Lysosomal storage disease proteo/lipidomic profiling using nMOST links ferritinophagy with mitochondrial iron deficiencies in cells lacking NPC2.

Lysosomal storage diseases (LSDs) comprised ~50 monogenic diseases characterized by the accumulation of cellular material in lysosomes and associated defects in lysosomal function, but systematic molecular phenotyping is lacking. Here, we develop a nanoflow-based multi-omic single-shot technology (nMOST) workflow allowing simultaneously quantify HeLa cell proteomes and lipidomes from more than two dozen LSD mutants, revealing diverse molecular phenotypes. Defects in delivery of ferritin and its autophagic receptor NCOA4 to lysosomes (ferritinophagy) were pronounced in NPC2-/- cells, which correlated with increased lyso-phosphatidylcholine species and multi-lamellar membrane structures visualized by cryo-electron-tomography. Ferritinophagy defects correlated with loss of mitochondrial cristae, MICOS-complex components, and electron transport chain complexes rich in iron-sulfur cluster proteins. Strikingly, mitochondrial defects were alleviated when iron was provided through the transferrin system. This resource reveals how defects in lysosomal function can impact mitochondrial homeostasis in trans and highlights nMOST as a discovery tool for illuminating molecular phenotypes across LSDs.

Optimizing cell voltage dependence on size of carbon nanotube-based electrodes in Na-ion and K-ion batteries.

Sodium-ion batteries (NIBs) and potassium-ion batteries (KIBs) are gaining extensive attention as promising alternatives to lithium-ion batteries owing to their superior energy density and cost-effectiveness. However, the larger ionic radius of Na+ and K+ ions in comparison to Li+ ions poses a challenge in designing anode materials characterized by enduring structures and elevated voltage to facilitate the efficacy of high-performance NIBs and KIBs. Carbon nanomaterials, particularly carbon nanotubes (CNTs), have emerged as a potential candidate in anode materials. Herein, we used density functional theory calculations to study the cell voltage of CNTs in relation to Na-ion and K-ion storage as a function of CNT size. The adsorption energy profiles of both Na+@CNT and K+@CNT systems exhibit a descending trend concomitant with the increase in the CNT diameter, where Na+/K+ ion primarily prefers to adsorb in the interior wall of CNT. Conversely, the cell voltage for the Na and K system gradually increases with the increasing diameter of CNT, which can be attributed to the stronger electrostatic interaction validated by energy decomposition calculation. The voltage of Na-ion adsorbed on the inter wall of (10,10) CNT attains 1.29 V, close to the previously theoretical voltage of Li-ion on the same CNT (1.35 V), while the much lower voltage pertaining to K-ion adsorption on the inter wall of (10,10) CNT just stands at 0.59 V, suggesting the viability of CNT-based electrode for NIBs but not for KIBs. These findings lay a solid foundation for delineating the interrelationship between the voltage properties of CNT as prospective anode material and their structural characteristics, thereby expanding the application of CNT-based optoelectronic devices.

Antibacterial and self-healing sepiolite-based hybrid hydrogel for hemostasis and wound healing.

The process of wound healing necessitates a specific environment, thus prompting extensive research into the utilization of hydrogels for this purpose. While numerous hydrogel structures have been investigated, the discovery of a self-healing hydrogel possessing favorable biocompatibility, exceptional mechanical properties, and effective hemostatic and antibacterial performance remains uncommon. In this work, a polyvinyl alcohol (PVA) hybrid hydrogel was meticulously designed through a simple reaction, wherein CuxO anchored sepiolite was incorporated into the hydrogel. The results indicate that introduction of sepiolite greatly improves the toughness, self-healing and adhesion properties of the PVA hydrogels. CuxO nanoparticles endow the hydrogels with excellent antibacterial performance towards Staphylococcus aureus and Escherichia coli. The application of hybrid hydrogels for fast hemostasis and wound healing are verified in vitro and in vivo with rat experiments. This work thereby demonstrates an effective strategy for designing biodegradable hemostatic and wound healing materials.

