuses on learning modality-specific representations for visual-tactile data, where deep non-negative matrix factorization (NMF) is adopted to extract the hidden information behind each modality. Meanwhile, we employ an autoencoder-like structure to enhance the robustness of the learned representations, and two graphs to improve its compactness. Furthermore, M U highlights how to mitigate the differences between vision and touch, and further maximize the mutual information, which adopts a minimizing disagreement scheme to guide the modality-specific representations toward a unified affinity graph. To achieve ideal clustering performance, a Laplacian rank constraint is imposed to regularize the learned graph with ideal connected components, where noises that caused wrong connections are removed and clustering labels can be obtained directly. Finally, we propose an efficient alternating iterative minimization updating strategy, followed by a theoretical proof to prove framework convergence. Comprehensive experiments on five public datasets demonstrate the superiority of the proposed framework.

Dam removal is considered an effective measure to solve the adverse ecological effects caused by dam construction and has started to be considered in China. The sediment migration and habitat restoration of river ecosystems after dam removal have been extensively studied abroad but are still in the exploratory stage in China. However, there are few studies on the ecological response of fishes at different growth stages. Considering the different habitat preferences of Schizothorax prenanti ( S. prenanti ) in the spawning and juvenile periods, this study coupled field survey data and a two-dimensional hydrodynamic model to explore the changes in river morphology at different scales and the impact of changes in hydrodynamic conditions on fish habitat suitability in the short term. The results show that after the dam is removed, in the upstream of the dam, the riverbed is eroded and cut down and the riverbed material coarsens. With the increase in flow velocity and the decrease in flow area, the weighted usable area (WUA) in the spawning and juvenile periods decreases by 5.52% and 16.36%, respectively. In the downstream of the dam, the riverbed is markedly silted and the bottom material becomes fine. With the increase in water depth and flow velocity, the WUA increases by 79.91% in the spawning period and decreases by 67.90% in the juvenile period, which is conducive to adult fish spawning but not to juvenile fish growth. The changes in physical habitat structure over a short time period caused by dam removal have different effects on different fish development periods, which are not all positive. The restoration of stream continuity increases adult fish spawning potential while limiting juvenile growth. Thus, although fish can spawn successfully, self-recruitment of fish stocks can still be affected if juvenile fish do not grow successfully. This study provides a research basis for habitat assessment after dam removal and a new perspective for the subsequent adaptive management strategy of the project.

Background: Cardiovascular magnetic resonance (CMR) imaging at ultra-high fields (UHF) such as 7T has encountered many challenges such as faster T 2 * relaxation, stronger B 0 and B 1+ field inhomogeneities and additional safety concerns due to increased specific absorption rate (SAR) and peripheral nervous stimulation (PNS). Recently, a new line of 5T whole body MRI system has become available, and this study aims at evaluating the performance and benefits of this new UHF system for CMR imaging., Methods: Gradient echo (GRE) CINE imaging was performed on healthy volunteers at both 5 and 3T, and was compared to balanced steady-state-free-procession (bSSFP) CINE imaging at 3T as reference. Higher spatial resolution GRE CINE scans were additionally performed at 5T. All scans at both fields were performed with ECG-gating and breath-holding. Image quality was blindly evaluated by two radiologists, and the cardiac functional parameters (e.g., EDV/ESV/mass/EF) of the left and right ventricles were measured for statistical analyses using the Wilcoxon signed-rank test and Bland-Altman analysis., Results: Compared to 3T GRE CINE imaging, 5T GRE CINE imaging achieved comparable or improved image quality with significantly superior SNR and CNR, and it has also demonstrated excellent capability for high resolution (1.0 × 1.0 × 6.0 mm 3 ) imaging. Functional assessments from 5T GRE CINE images were highly similar with the 3T bSSFP CINE reference., Conclusions: This pilot study has presented the initial evaluation of CMR CINE imaging at 5T UHF, which yielded superior image quality and accurate functional quantification when compared to 3T counterparts. Along with reliable ECG gating, the new 5T UHF system has the potential to achieve well-balanced performance for CMR applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Lin, Liu, Sun, Wang, Liang, Zheng, Jin and Wang.)

