Identification of a distinct cluster of GDF15high macrophages induced by in vitro differentiation exhibiting anti-inflammatory activities.

Introduction: Macrophage-mediated inflammatory response may have crucial roles in the pathogenesis of a variety of human diseases. Growth differentiation factor 15 (GDF15) is a cytokine of the transforming growth factor-ß superfamily, with potential anti-inflammatory activities. Previous studies observed in human lungs some macrophages which expressed a high level of GDF15. Methods: In the present study, we employed multiple techniques, including immunofluorescence, flow cytometry, and single-cell RNA sequencing, in order to further clarify the identity of such GDF15high macrophages. Results: We demonstrated that macrophages derived from human peripheral blood mononuclear cells and rat bone marrow mononuclear cells by in vitro differentiation with granulocyte-macrophage colony stimulating factor contained a minor population (~1%) of GDF15high cells. GDF15high macrophages did not exhibit a typical M1 or M2 phenotype, but had a unique molecular signature as revealed by single-cell RNA sequencing. Functionally, the in vitro derived GDF15high macrophages were associated with reduced responsiveness to pro-inflammatory activation; furthermore, these GDF15high macrophages could inhibit the pro-inflammatory functions of other macrophages via a paracrine mechanism. We further confirmed that GDF15 per se was a key mediator of the anti-inflammatory effects of GDF15high macrophage. Also, we provided evidence showing that GDF15high macrophages were present in other macrophage-residing human tissues in addition to the lungs. Further scRNA-seq analysis in rat lung macrophages confirmed the presence of a GDF15high sub-population. However, these data indicated that GDF15high macrophages in the body were not a uniform population based on their molecular signatures. More importantly, as compared to the in vitro derived GDF15high macrophage, whether the tissue resident GDF15high counterpart is also associated with anti-inflammatory functions remains to be determined. We cannot exclude the possibility that the in vitro priming/induction protocol used in our study has a determinant role in inducing the anti-inflammatory phenotype in the resulting GDF15high macrophage cells. Conclusion: In summary, our results suggest that the GDF15high macrophage cells obtained by in vitro induction may represent a distinct cluster with intrinsic anti-inflammatory functions. The (patho)physiological importance of these cells in vivo warrants further investigation.

Electrolyte Regulation in Stabilizing the Interface of a Cobalt-Free Layered Cathode for 4.8 V High-Voltage Lithium-Ion Batteries.

The cobalt-free layered oxide cathode of LiNi0.65Mn0.35O2 is promising for high-energy-density lithium-ion batteries (LIBs). However, under high-voltage conditions, severe side reactions between the Co-free cathode and electrolyte, as well as grain boundary cracks and pulverization of particles, hinder its practical applications. Herein, an electrolyte regulation strategy is proposed by adding fluoroethylene carbonate (FEC) and LiPO2F2 as electrolyte additives in carbonate-based electrolytes to address the above issues. As a result, a homogeneous and dense organic-inorganic hybrid cathode electrolyte interface (CEI) film is formed on the cathode surface. The CEI film consists of C-F, LiF, Li2CO3, and LixPOyFz species, which is proven to be highly conductive and effective in suppressing structure damage and alleviating the interfacial reactions between the cathode and electrolyte. With such a CEI film, the interfacial stability of the Co-free cathode and the high-voltage cycling performance of Li||LiNi0.65Mn0.35O2 are greatly improved. A reversible capacity of 155.1 mAh g-1 and a capacity retention of 81.3% over 150 cycles are attained at a 4.8 V charge cutoff voltage with the tamed electrolyte, whereas the cell without the additives only retains 76.1% capacity retention. Therefore, our work demonstrates the synergistic effect of FEC and LiPO2F2 in stabilizing the interface of Co-free cathode materials and provides an alternative strategy for the electrolyte design of high-voltage LIBs.

Discovery of 4-((3,4-dichlorophenyl)amino)-2-methylquinolin-6-ol derivatives as EGFR and HDAC dual inhibitors.