Combinatorial selective ER-phagy remodels the ER during neurogenesis.

The endoplasmic reticulum (ER) employs a diverse proteome landscape to orchestrate many cellular functions, ranging from protein and lipid synthesis to calcium ion flux and inter-organelle communication. A case in point concerns the process of neurogenesis, where a refined tubular ER network is assembled via ER shaping proteins into the newly formed neuronal projections to create highly polarized dendrites and axons. Previous studies have suggested a role for autophagy in ER remodelling, as autophagy-deficient neurons in vivo display axonal ER accumulation within synaptic boutons, and the membrane-embedded ER-phagy receptor FAM134B has been genetically linked with human sensory and autonomic neuropathy. However, our understanding of the mechanisms underlying selective removal of the ER and the role of individual ER-phagy receptors is limited. Here we combine a genetically tractable induced neuron (iNeuron) system for monitoring ER remodelling during in vitro differentiation with proteomic and computational tools to create a quantitative landscape of ER proteome remodelling via selective autophagy. Through analysis of single and combinatorial ER-phagy receptor mutants, we delineate the extent to which each receptor contributes to both the magnitude and selectivity of ER protein clearance. We define specific subsets of ER membrane or lumenal proteins as preferred clients for distinct receptors. Using spatial sensors and flux reporters, we demonstrate receptor-specific autophagic capture of ER in axons, and directly visualize tubular ER membranes within autophagosomes in neuronal projections by cryo-electron tomography. This molecular inventory of ER proteome remodelling and versatile genetic toolkit provide a quantitative framework for understanding the contributions of individual ER-phagy receptors for reshaping ER during cell state transitions.

Effective treatment with Gilteritinib-based regimens for FLT3-mutant extramedullary relapse in acute promyelocytic leukemia.

OBJECTIVE: Extramedullary relapse (EMR) is rare in acute promyelocytic leukemia (APL) and, there is a lack of information on its management. Current practices for EMR in APL are always to adopt strategies from other subtypes of Acute lymphoblastic leukemia (ALL) and Acute myeloid leukemia (AML). Gilteritinib, a highly selective FLT3 inhibitor, has demonstrated a remarkable effect on EMR in FLT3-mutant AML. Therefore, it is worthwhile exploring if FLT3 mutation can be a therapeutic target and assessing the efficacy of Gilteritinib on FLT3-mutant EMR in APL. METHODS: We described three cases of FLT3-mutant EMR in APL, comprising two isolated EMR cases and one systemic relapse. The patients underwent treatment with Gilteritinib-based regimens based on FLT3 mutation. RESULTS: All three patients achieved complete regression of EMR, and no signs of tumor lysis syndrome during Gilteritinib-based therapy, only patient 1 showed mild granulocytopenia. They all maintained molecular complete remission (mCR) during the follow-up period. CONCLUSIONS: The Gilteritinib-based regimen shows a high and sustained therapeutic effect with minimal adverse effects, and provides a valuable experience for further evaluation in EMR APL patients.

Predicting anticancer synergistic drug combinations based on multi-task learning.

BACKGROUND: The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS: In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS: Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION: Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

Fine-Tuning Electrolyte Concentration and Metal-Organic Framework Surface toward Actuating Fast Zn2+ Dehydration for Aqueous Zn-Ion Batteries.