Background: Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare disorder of the immune regulatory system caused by forkhead box P3 ( FOXP3 ) mutations. Abnormal numbers or functions of regulatory T (Treg) cells account for the various autoimmune symptoms. We aimed to explore the molecular genetics and phenotypic spectra of patients with atypical IPEX syndrome in China., Methods: We analyzed the molecular, clinical and immune phenotype characteristics of five Chinese patients with FOXP3 mutations., Results: We summarized the molecular and phenotypic features of five patients with FOXP3 mutations, including two novel mutations. Four of the five patients displayed atypical phenotypes, and one developed immune-related peripheral neuropathy. Three of the five patients showed normal frequencies of Treg cells, but the proportions of subsets of Treg cells, CD4 + T cells and B cells were out of balance., Conclusions: Our report broadens the understanding of the clinical features of atypical IPEX syndrome. Our detailed analyses of the immunological characteristics of these patients enhance the understanding of the possible mechanisms underlying the clinical manifestations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Huang, Fang, Zeng, Chen, Yang, Sun, Dai, An, Tang, Dou, Zhao and Zhou.)

Monogenic autoinflammatory diseases (mAIDs) are a heterogeneous group of diseases affecting primarily innate immunity, with various genetic causes. Genetic diagnosis of mAIDs can assist in the patient's management and therapy. However, a large number of sporadic and familial cases remain genetically uncharacterized. Deficiency in ELF4, X-linked (DEX) is recently identified as a novel mAID. Here, we described a pediatric patient suffering from recurrent viral and bacterial respiratory infection, refractory oral ulcer, constipation, and arthritis. Whole-exome sequencing found a hemizygous variant in ELF4 (chrX:129205133 A > G, c.691 T > C, p.W231R). Using cells from patient and point mutation mice, we showed mutant cells failed to restrict viral replication effectively and produced more pro-inflammatory cytokines. RNA-seq identified several potential critical antiviral and anti-inflammation genes with decreased expression, and ChIP-qPCR assay suggested mutant ELF4 failed to bind to the promoters of these genes. Thus, we presented the second report of DEX., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are of prescribed size and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our FCL3 model firstly transfers knowledge from these libraries to encode the new task, i.e., effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then: 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multitask data sets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multitask learning models.

The high rate of false arrhythmia alarms in Intensive Care Units (ICUs) can lead to disruption of care, negatively impacting patients' health through noise disturbances, and slow staff response time due to alarm fatigue. Prior false-alarm reduction approaches are often rule-based and require hand-crafted features from physiological waveforms as inputs to machine learning classifiers. Despite considerable prior efforts to address the problem, false alarms are a continuing problem in the ICUs. In this work, we present a deep learning framework to automatically learn feature representations of physiological waveforms using convolutional neural networks (CNNs) to discriminate between true vs. false arrhythmia alarms. We use Contrastive Learning to simultaneously minimize a binary cross entropy classification loss and a proposed similarity loss from pair-wise comparisons of waveform segments over time as a discriminative constraint. Furthermore, we augment our deep models with learned embeddings from a rule-based method to leverage prior domain knowledge for each alarm type. We evaluate our method using the dataset from the 2015 PhysioNet Computing in Cardiology Challenge. Ablation analysis demonstrates that Contrastive Learning significantly improves the performance of a combined deep learning and rule-based-embedding approach. Our results indicate that the final proposed deep learning framework achieves superior performance in comparison to the winning entries of the Challenge., (© 2022. The Author(s).)