In patients with non-small cell lung cancer (NSCLC), the standard therapy consists of selective tyrosine kinase inhibitors that target epidermal growth factor receptors (EGFR). Nonetheless, their clinical utility is primarily limited by the development of resistance to drugs. HDAC inhibitors have been shown in studies to reduce the level of EGFR that is expressed and downregulate the EGFR-induced phosphorylation of AKT and ERK. Therefore, dual inhibitors of EGFR and HDAC provide a potential approach as combination treatment synergistically inhibited the growth of NSCLC. Herein, we examined the EGFR inhibition effect of twenty compounds which designed and synthesized by us previously. Among them, compounds 12c and 12d exhibited powerful antiproliferative activity against the NCI-H1975 cell line with IC50 values of 0.48 ± 0.07 and 0.35 ± 0.02 µM, correspondingly. In cell-free kinase assays, both 12c and 12d demonstrated target-specific EGFR inhibition against wild type (EGFRwt). Furthermore, the expression of EGFR and phosphorylation of the EGF-induced pathways were significantly suppressed under the treatment of 12c and 12d. Besides, both histones H3 and H4 exhibited increased levels of acetylation following 12c and 12d treatment. The animal experiments shown that 12d could prevent the growth of tumor, inhibited the expression of EGFR and the phosphorylation levels of p70 S6K, AKT and p38 MAPK in vivo, and did not cause organ damage to the mice during the experiment. Overall, the results illustrated that compound 12c and 12d could serve as effective EGFR and HDAC dual inhibitors in NSCLC cells. Our work offers an alternative strategy for NSCLC therapy.

Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.

EEG decoding based on motor imagery is an important part of brain-computer interface technology and is an important indicator that determines the overall performance of the brain-computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain-computer interface technology research.

DNA Origami Nanostructure Detection and Yield Estimation Using Deep Learning.

DNA origami is a milestone in DNA nanotechnology. It is robust and efficient in constructing arbitrary two- and three-dimensional nanostructures. The shape and size of origami structures vary. To characterize them, an atomic force microscope, a transmission electron microscope, and other microscopes are utilized. However, the identification of various origami nanostructures heavily depends on the experience of researchers. In this study, we used the deep learning method (improved Yolox) to detect multiple DNA origami structures and estimate their yield. We designed a feature enhancement fusion network with the attention mechanism, and related parameters were researched. Experiments conducted to verify the proposed method showed that the detection accuracy was higher than that of other methods. This method can detect and estimate the DNA origami yield in complex environments, and the detection speed is in the millisecond range.

Developing and validating a prognostic nomogram for ovarian clear cell carcinoma patients: A retrospective comparison of lymph node staging schemes with competing risk analysis.

Purpose: Lymph node (LN) involvement is a key factor in ovarian clear cell carcinoma (OCCC) although, there several indicators can be used to define prognosis. This study examines the prognostic performances of each indicator for OCCC patients by comparing the number of lymph nodes examined (TNLE), the number of positive lymph nodes (PLN), lymph node ratio (LNR), and log odds of metastatic lymph nodes (LODDS). Methods: 1,300 OCCC patients who underwent lymphadenectomy between 2004 and 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Primary outcomes were Overall Survival (OS) and the cumulative incidence of Cancer-Specific Survival (CSS). Kaplan-Meier's and Fine-Gray's tests were implemented to assess OS and CSS rates. After conducting multivariate analysis, nomograms using OS and CSS were constructed based upon an improved LN system. Each nomograms' performance was assessed using Receiver Operating Characteristics (ROC) curves, calibration curves, and the C-index which were compared to traditional cancer staging systems. Results: Multivariate Cox's regression analysis was used to assess prognostic factors for OS, including age, T stage, M stage, SEER stage, and LODDS. To account for the CSS endpoint, a proportional subdistribution hazard model was implemented which suggested that the T stage, M stage, SEER stage, and LNR are all significant. This enabled us to develop a LODDS-based nomogram for OS and a LNR-based nomogram for CSS. C-indexes for both the OS and CSS nomograms were higher than the traditional American Joint Committee on Cancer (AJCC), 8th edition, staging system. Area Under the Curve (AUC) values for predicting 3- and 5-year OS and CSS between nomograms also highlighted an improvement upon the AJCC staging system. Calibration curves also performed with consistency, which was verified using a validation cohort. Conclusions: LODDS and LNR may be better predictors than N stage, TNLE, and PLNs. For OCCC patients, both the LODDS-based and LNR-based nomograms performed better than the AJCC staging system at predicting OS and CSS. However, further large sample, real-world studies are necessary to validate the assertion.