Functional porous coating on zinc electrode is emerging as a powerful ionic sieve to suppress dendrite growth and side reactions, thereby improving highly reversible aqueous zinc ion batteries. However, the ultrafast charge rate is limited by the substantial cation transmission strongly associated with dehydration efficiency. Here, we unveil the entire dynamic process of solvated Zn2+ ions' continuous dehydration from electrolyte across the MOF-electrolyte interface into channels with the aid of molecular simulations, taking zeolitic imidazolate framework ZIF-7 as proof-of-concept. The moderate concentration of 2 M ZnSO4 electrolyte being advantageous over other concentrations possesses the homogeneous water-mediated ion pairing distribution, resulting in the lowest dehydration energy, which elucidates the molecular mechanism underlying such concentration adopted by numerous experimental studies. Furthermore, we show that modifying linkers on the ZIF-7 surface with hydrophilic groups such as -OH or -NH2 can weaken the solvation shell of Zn2+ ions to lower the dehydration free energy by approximately 1 eV, and may improve the electrical conductivity of MOF. These results shed light on the ions delivery mechanism and pave way to achieve long-term stable zinc anodes at high capacities through atomic-scale modification of functional porous materials.

Combinatorial selective ER-phagy remodels the ER during neurogenesis.

The endoplasmic reticulum (ER) employs a diverse proteome landscape to orchestrate many cellular functions ranging from protein and lipid synthesis to calcium ion flux and inter-organelle communication. A case in point concerns the process of neurogenesis: a refined tubular ER network is assembled via ER shaping proteins into the newly formed neuronal projections to create highly polarized dendrites and axons. Previous studies have suggested a role for autophagy in ER remodeling, as autophagy-deficient neurons in vivo display axonal ER accumulation within synaptic boutons, and the membrane-embedded ER-phagy receptor FAM134B has been genetically linked with human sensory and autonomic neuropathy. However, our understanding of the mechanisms underlying selective removal of ER and the role of individual ER-phagy receptors is limited. Here, we combine a genetically tractable induced neuron (iNeuron) system for monitoring ER remodeling during in vitro differentiation with proteomic and computational tools to create a quantitative landscape of ER proteome remodeling via selective autophagy. Through analysis of single and combinatorial ER-phagy receptor mutants, we delineate the extent to which each receptor contributes to both magnitude and selectivity of ER protein clearance. We define specific subsets of ER membrane or lumenal proteins as preferred clients for distinct receptors. Using spatial sensors and flux reporters, we demonstrate receptor-specific autophagic capture of ER in axons, and directly visualize tubular ER membranes within autophagosomes in neuronal projections by cryo-electron tomography. This molecular inventory of ER proteome remodeling and versatile genetic toolkit provides a quantitative framework for understanding contributions of individual ER-phagy receptors for reshaping ER during cell state transitions.

Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

Safety and efficacy of an anti-human APC antibody for prophylaxis of congenital factor deficiencies in preclinical models.

Rebalance of coagulation and anticoagulation to achieve a hemostatic effect has recently gained attention as an alternative therapeutic strategy for hemophilia. We engineered a humanized chimeric antibody, SR604, based on a previously published murine antibody, HAPC1573, which selectively blocks the anticoagulant activity of human activated protein C (APC). SR604 effectively blocked the anticoagulation activities of APC in human plasma deficient in various coagulation factors in vitro with affinities ∼60 times greater than that of HAPC1573. SR604 exhibited prophylactic and therapeutic efficacy in the tail-bleeding and knee-injury models of hemophilia A and B mice expressing human APC (humanized hemophilic mice). SR604 did not interfere with the cytoprotection and endothelial barrier function of APC, nor were there obvious toxicity effects in humanized hemophilic mice. Pharmacokinetic study showed a high bioavailability (106%) of subcutaneously injected SR604 in cynomolgus monkeys. These results demonstrate that SR604 is expected to be a safe and effective therapeutic and/or prophylactic agent with a prolonged half-life for patients with congenital factor deficiencies including hemophilia A and B.

PARK15/FBXO7 is dispensable for PINK1/Parkin mitophagy in iNeurons and HeLa cell systems.