Restoring images degraded by rain has attracted more academic attention since rain streaks could reduce the visibility of outdoor scenes. However, most existing deraining methods attempt to remove rain while recovering details in a unified framework, which is an ideal and contradictory target in the image deraining task. Moreover, the relative independence of rain streak features and background features is usually ignored in the feature domain. To tackle these challenges above, we propose an effective Pyramid Feature Decoupling Network (i.e., PFDN) for single image deraining, which could accomplish image deraining and details recovery with the corresponding features. Specifically, the input rainy image features are extracted via a recurrent pyramid module, where the features for the rainy image are divided into two parts, i.e., rain-relevant and rain-irrelevant features. Afterwards, we introduce a novel rain streak removal network for rain-relevant features and remove the rain streak from the rainy image by estimating the rain streak information. Benefiting from lateral outputs, we propose an attention module to enhance the rain-irrelevant features, which could generate spatially accurate and contextually reliable details for image recovery. For better disentanglement, we also enforce multiple causality losses at the pyramid features to encourage the decoupling of rain-relevant and rain-irrelevant features from the high to shallow layers. Extensive experiments demonstrate that our module can well model the rain-relevant information over the domain of the feature. Our framework empowered by PFDN modules significantly outperforms the state-of-the-art methods on single image deraining with multiple widely-used benchmarks, and also shows superiority in the fully-supervised domain.

Background: TYK2 deficiency is a rare primary immunodeficiency disease caused by loss-of-function mutations of TYK2 gene, which is initially proposed as a subset of hyper-IgE syndrome (HIES). However, accumulating evidence suggests TYK2-deficient patients do not necessarily present with HIES characteristics, indicating a vacuum of knowledge on the exact roles of TYK2 in human immune system., Method: Pathogenic effects of patients were confirmed by qRT-PCR, Western blot, and protein stability assays. The responses to cytokines including IFN-α/β/γ, IL-6, IL-10, IL-12, and IL-23 of peripheral blood mononuclear cells (PBMCs) from these patients were detected by Western blot, qRT-PCR, and flow cytometry. The differentiation of T and B cells was detected by flow cytometry., Results: We described five more TYK2-deficient cases presenting with or without hyper-IgE levels, atopy, and distinct pathogen infection profile, which are caused by novel TYK2 mutations. These mutations were all found by high-throughput sequencing and confirmed by Sanger sequencing. The patients showed heterogeneous responses to various cytokine treatments, including IFN-α/β/γ, IL-6, IL-10, IL-12, and IL-23. The homeostasis of lymphocytes is also disrupted., Conclusion: Based on our findings, we propose that TYK2 works as a multi-tasker in orchestrating various cytokine signaling pathways, differentially combined defects which account for the expressed clinical manifestations., (© 2021 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.)

Although it is widely accepted that the construction of dams may alter fish habitats, few studies have followed the life cycles of fish and combined the environmental conditions with the ecological behaviors and habit preferences of fish during reproductive processes to assess its effects of dam construction. In this study, we call for more sophisticated and holistic assessment framework, including effectiveness of technologies intended to mitigate environmental impacts in different life stages. An assessment framework that considers the swimming ability, perception ability of water flow and environmental preference of different fish species during migration, spawning and hatching was proposed. We used the Baihetan Reservoir as an example environment to assess the impoundment effect on the habitat of a tributary upstream of the reservoir. We observed shifts in the habitats of target fish in different life stages which is dominated by reservoir operation of the Baihetan Dam. Combined with the response of fish activities to impoundment, the selection of suitable positions for artificial breeding and release projects and the outlet of the fish transportation system were recommended measures to improve the migration possibilities. Our reassessment results also demonstrated the theoretical possibility and feasibility of joint improvements in spawning and hatching periods using instream structures. Our framework provides a complete set of "assessment-solution" processes for developers and managers to address the aquatic ecological degradation caused by resource development, and its use is strongly recommended for assessments or assessments of damming effects in other regions and on other fish species., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Unsupervised domain adaptation without accessing expensive annotation processes of target data has achieved remarkable successes in semantic segmentation. However, most existing state-of-the-art methods cannot explore whether semantic representations across domains are transferable or not, which may result in the negative transfer brought by irrelevant knowledge. To tackle this challenge, in this paper, we develop a novel Knowledge Aggregation-induced Transferability Perception (KATP) for unsupervised domain adaptation, which is a pioneering attempt to distinguish transferable or untransferable knowledge across domains. Specifically, the KATP module is designed to quantify which semantic knowledge across domains is transferable, by incorporating transferability information propagation from global category-wise prototypes. Based on KATP, we design a novel KATP Adaptation Network (KATPAN) to determine where and how to transfer. The KATPAN contains a transferable appearance translation module T&#95;A() and a transferable representation augmentation module T&#95;R(), where both modules construct a virtuous circle of performance promotion. T&#95;A() develops a transferability-aware information bottleneck to highlight where to adapt transferable visual characterizations and modality information; T&#95;R() explores how to augment transferable representations while abandoning untransferable information, and promotes the translation performance of T&#95;A() in return. Experiments on several representative datasets and a medical dataset support the state-of-the-art performance of our model.