Astragaloside IV ameliorates sepsis-induced myocardial dysfunction by regulating NOX4/JNK/BAX pathway.

AIMS: Sepsis can induce multiple organ dysfunction, and sepsis-induced myocardial dysfunction (SIMD) is relatively common. The current dilemma might ascribe partly to SIMD's lack of unified molecular mechanisms. Our study aims to assess the function of Astragaloside IV (ASI) in cecal ligation and puncture (CLP)-induced cardiac dysfunction and explore its underlying mechanisms. MAIN METHODS: In vivo, ASI (30 mg/kg/day), NADPH oxidase 4 (NOX4) inhibitor 4-hydroxy-3-methoxyacetophenone (APO, 30 mg/kg/day), reactive oxygen species (ROS) inhibitor N-Acetylcysteine (NAC, 150 mg/kg/day) and c-Jun NH2-terminal kinase (JNK) inhibitor (SP600125, 15 mg/kg/day) were severally administered to Sprague Dawley rats following the CLP surgery. The cardiac function, cardiac enzyme markers, proinflammatory cytokine, and cell apoptosis-associated proteins were detected. In vitro, cardiomyocyte H9C2 cells were treated with lipopolysaccharide (LPS, 40 µg/ml) after the presence of ASI (100 µmol/ml), SP600125 (10 µmol/ml), APO (10 µmol/ml). A series of experiments verified the relationship among NOX4, JNK, and BAX. KEY FINDINGS: The results indicated that CLP-induced sepsis increased the secretion of creatine kinase isoenzymes (CKMB), brain natriuretic peptide (BNP), cardiac troponin T (c-TnI), interleukin-1ß (IL-1ß) and interleukin-18 (IL-18), as well as the protein expression of NOX4 and Caspase-3 in vivo. LPS increased the protein level of NOX4 and Caspase-3, upregulated the rate of p-JNK/JNK, and downregulated the rate of Bcl2/BAX in vitro. ASI can reverse these changes in vivo and has a synergistic effect with APO and SP600125 in vitro. SIGNIFICANCE: This study suggested that ASI may ameliorate SIMD, through regulating NOX4/JNK/BAX signaling pathway, which may be a feasible therapeutic strategy.

The BCL2/BAX/ROS pathway is involved in the inhibitory effect of astragaloside IV on pyroptosis in human umbilical vein endothelial cells.