The protein kinase PINK1 and ubiquitin ligase Parkin promote removal of damaged mitochondria via a feed-forward mechanism involving ubiquitin (Ub) phosphorylation (pUb), Parkin activation, and ubiquitylation of mitochondrial outer membrane proteins to support the recruitment of mitophagy receptors. The ubiquitin ligase substrate receptor FBXO7/PARK15 is mutated in an early-onset parkinsonian-pyramidal syndrome. Previous studies have proposed a role for FBXO7 in promoting Parkin-dependent mitophagy. Here, we systematically examine the involvement of FBXO7 in depolarization and mt UPR-dependent mitophagy in the well-established HeLa and induced-neurons cell systems. We find that FBXO7-/- cells have no demonstrable defect in: (i) kinetics of pUb accumulation, (ii) pUb puncta on mitochondria by super-resolution imaging, (iii) recruitment of Parkin and autophagy machinery to damaged mitochondria, (iv) mitophagic flux, and (v) mitochondrial clearance as quantified by global proteomics. Moreover, global proteomics of neurogenesis in the absence of FBXO7 reveals no obvious alterations in mitochondria or other organelles. These results argue against a general role for FBXO7 in Parkin-dependent mitophagy and point to the need for additional studies to define how FBXO7 mutations promote parkinsonian-pyramidal syndrome.

Clinical characteristics of a patient with de novo acute promyelocytic leukemia with JAK2 v617f mutation.

BACKGROUND: The V617F mutation of Janus-associated kinase 2 (JAK2) is common in myeloproliferative neoplasms (MPN). JAK2 V617F mutation can be detected in patients with de novo acute myeloid leukemia (AML), but de novo acute promyelocytic leukemia (APL) with JAK2 V617F mutation is rare. CASE PRESENTATION: We report a case of APL with both the t(15;17) translocation as well as the JAK2 V617F mutation that transformed into MPN (PV/ET). CONCLUSIONS: A de novo APL patient presented initially with JAK2 V617F. After ATRA and ATO dual induction and chemotherapy consolidation, the patient achieved complete remission (CR) with undetectable PML/RARα. However, the JAK2 V617F remained positive, and the patient developed MPN (PV/ET) 22 months later, which responded well to interferon therapy.AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; ATRA, all-trans retinoic acid; ATO, arsenic trioxide; BM, bone marrow; CR, complete remission; ET, essential thrombocythemia; Hb, hemoglobin; JAK2, Janus-associated kinase 2; MPN, myeloproliferative neoplasms; PLT, platelets; PMF, primary myelofibrosis; PML/RARα; PV, polycythemia vera; WBC, white blood cells.

Double-Network Polymer Electrolytes with Ionic Liquids for Lithium Metal Batteries.

Solid-state polymer electrolytes have become promising candidates for high-energy-density lithium metal batteries (LMBs). However, they suffer from low ionic conductivities at room temperature. In this work, two types of composite polymer electrolytes based on a double-network polymer, an ionic liquid (IL) of 1-butyl-1-methylpyrrolidinium bis(trifluoromethanesulfonyl) imide (Pyr14TFSI) or 1-ethyl-3-methylimidazolium bis((trifluoromethyl)sulfonyl) imide (EmimTFSI), and bis(trifluoromethane)sulfonamide lithium salt (LiTFSI) were prepared by a facile one-pot method. The two types of CPEs possess good mechanical properties, excellent thermal stability, and high ionic conductivities greater than 10-4 S cm-1 at 20 °C with 26 wt% IL. The performance diversity of the CPEs was also carefully investigated through a series of electrochemical measurements. Although the CPEs containing EmimTFSI show higher ionic conductivities than those of CPEs with Pyr14TFSI, the latter ones have wider electrochemical stability windows and better resistance to the growth of lithium dendrites. Moreover, CPE with 34 wt% Pyr14TFSI leads to Li/LiFePO4 batteries with favorable rate capability and cycling stability and a columbic efficiency of 98.8% at 20 °C, which suggests that CPEs are promising for practical application in solid-state LMBs.

Progerin-Induced Impairment in Wound Healing and Proliferation in Vascular Endothelial Cells.