Background: NOD-like receptor family CARD-containing 4 protein (NLRC4) is a cytosolic protein that forms an inflammasome in response to flagellin and type 3 secretion system (T3SS) proteins from invading Gram-negative bacteria. NLRC4 mutations have been recently identified in early-onset severe autoinflammatory disorders. In this study, we reported a novel mutation in NLRC4 in two Chinese patients, who manifested with recurrent urticaria and arthralgia., Methods: We summarized the clinical data of the two patients. Gene mutations were identified by whole-exome sequencing (WES). Swiss-PdbViewer was used to predict the pathogenicity of the identified mutations. Cytokine levels and caspase-1 activation were detected in the patient PBMCs with lipopolysaccharide (LPS) stimulation. All previously published cases with NLRC4 mutations were reviewed., Results: We identified a missense heterozygous mutation (c.514G>A, p.Gly172Ser), which was located in the highly conserved residue of nucleotide-binding domain (NBD) of NLRC4. The mutation did not alter the expression of NLRC4 protein, but induced considerably much higher production of IL-1β and IL-6 in patient PBMCs than in healthy controls after LPS stimulation. Four NLRC4 inflammasomopathy phenotypes have been described, with severe inflammatory diseases including macrophage activation syndrome, enterocolitis and NOMID in patients with mutations in the NBD and HD1 domains, whereas a mild clinical phenotype was associated with two mutations in the WHD domain of NLRC4., Conclusion: We identified a novel mutation in the NBD domain, and the patients just presented with a mild inflammatory phenotype. Thus, our findings reinforce the diversity of NLRC4 mutations and expand the clinical spectrum of associated diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wang, Wen, Deng, Li, Sun, Zhao, Tang and Mao.)

Development of aqueous asymmetric supercapacitors (ASCs) is often limited by low specific capacitance of negative electrodes. Herein, a composite electrode with tiny vanadium pentoxide (V 2 O 5 ) nanoparticles homogeneously decorated in vertically-aligned carbon nanotube arrays (VACNTs) is prepared by supercritical CO 2 impregnation and subsequent annealing, and used as binder-free negative electrode for aqueous ASCs. Owing to its unique three-dimensional (3D) hierarchical nanostructure, the V 2 O 5 /VACNTs (VN) electrodes exhibit an ideal specific capacitance of 284 F g -1 in the potential range of -1.1 to 0 V vs SCE at 2 A g -1 and outstanding cycling stability in the Na 2 SO 4 aqueous solution. An aqueous ASC device possessing wide potential range of 1.7 V was constructed with pure VACNTs and VN-350 as the positive and negative electrodes, respectively. The ASC delivers a high energy density of 32.3 Wh kg -1 at a power density of 118 W kg -1 and satisfactory cycling life with capacitance retention of 76% after 5000 cycles., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Spectral clustering has become one of the most effective clustering algorithms. We in this work explore the problem of spectral clustering in a lifelong learning framework termed as Generalized Lifelong Spectral Clustering (GL 2 SC). Different from most current studies, which concentrate on a fixed spectral clustering task set and cannot efficiently incorporate a new clustering task, the goal of our work is to establish a generalized model for new spectral clustering task by What and How to lifelong learn from past tasks. For what to lifelong learn, our GL 2 SC framework contains a dual memory mechanism with a deep orthogonal factorization manner: an orthogonal basis memory stores hidden and hierarchical clustering centers among learned tasks, and a feature embedding memory captures deep manifold representation common across multiple related tasks. When a new clustering task arrives, the intuition here for how to lifelong learn is that GL 2 SC can transfer intrinsic knowledge from dual memory mechanism to obtain task-specific encoding matrix. Then the encoding matrix can redefine the dual memory over time to provide maximal benefits when learning future tasks. To the end, empirical comparisons on several benchmark datasets show the effectiveness of our GL 2 SC, in comparison with several state-of-the-art spectral clustering models.