CONTEXT: Astragaloside IV (AS-IV) is extracted from Astragalus membranaceus (Fisch.) Bunge (Fabaceae). However, its effects on endothelial cell injury remain unclear. OBJECTIVE: To investigate the mechanisms underlying the effects of AS-IV on lipopolysaccharide (LPS)-induced endothelial injury in vitro. MATERIALS AND METHODS: Human umbilical vein endothelial cells (HUVECs) were pre-treated with AS-IV (100 µmol/mL), 4-hydroxy-3-methoxyacetophenone (APO, 10 µmol/mL), N-acetylcysteine (NAC, 50 µmol/mL) and Ac-YVAD-cmk (AC, 5 µmol/mL) for 2 h before 1 µg/mL LPS 24 h exposure. Untreated cells cultured without any exposure were used as controls. Cell viability, reactive oxygen species (ROS) and pyroptosis assays were performed. The pyroptosis related proteins were detected by western blot. RESULTS: The rate in late pyroptosis (Q2-2) of AS-IV (13.65 ± 0.74%), APO (13.69 ± 0.67%) and NAC (15.87 ± 0.46%) groups was lower than the LPS group (21.89 ± 0.66%, p < 0.05), while the rate in early pyroptosis (Q2-4) of AS-IV group (12.00 ± 0.26%) was lower than other groups (p < 0.05). The expression of NOX4, GSDMD, NLRP3, ASC and caspase-1 decreased after AS-IV, NAC or AC intervention (p < 0.05). The ROS production in AS-IV (4664 ± 153.20), APO (4094 ± 78.37), NAC (5103 ± 131.10) and AC (3994 ± 102.50) groups was lower than the LPS (5986 ± 127.30) group, while the mitochondrial BCL2/BAX protein expression ratio increased in AS-IV, APO and NAC groups (p < 0.05). DISCUSSION AND CONCLUSIONS: AS-IV suppressed pyroptosis in LPS-activated HUVECs by inducing ROS/NLRP3-mediated inhibition of the inflammatory response, providing a scientific basis for clinical applications of AS-IV.

SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.

Increasing evidence has suggested that microRNAs (miRNAs) are important biomarkers of various diseases. Numerous graph neural network (GNN) models have been proposed for predicting miRNA-disease associations. However, the existing GNN-based methods have over-smoothing issue-the learned feature embeddings of miRNA nodes and disease nodes are indistinguishable when stacking multiple GNN layers. This issue makes the performance of the methods sensitive to the number of layers, and significantly hurts the performance when more layers are employed. In this study, we resolve this issue by a novel self-feature-based graph autoencoder model, shortened as SFGAE. The key novelty of SFGAE is to construct miRNA-self embeddings and disease-self embeddings, and let them be independent of graph interactions between two types of nodes. The novel self-feature embeddings enrich the information of typical aggregated feature embeddings, which aggregate the information from direct neighbors and hence heavily rely on graph interactions. SFGAE adopts a graph encoder with attention mechanism to concatenate aggregated feature embeddings and self-feature embeddings, and adopts a bilinear decoder to predict links. Our experiments show that SFGAE achieves state-of-the-art performance. In particular, SFGAE improves the average AUC upon recent GAEMDA [1] on the benchmark datasets HMDD v2.0 and HMDD v3.2, and consistently performs better when less (e.g. 10%) training samples are used. Furthermore, SFGAE effectively overcomes the over-smoothing issue and performs stably well on deeper models (e.g. eight layers). Finally, we carry out case studies on three human diseases, colon neoplasms, esophageal neoplasms and kidney neoplasms, and perform a survival analysis using kidney neoplasm as an example. The results suggest that SFGAE is a reliable tool for predicting potential miRNA-disease associations.

Bifunctional WC-Supported RuO2 Nanoparticles for Robust Water Splitting in Acidic Media.

We report the strong catalyst-support interaction in WC-supported RuO2 nanoparticles (RuO2 -WC NPs) anchored on carbon nanosheets with low loading of Ru (4.11 wt.%), which significantly promotes the oxygen evolution reaction activity with a η10 of 347 mV and a mass activity of 1430 A gRu -1 , eight-fold higher than that of commercial RuO2 (176 A gRu -1 ). Theoretical calculations demonstrate that the strong catalyst-support interaction between RuO2 and the WC support could optimize the surrounding electronic structure of Ru sites to reduce the reaction barrier. Considering the likewise excellent catalytic ability for hydrogen production, an acidic overall water splitting (OWS) electrolyzer with a good stability constructed by bifunctional RuO2 -WC NPs only requires a cell voltage of 1.66 V to afford 10 mA cm-2 . The unique 0D/2D nanoarchitectures rationally combining a WC support with precious metal oxides provides a promising strategy to tradeoff the high catalytic activity and low cost for acidic OWS applications.

Protection of toll-like receptor 9 against lipopolysaccharide-induced inflammation and oxidative stress of pulmonary epithelial cells via MyD88-mediated pathways.