Progerin as a mutated isoform of lamin A protein was first known to induce premature atherosclerosis progression in patients with Hutchinson-Gilford progeria syndrome (HGPS), and its role in provoking an inflammatory response in vascular cells and accelerating cell senescence has been investigated recently. However, how progerin triggers endothelial dysfunction that often occurs at the early stage of atherosclerosis in a mechanical environment has not been studied intensively. Here, we generated a stable endothelial cell line that expressed progerin and examined its effects on endothelial wound repair under laminar flow. We found decreased wound healing rate in progerin-expressing ECs under higher shear stress compared with those under low shear. Furthermore, the decreased wound recovery could be due to reduced number of cells at late mitosis, suggesting potential interference by progerin with endothelial proliferation. These findings provided insights into how progerin affects endothelial mechanotransduction and may contribute to the disruption of endothelial integrity in HGPS vasculature, as we continue to examine the mechanistic effect of progerin in shear-induced endothelial functions.

Blocking human protein C anticoagulant activity improves clotting defects of hemophilia mice expressing human protein C.

Hemophilia A and B are hereditary coagulation defects resulting in unstable blood clotting and recurrent bleeding. Current factor replacement therapies have major limitations such as the short half-life of the factors and development of inhibitors. Alternative approaches to rebalance the hemostasis by inhibiting the anticoagulant pathways have recently gained considerable interest. In this study, we tested the therapeutic potential of a monoclonal antibody, HAPC1573, that selectively blocks the anticoagulant activity of human activated protein C (APC). We generated F8-/- or F9-/- hemophilia mice expressing human protein C by genetically replacing the murine Proc gene with the human PROC. The resulting PROC+/+;F8-/- or PROC+/+;F9-/- mice had bleeding characteristics similar to their corresponding F8-/- or F9-/- mice. Pretreating the PROC+/+;F8-/- mice with HAPC1573 shortened the tail bleeding time. HAPC1573 pretreatment significantly reduced mortality and alleviated joint swelling, similar to those treated with either FVIII or FIX, of either PROC+/+;F8-/- or PROC+/+;F9-/- mice in a needle puncture-induced knee-joint bleeding model. Additionally, we found that HAPC1573 significantly improved the thrombin generation of PROC+/+;F8-/- mice but not F8-/- mice, indicating that HAPC1573 enhanced the coagulant activity of hemophilia mice by modulating human APC in vivo. We further documented that HAPC1573 inhibited the APC anticoagulant activity to improve the clotting time of human plasma deficient of FVIII, FIX, FXI, FVII, VWF, FV, or FX. These results demonstrate that selectively blocking the anticoagulant activity of human APC may be an effective therapeutic and/or prophylactic approach for bleeding disorders lacking FVIII, FIX, or other clotting factors.

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover new synergistic drug combinations due to the combinatorial explosion. Deep learning technology holds immense promise for better prediction of in vitro synergistic drug combinations for certain cell lines. In methods applying such technology, omics data are widely adopted to construct cell line features. However, biological network data are rarely considered yet, which is worthy of in-depth study. In this study, we propose a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations. By leveraging the Graph Convolutional Network, PRODeepSyn integrates the protein-protein interaction (PPI) network with omics data to construct low-dimensional dense embeddings for cell lines. PRODeepSyn then builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features. PRODeepSyn achieves the lowest root mean square error of 15.08 and the highest Pearson correlation coefficient of 0.75, outperforming two deep learning methods and four machine learning methods. On the classification task, PRODeepSyn achieves an area under the receiver operator characteristics curve of 0.90, an area under the precision-recall curve of 0.63 and a Cohen's Kappa of 0.53. In the ablation study, we find that using the multi-omics data and the integrated PPI network's information both can improve the prediction results. Additionally, the case study demonstrates the consistency between PRODeepSyn and previous studies.