Cross-modal clustering aims to cluster the high-similar cross-modal data into one group while separating the dissimilar data. Despite the promising cross-modal methods have developed in recent years, existing state-of-the-arts cannot effectively capture the correlations between cross-modal data when encountering with incomplete cross-modal data, which can gravely degrade the clustering performance. To well tackle the above scenario, we propose a novel incomplete cross-modal clustering method that integrates canonical correlation analysis and exclusive representation, named incomplete Cross-modal Subspace Clustering (i.e., iCmSC). To learn a consistent subspace representation among incomplete cross-modal data, we maximize the intrinsic correlations among different modalities by deep canonical correlation analysis (DCCA), while an exclusive self-expression layer is proposed after the output layers of DCCA. We exploit a l 1,2 -norm regularization in the learned subspace to make the learned representation more discriminative, which makes samples between different clusters mutually exclusive and samples among the same cluster attractive to each other. Meanwhile, the decoding networks are employed to reconstruct the feature representation, and further preserve the structural information among the original cross-modal data. To the end, we demonstrate the effectiveness of the proposed iCmSC via extensive experiments, which can justify that iCmSC achieves consistently large improvement compared with the state-of-the-arts.

Image denoising is a classical topic yet still a challenging problem, especially for reducing noise from the texture information. Feature scaling (e.g., downscale and upscale) is a widely practice in image denoising to enlarge receptive field size and save resources. However, such a common operation would lose some visual informative details. To address those problems, we propose fast and accurate image denoising via attention guided scaling (AGS). We find that the main informative feature channel and visual primitives during the scaling should keep similar. We then propose to extract the global channel-wise attention to maintain main channel information. Moreover, we propose to collect global descriptors by considering the entire spatial feature. And we then distribute the global descriptors to local positions of the scaled feature, based on their specific needs. We further introduce AGS for adversarial training, resulting in a more powerful discriminator. Extensive experiments show the effectiveness of our proposed method, where we clearly surpass all the state-of-the-art methods on most popular synthetic and real-world denoising benchmarks quantitatively and visually. We further show that our network contributes to other high-level vision applications and improves their performances significantly.

In a real-world scenario, an object could contain multiple tags instead of a single categorical label. To this end, multi-label learning (MLL) emerged. In MLL, the feature distributions are long-tailed and the complex semantic label relation and the long-tailed training samples are the main challenges. Semi-supervised learning is a potential solution. While, existing methods are mainly designed for single class scenario while ignoring the latent label relations. In addition, they cannot well handle the distribution shift commonly existing across source and target domains. To this end, a Semi-supervised Dual Relation Learning (SDRL) framework for multi-label classification is proposed. SDRL utilizes a few labeled samples as well as large scale unlabeled samples in the training stage. It jointly explores the inter-instance feature-level relation and the intra-instance label-level relation even from the unlabeled samples. In our model, a dual-classifier structure is deployed to obtain domain invariant representations. The prediction results from the classifiers are further compared and the most confident predictions are extracted as pseudo labels. A trainable label relation tensor is designed to explicitly explore the pairwise latent label relations and refine the predicted labels. SDRL is able to effectively and efficiently explore the feature-label relation as well as the label-label relation knowledge without any extra semantic knowledge. We evaluated SDRL in general and zero-shot multi-label classification tasks and we concluded that SDRL is superior to other SOTA baselines. Furthermore, extensive ablation studies have been done which reveal the effectiveness of each component in our framework.