Acute lung injury (ALI) caused by lipopolysaccharide (LPS) is a common, severe clinical syndrome. Injury caused by inflammation and oxidative stress in vascular endothelial and alveolar epithelial cells is a vital process in the pathogenesis of ALI. Toll-like receptor 9 (TLR9) is highly expressed in LPS-induced ALI rats. In this study, Beas-2B human pulmonary epithelial cells and A549 alveolar epithelial cells were stimulated by LPS, resulting in the upregulation of TLR9 in a concentrationdependent manner. Furthermore, TLR9 overexpression and interference vectors were transfected before LPS administration to explore the role of TLR9 in LPS-induced ALI in vitro. The findings revealed that inhibition of TLR9 reduced inflammation and oxidative stress while suppressing apoptosis of LPS-induced Beas-2B and A549 cells, whereas TLR9 overexpression aggravated these conditions. Moreover, TLR9 inhibition resulted in downregulated protein expression of myeloid differentiation protein 88 (MyD88) and activator activator protein 1 (AP-1), as well as phosphorylation of nuclear factor-?B (NF-kappaB), c-Jun N-terminal kinase (JNK), and p38 mitogen-activated protein kinase (MAPK). The phosphorylation of extracellular-regulated protein kinases 1/2 was upregulated compared to that of cells subjected to only LPS administration, and this was reversed by TLR9 overexpression. These results indicate that inhibition of TLR9 plays a protective role against LPS-induced inflammation and oxidative stress in Beas-2B and A549 cells, possibly via the MyD88/NF-kappaB and MyD88/MAPKs/AP-1 pathways.

Percutaneous In Situ Microneedle Puncture Fenestration via the Left Subclavian Artery for branched Thoracic Endovascular Aortic Repair.

This study explored the feasibility of a totally percutaneous approach to perform in situ microneedle puncture fenestration during thoracic endovascular aortic repair (TEVAR) via access from the left subclavian artery (LSA). In total, 23 patients with either thoracic aortic dissection or thoracic aortic aneurysm were treated with in situ LSA fenestration during TEVAR. The procedure was technically successful in all the patients. No serious complications occurred during a mean 9-month follow-up period. In situ microneedle puncture fenestration during TEVAR via the LSA is a feasible and effective method for LSA reconstruction.

Self-assembly of polyhedral bilayer vesicles from Piezo ion channels.

Piezo ion channels underlie many forms of mechanosensation in vertebrates and have been found to bend the membrane into strongly curved dome shapes. We develop a methodology describing the self-assembly of lipids and Piezo proteins into polyhedral bilayer vesicles. We validate this methodology for bilayer vesicles formed from bacterial mechanosensitive channels of small conductance, for which experiments found a polyhedral arrangement of proteins with snub cube symmetry and a well-defined characteristic vesicle size. On this basis, we calculate the self-assembly diagram for polyhedral bilayer vesicles formed from Piezo proteins. We find that the radius of curvature of the Piezo dome provides a critical control parameter for the self-assembly of Piezo vesicles, with high abundances of Piezo vesicles with octahedral, icosahedral, and snub cube symmetry with increasing Piezo dome radius of curvature.

Programmable and scalable assembly of a flexible hexagonal DNA origami.

Nanoscale structures demonstrate considerable potential utility in the construction of nanorobots, nanomachines, and many other devices. In this study, a hexagonal DNA origami ring was assembled and visualized via atomic force microscopy. The DNA origami shape could be programmed into either a hexagonal or linear shape with an open or folded pattern. The flexible origami was robust and switchable for dynamic pattern recognition. Its edges were folded by six bundles of DNA helices, which could be opened or folded in a honeycomb shape. Additionally, the edges were programmed into a concave-convex pattern, which enabled linkage between the origami and dipolymers. Furthermore, biotin-streptavidin labels were embedded at each edge for nanoscale calibration. The atomic force microscopy results demonstrated the stability and high-yield of the flexible DNA origami ring. The polymorphous nanostructure is useful for dynamic nano-construction and calibration of structural probes or sensors.