Pregnancy-associated thrombotic thrombocytopenic purpura complicated by Sjögren's syndrome and non-neutralising antibodies to ADAMTS13: a case report.

BACKGROUND: Thrombotic thrombocytopenic purpura (TTP) is a severe and life-threatening disease. Given its heterogeneous clinical presentation, the phenotype of TTP during pregnancy and its management have not been well documented. CASE PRESENTATION: We report here a 25-year-old woman, G1P0 at 36 weeks gestation, who developed severe thrombocytopenia and anemia. She was performed an emergent caesarean section 1 day after admission because of multiple organ failure. As ADAMTS 13 enzyme activity of the patient was 0% and antibodies were identified by enzyme-linked immunosorbent assay, she was diagnosed as acquired thrombotic thrombocytopenic purpura (aTTP). Furthermore, asymptomatic primary Sjögren's syndrome was incidentally diagnosed on screening. After treatment with rituximab in addition to PEX and steroids, the activity of the ADAMTS 13 enzyme increased significantly from 0 to 100%. CONCLUSIONS: To the best of our knowledge, this is the first case report of concomitant TTP and asymptomatic Sjögren's syndrome in a pregnant woman. It highlights the association between pregnancy, autoimmune disease, and TTP. It also emphasizes the importance of an enzyme-linked immunosorbent assay in the diagnosis and rituximab in the treatment of patients with acquired TTP.

Durable Cord Blood Cell Engraftment in a Patient with Severe Aplastic Anemia after a Matched Sibling Bone Marrow Transplantation and an Unrelated Cord Blood Transplantation.

OBJECTIVE: Severe aplastic anemia (SAA) is a fatal bone marrow failure disease. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) with a matched sibling donor is the first-line treatment for older SAA patients. However, the number of CD34+ cells collected from a matched donor is often lower than expected. To overcome the problem, this study was conducted to combine a matched sibling donor with an unrelated cord blood transplantation for the treatment of a patient with SAA. CASE REPORT: A 45-year-old male patient with SAA was treated with a sibling-matched allo-HSCT. Due to the low amount of donor CD34+ cells, an unrelated umbilical cord blood stem cell transplantation (UCBT) with 9/10 HLA matching was subsequently carried out. Successful hematopoietic reconstitution was achieved by the dual transplantation. Unexpectedly, beginning in the fourth month after transplantation, the sibling donor chimerism was transformed to a stable and complete UCB source. CONCLUSION: This study provides evidence that UCB-derived HSCs have a higher capacity for hematopoietic reconstitution, suggesting that UCB plus an HLA-matched sibling donor is a good alternative for older patients with SAA.

FTRLIM: Distributed Instance Matching Framework for Large-Scale Knowledge Graph Fusion.

Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects' features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.

Kupffer cell receptor CLEC4F is important for the destruction of desialylated platelets in mice.

The liver has recently been identified as a major organ for destruction of desialylated platelets. However, the underlying mechanism remains unclear. Kupffer cells, which are professional phagocytic cells in the liver, comprise the largest population of resident tissue macrophages in the body. Kupffer cells express a C-type lectin receptor, CLEC4F, that recognizes desialylated glycans with an unclear in vivo role in mediating platelet destruction. In this study, we generated a CLEC4F-deficient mouse model (Clec4f-/-) and found that CLEC4F was specifically expressed by Kupffer cells. Using the Clec4f-/- mice and a newly generated platelet-specific reporter mouse line, we revealed a critical role for CLEC4F on Kupffer cells in mediating destruction of desialylated platelets in the liver in vivo. Platelet clearance experiments and ultrastructural analysis revealed that desialylated platelets were phagocytized predominantly by Kupffer cells in a CLEC4F-dependent manner in mice. Collectively, these findings identify CLEC4F as a Kupffer cell receptor important for the destruction of desialylated platelets induced by bacteria-derived neuraminidases, which provide new insights into the pathogenesis of thrombocytopenia in disease conditions such as sepsis.