Recently, deep convolutional neural networks have demonstrated remarkable progresses on single image super-resolution (SR) problem. However, most of them use more deeper and wider networks to improve SR performance, which is not practical in real-world applications due to large complexity, high computation cost, and low efficiency. In addition, they cannot provide high perception quality and guarantee objective quality simultaneously. To address these limitations, we in this paper propose a novel Adversarial Multipath Residual Network (AMPRN), which can largely suppress the number of network parameters and achieve a higher SR performance compared with the state-of-the-art methods. More specifically, we propose a multi-path residual block (MPRB) for multi-path residual network (MPRN) with fewer network parameters, which can extract abundant local features by fully using features from different paths generated by channel slices. These hierarchical features from all the MPRBs are then jointly aggregated by global gradual feature fusion. Following MPRN, we construct an adversarial gradient network with a gradient loss to make the gradient distribution of the generated SR images and ground truth image closer. In this way, the generated SR images of our model can provide high perception quality and objective quality. Finally, several experimental results demonstrate that our AMPRN achieves better performance in comparison with fewer parameters than the state-of-the-art methods.

The state-of-the-art multitask multiview (MTMV) learning tackles a scenario where multiple tasks are related to each other via multiple shared feature views. However, in many real-world scenarios where a sequence of the multiview task comes, the higher storage requirement and computational cost of retraining previous tasks with MTMV models have presented a formidable challenge for this lifelong learning scenario. To address this challenge, in this article, we propose a new continual multiview task learning model that integrates deep matrix factorization and sparse subspace learning in a unified framework, which is termed deep continual multiview task learning (DCMvTL). More specifically, as a new multiview task arrives, DCMvTL first adopts a deep matrix factorization technique to capture hidden and hierarchical representations for this new coming multiview task while accumulating the fresh multiview knowledge in a layerwise manner. Then, a sparse subspace learning model is employed for the extracted factors at each layer and further reveals cross-view correlations via a self-expressive constraint. For model optimization, we derive a general multiview learning formulation when a new multiview task comes and apply an alternating minimization strategy to achieve lifelong learning. Extensive experiments on benchmark data sets demonstrate the effectiveness of our proposed DCMvTL model compared with the existing state-of-the-art MTMV and lifelong multiview task learning models.

3D object classification has been widely applied in both academic and industrial scenarios. However, most state-of-the-art algorithms rely on a fixed object classification task set, which cannot tackle the scenario when a new 3D object classification task is coming. Meanwhile, the existing lifelong learning models can easily destroy the learned tasks performance, due to the unordered, large-scale, and irregular 3D geometry data. To address these challenges, we propose a Lifelong 3D Object Classification (i.e., L3DOC) model, which can consecutively learn new 3D object classification tasks via imitating "human learning". More specifically, the core idea of our model is to capture and store the cross-task common knowledge of 3D geometry data in a 3D neural network, named as point-knowledge, through employing layer-wise point-knowledge factorization architecture. Afterwards, a task-relevant knowledge distillation mechanism is employed to connect the current task to previous relevant tasks and effectively prevent catastrophic forgetting. It consists of a point-knowledge distillation module and a transforming-space distillation module, which transfers the accumulated point-knowledge from previous tasks and soft-transfers the compact factorized representations of the transforming-space, respectively. To our best knowledge, the proposed L3DOC algorithm is the first attempt to perform deep learning on 3D object classification tasks in a lifelong learning way. Extensive experiments on several point cloud benchmarks illustrate the superiority of our L3DOC model over the state-of-the-art lifelong learning methods.

The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider a lifelong learning problem to mimic "human learning," i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating the previous experiences. Therefore, we propose a new metric learning framework: lifelong metric learning (LML), which only utilizes the data of the new task to train the metric model while preserving the original capabilities. More specifically, the proposed LML maintains a common subspace for all learned metrics, named lifelong dictionary, transfers knowledge from the common subspace to learn each new metric learning task with task-specific idiosyncrasy, and redefines the common subspace over time to maximize performance across all metric tasks. For model optimization, we apply online passive aggressive optimization algorithm to achieve lifelong metric task learning, where the lifelong dictionary and task-specific partition are optimized alternatively and consecutively. Finally, we evaluate our approach by analyzing several multitask metric learning datasets. Extensive experimental results demonstrate effectiveness and efficiency of the proposed framework.

Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l 2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

Human embryonic stem cells (hESCs) are considered to be able to stably maintain their characteristics in vitro for prolonged periods, but we had previously encountered changes in proliferative ability and differentiation potential during extended culture of hESCs. Therefore, we investigated the proliferative ability and differentiation potential of hESCs during long-term culture. The hESCs, SNUhES3, were used to analyze population-doubling time, proliferation rate and differentiation potential. We classified hESCs into three groups according to culture period. Ten colonies of hESCs for each group were daily measured colony area and population-doubling time was assessed by the changes of colony area. Proliferation rate of hESCs was measured by 5-bromo-2'-deoxyuridine (BrdU) assay and telomerase activity. To evaluate differentiation potentials for hESCs, expression levels of undifferentiated and/or differentiated hESCs markers were examined by FACS, RT-PCR and immunostaining. Population-doubling time of early passage hESCs was longer than those of middle or late passage. Proliferative ability of hESCs was accelerated depending on culture periods. Cellular morphologies and the expression level of each three germ layer markers were obviously different from each passage of reattached embryoid bodies (EBs) after spontaneous differentiation. Differentiated cells of late passage expressed higher levels of undifferentiated markers such as Oct4 and SSEA4 than those of early and middle passage. But differentiated cells of early and middle passage expressed higher level of differentiated state markers, Nestin (ectoderm), Brachyury (mesoderm), HNF3beta (endoderm). From these results, it can be inferred that hESCs show higher proliferative abilities and reduced differentiation potentials as the passage number increased. Therefore, we conclude that early passage hESCs could be more suitable than middle and late passage hESCs in differentiation studies.

Seventy-three analogs of SJ-3366 (1-(3-cyclopenten-1-ylmethyl)-5-ethyl-6-(3,5-dimethylbenzoyl)-2,4(1H,3H)-pyrimidinedione) were synthesized and comparatively evaluated for their ability to inhibit the replication of human immunodeficiency virus type 1 (HIV-1) and HIV-2 and for their ability to suppress virus entry and reverse transcription. These studies were performed to identify inhibitors with activity greater than that of the current lead molecule (SJ-3366) and to utilize structure-activity relationships (SAR) to define the chemical features of the pyrimidinedione congeners responsible for their efficacy, toxicity, and dual mechanism of action against HIV. The results of our SAR evaluations have demonstrated that the addition of the homocyclic moiety at the N-1 of the pyrimidinedione results in acquisition of the ability to inhibit virus entry and extends the range of action of the compounds to include HIV-2. In addition, the results demonstrate that analogs with a methyl linker between the homocyclic substitution and the N-1 of the pyrimidinedione had a greater number of highly active molecules than those analogs possessing ethyl linkers. Six molecules were identified with activity equivalent to or greater than that of SJ-3366, and five additional molecules with highly potent inhibition of reverse transcriptase and virus entry and possessing high efficacy against both HIV-1 and HIV-2 were identified. Six molecules exhibited significant inhibition of viruses with the highly problematic nonnucleoside reverse transcriptase inhibitor (NNRTI) resistance engendering amino acid change K103N in the reverse transcriptase. These evaluations indicate that a new class of NNRTIs has been identified and that these NNRTIs possess highly potent inhibition of HIV-1 with an extended range of action, which now includes HIV-2.

Since the discovery of the 2,4 (1H,3H)-pyrimidinediones as potent non-nucleoside inhibitors of the HIV-1 reverse transcriptase (RT) this class of compounds has yielded a number of N-1 acyclic substituted pyrimidinediones with substantial antiviral activity, which is highly dependent upon their molecular fit into the binding pocket common to this inhibitory class. We have specifically examined the structure activity relationships of compounds with chemical modification made by substituting homocyclic rather than acyclic moieties at N-1 of the pyrimidinedione. Seventy-four compounds were synthesized and evaluated for antiviral activity against HIV-1 and HIV-2. The homocyclic modifications resulted in compounds with significant activity against both HIV-1 and HIV-2, suggesting these compounds represent a new class of non-nucleoside RT inhibitors. The structure-activity relationship (SAR) evaluations indicated that cyclopropyl, phenyl and 1- or 3-cyclopenten-1-yl substitutions at the N-1 of the pyrimidinedione, the addition of a methyl linker between the cyclic moiety and the N-1 and the addition of a benzoyl group at the C-6 of the pyrimidinedione had the greatest contribution to antiviral activity. Five pyrimidinedione analogues with therapeutic indexes (TIs) > 450,000 and a specific analogue (1-cyclopropylmethyl-5-isopropyl-6-(3,5-dimethylbenzoyl)-2,4(1H,3H)-pyrimidinedione), which exhibited a TI of > 2,000,000, were identified. None of the analogues were cytotoxic to target cells at the highest in vitro test concentration, which is the upper limit of compound solubility of the analogues in aqueous solution. Thus, we have identified a series of pyrimidinediones with substantially improved antiviral efficacy and range of action and with significantly reduced cellular cytotoxicity.