The Effect of Xinmailong Infusion on Sepsis-Induced Myocardial Dysfunction: a Pragmatic Randomized Controlled Trial.

ABSTRACT: Sepsis-induced myocardial dysfunction (SIMD) contributes significantly to cardiovascular dysfunction during septic shock. We aimed to evaluate the potential role of Xinmailong injection (XMLI), a polypeptide medicine extracted from Periplaneta americana, in reversing the progression of myocardial damage to SIMD in sepsis patients. This was a multicenter, randomized, double-blind, parallel-group trial. We recruited all patients consecutively admitted to intensive care units (ICUs) who were aged 18 to 85 years old and met the sepsis 3.0 criteria. The primary outcome measure was the incidence of sepsis-induced myocardial dysfunction while in the ICU. Of the 192 patients, 96 were assigned to the treatment group, and 96 to the control group. Subsequently, 41 patients [41/96 (42.7%)] in the XMLI group and 61 patients in the placebo group [61/96 (63.5%)] were confirmed to have diastolic dysfunction on the fifth day (D5). The incidence of diastolic SIMD was significantly different between the two groups (P = 0.004). There were 36 deaths in the two groups during the 28-day follow-up, with a general mortality rate of 18.8% (36/192). The 28-day mortality rates were not significantly different between the groups (P = 0.45). However, the brain natriuretic peptide (BNP) plasma concentration trends on D0, D2, and D5 significantly differed between the two groups (P = 0.049). In septic patients, XMLI decreased the occurrence rate of diastolic SIMD more effectively than the placebo. The improvement in serum BNP concentration was also greater in the XMLI group. XMLI may, therefore, effectively and safely improve cardiac function in patients with sepsis.

Hyperlipidaemic acute pancreatitis complicated with multiple deep vein thromboses and pulmonary embolism: a case successfully salvaged by radiologic intervention.

Acute pancreatitis complicated with pulmonary embolism has been described in literature, however, hyperlipidaemic acute pancreatitis complicated with pulmonary embolism and deep vein thrombosis has rarely been reported. We reported here a rare case of hyperlipidaemic acute pancreatitis. Although he had undergone plasmapheresis and his TG level reduced to normal range with symptoms relieved, he developed pulmonary embolism and multiple deep vein thromboses. The patient was diagnosed early and successfully salvaged by interventional radiology and oral anticoagulants. The patient was a 51-year-old man, he experienced a sudden upper abdomen pain for 24 h before being admitted to a local hospital where diagnosis of acute pancreatitis was made, and he had no relief of the symptoms after treatment. The patient was a non-smoker and did not consume alcohol. He had no history of diabetes, gallstones or cholelithasis. After transferring to our unit, the patient was treated with plasmapheresis along with low molecular weight heparin, insulin, antibiotics and proton pump inhibitors and the abdomen pain was alleviated gradually. However, 8 days after admission, the patient developed a sudden chest tightness and shortness of breath. Examination revealed a high level of D-dimer (16700 ug/L), a computer tomography angiography of chest revealed pulmonary embolism. Urokinase was started intravenously. Pulmonary angiography and venography demonstrated pulmonary embolism and extensive lower limb deep vein thrombosis. Catheter directed thrombolysis and urokinase was initiated through catheter followed by an IVC filter implantation. Dyspnea of the patient got well with thrombolytic treatment and anticoagulation therapy. This is a rare case of hyperlipidaemic acute pancreatitis complicated pulmonary embolism and Deep vein thrombosis even after treated with plasmapheresis. The case we present here will aid in its early recognition, interventional radiology hence the prevention for this rare but catastrophic complication.

Effect of protein steric constraints on the symmetry of membrane protein polyhedra.

Experiments have shown that, in an aqueous environment, lipids and membrane proteins can self-assemble into membrane protein polyhedral nanoparticles (MPPNs). MPPNs are closed, spherical vesicles composed of a lipid bilayer membrane and membrane proteins, with a polyhedral arrangement of membrane proteins. The observed symmetry and size of MPPNs can be understood from the interplay of protein-induced lipid bilayer deformations in MPPNs, topological defects in protein packing necessitated by the spherical shape of MPPNs, and thermal fluctuations in MPPN self-assembly. We explore here the effect of protein steric constraints on MPPN shape. The protein steric constraints considered here may arise from a well-defined shape of protein domains outside the membrane, entropic repulsion between membrane proteins with flexible domains outside the membrane, or binding of other molecules to membrane proteins. Calculating MPPN self-assembly diagrams under protein steric constraints we find that protein steric constraints can strongly affect MPPN self-assembly. Depending on the specific scenario considered, protein steric constraints can leave large portions of the MPPN self-assembly diagrams with no clearly defined MPPN symmetry or substantially expand the regions of MPPN self-assembly diagrams dominated by highly symmetric MPPN states, such as MPPNs with icosahedral or snub cube symmetry. Our results suggest that modification of protein steric constraints may allow the directed self-assembly of MPPNs with specified symmetry, size, and protein composition and may thus facilitate the further utilization of MPPNs for membrane protein structural analysis or targeted drug delivery.

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.

Symmetry of membrane protein polyhedra with heterogeneous protein size.

In experiments on membrane protein polyhedral nanoparticles (MPPNs) [Basta et al., Proc. Natl. Acad. Sci. USA 111, 670 (2014)PNASA60027-842410.1073/pnas.1321936111], it has been observed that membrane proteins and lipids can self-assemble into closed lipid bilayer vesicles with a polyhedral arrangement of membrane proteins. In particular, MPPNs formed from the mechanosensitive channel of small conductance (MscS) were found to have the symmetry of the snub cube-a chiral, Archimedean solid-with one MscS protein located at each one of the 24 vertices of the snub cube. It is currently unknown whether MPPNs with heterogeneous protein composition maintain a high degree of symmetry. Inspired by previous work on viral capsid symmetry, we employ here computational modeling to study the symmetry of MPPNs with heterogeneous protein size. We focus on MPPNs formed from MscS proteins, which can exist in closed or open conformational states with distinct sizes. We find that, as an increasing number of closed-state MscS proteins transitions to the open conformational state of MscS, the minimum-energy MscS arrangement in MPPNs follows a strikingly regular pattern, with the dominant MPPN symmetry always being provided by the snub cube. Our results suggest that MPPNs with heterogeneous protein size can be highly symmetric, with a well-defined polyhedral ordering of membrane proteins of different sizes.

Capturing hemoglobin on graphene sheet from sub-microliter whole blood for quantitative characterization by internal extractive electrospray ionization mass spectrometry.

A disposable blood sampler, which is consisted of a sub-microliter whole blood collector and a graphene filter, loading graphene sheet to selectively capture hemoglobin from sub-microliter whole blood, was developed for both qualitative and quantitative characterization hemoglobin by internal extractive electrospray ionization mass spectrometry (iEESI-MS). The blood collector was elegantly fabricated in syringe-like fashion for precisely sampling tiny amounts (1.0 µL - 2%) of whole blood, which was immediately diluted by water inside the syringe and was then pressed through the graphene filter placed between the waste outlet and the syringe reservoir to capture the hemoglobin in the blood sample. Then the graphene with hemoglobin was directly eluted by a charged (+2.5 kV) solution (mathanol/water/formic acid, 48/48/4, v/v/v) to produce the hemoglobin ions for mass spectrometric analysis. Low detection-of-limit (19.3 mg L-1 (89.5 picomol)), acceptable linear response range (300-1500 mg L-1, R2 = 0.998), relative standard deviation (0.5-6.5%, n = 3), low sample consumption (≤1.0 µL) and a relatively high speed (≤4 min per sample, including the sample loading) were achieved, demonstrating that the graphene based iEESI-MS was an alternative choice for direct detection of hemoglobin in whole blood with minimal sample consumption.