A new piperazine derivative, SJ-8026, is a synthetic anti-cancer agent which exhibits topoisomerase II-inhibiting activities. In this study, we investigated the possibility that this compound inhibits angiogenesis and induces tumor-cell apoptosis using bovine aortic endothelial cells (BAECs) and human hepatocellular carcinoma cells (HepG2) as a model system. in vivo, SJ-8026 decreased the neovascularization of chick embryos and the basic fibroblast growth factor-induced angiogenesis in the chorioallantoic membrane and the mouse Matrigel implants. in vitro, SJ-8026 treatment resulted in the inhibition of proliferation, migration, invasion and tube formation in BAECs. In addition, the treatment of SJ-8026 in HepG2 cells reduced the cell viability, and caused the production of fragmented DNA and the morphological changes corresponding to apoptosis including condensed and fragmented DNA. SJ-8026 also elicited the release of cytochrome c and the activation of caspase-3. Therefore, it is possible that SJ-8026 functions as both angiogenesis inhibitor and apoptosis inducer. Taken together, these results suggest that SJ-8026 may be a candidate for strong anti-cancer agent with the ability to inhibit the angiogenesis of endothelial cells and to induce the apoptosis of tumor cells.

A new piperazine derivative, SJ-8002, is a synthetic anti-cancer agent which exhibits microtubule-inhibiting activities. In this study, we investigated the possibility that this compound inhibits angiogenesis and induces tumor-cell apoptosis using bovine aortic endothelial cells (BAECs) and human hepatocellular carcinoma cells (HepG2) as a model system, respectively. In vivo, SJ-8002 decreased the neovascularization of chick embryos and the basic fibroblast growth factor (bFGF)-induced angiogenesis in the chorioallantoic membrane (CAM) and the mouse Matrigel implants, respectively. In vitro, SJ-8002 treatment resulted in the inhibition of proliferation, migration, invasion and tube formation, and of matrix metalloproteinase-2 (MMP-2) expression in BAECs. In addition, the SJ-8002 treatment in HepG2 cells reduced cell viability, and caused the production of fragmented DNA and the morphological changes corresponding to apoptosis including condensed and fragmented DNA in a concentration-dependent manner. SJ-8002 also elicited the release of cytochrome c and the activation of caspase-3. Therefore, it is possible that SJ-8002 functions as both angiogenesis inhibitor and apoptosis inducer. Taken together, these results suggest that SJ-8002 may be a candidate for strong anti-cancer agent with the ability to inhibit the angiogenesis of endothelial cells and to induce the apoptosis of tumor cells.

SJ compounds (SJ8002 and related compounds) are a group of novel anticancer agents (Cho, Chung, Lee, Kwon, Kang, Joo, and Oh. PCT/KR02/00392). To explore the anticancer mechanism of these compounds, we examined the effect of SJ8002 on microtubules of six human cell lines. At a high concentration (2 microg/mL), SJ8002 effectively disrupted microtubules of the six cell lines within 1 h. At lower concentrations (0.05 to approximately 1.0 microg/mL), the antimicrotubule activity of SJ8002 varied defending on cell lines. The inhibition of in vitro polymerization of pure tubulin by SJ8002 suggested that SJ8002 acts on free tubulin, inhibits the polymerization of tubulin dimer into microtubules, and hence induces the depolymerization of microtubules.