Artificial peptides containing Ca,a- disubstituted amino acids: synthesis, conformational studies, and application as β-strand mimics

Short peptides containing Cα,α-dipropylglycine (Dpg) at alternating sequence positions were synthesized and examined for conformational behavior. Peptide assembly was performed using Fmoc-solid-phase chemistry where the coupling with PyAOP could be significantly enhanced at elevated temperature. Circular dichroism (CD) and NMR conformational studies revealed that incorporation of Dpg residues induced folded structures into peptides. It was observed that Dpg residues adopted helical conformation in a helix-promoting sequence. The resulting helical structure was comprised of consecutive β-turns whose structure was stabilized by salt bridge in aqueous solution. In this study, the preparation of sterically and polyfunctional Cα,α-disubstituted amino acids via alkylation of ethyl nitroacetate and transformation into derivatives ready for incorporation into peptides are described. Treatment of ethyl nitroacetate with N,N-diisopropylethylamine in the presence of a catalytic amount of tetraalkylammonium salt, followed by the addition of an activated alkyl halide or Michael acceptor, gave the doubly C-alkylated product in good to excellent yields. Selective nitro reduction with Zn in acetic or hydrogen over Raney Ni gave the corresponding amino ester that, upon saponification, can be protected with the fluorenylmethyloxycarbonyl (Fmoc) group. The synthesis of a sterically demanding Cα,α-dibenzylglycine (Dbzg), and an orthogonally protected, tetrafunctional Cα,α-disubstituted analogue of aspartic acid Bcmg is described. The preparation of amyloid fibril blocker peptides based on amyloid peptide hydrophobic core Aβ16-20 is described. These blocker peptides containing sterically hindered ααAA are β-strand mimics and are likely to interact with the amyloid hydrophobic core based on “like likes like” residue relationships. Amino acid symmetrical anhydride method was employed for the peptide synthesis. It was observed that Fmoc amino acid symmetrical anhydrides were efficient and readily available reagents for acylation of the N-terminus of highly hindered ααAAs. Comparison of a variety of coupling protocols showed that the symmetrical anhydride method always provided the superior results. Amyloid fibril inhibitor AMY-1 was synthesized and examined for its biological activities. It was observed that AMY-1 could significantly reduce the aggregation of amyloidogenic Aβ10-35 at different ratio at either room temperature and 37 ºC

Learning to Generate Posters of Scientific Papers

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 201

GeoSegNet: Point Cloud Semantic Segmentation via Geometric Encoder-Decoder Modeling

Semantic segmentation of point clouds, aiming to assign each point a semantic category, is critical to 3D scene understanding.Despite of significant advances in recent years, most of existing methods still suffer from either the object-level misclassification or the boundary-level ambiguity. In this paper, we present a robust semantic segmentation network by deeply exploring the geometry of point clouds, dubbed GeoSegNet. Our GeoSegNet consists of a multi-geometry based encoder and a boundary-guided decoder. In the encoder, we develop a new residual geometry module from multi-geometry perspectives to extract object-level features. In the decoder, we introduce a contrastive boundary learning module to enhance the geometric representation of boundary points. Benefiting from the geometric encoder-decoder modeling, our GeoSegNet can infer the segmentation of objects effectively while making the intersections (boundaries) of two or more objects clear. Experiments show obvious improvements of our method over its competitors in terms of the overall segmentation accuracy and object boundary clearness. Code is available at https://github.com/Chen-yuiyui/GeoSegNet

The role of inflammatory biomarkers in the development and progression of pre-eclampsia: a systematic review and meta-analysis

BackgroundPre-eclampsia (PE) is a pregnancy complication associated with maternal and fetal morbidity and mortality. Among the potential pathogenesis discussed, inflammation is considered an essential initiator of PE. Previous studies have compared the levels of various inflammatory biomarkers that indicate the existence of PE; however, the relative levels of pro-inflammatory and anti-inflammatory biomarkers and their dynamic changes during PE progression remain unclear. This knowledge is essential to explain the occurrence and progression of the disease.ObjectiveWe aimed to identify the relationship between inflammatory status and PE using inflammatory biomarkers as indicators. We also discussed the underlying mechanism by which inflammatory imbalance contributes to PE by comparing the relative levels of pro-inflammatory and anti-inflammatory biomarkers. Furthermore, we identified additional risk factors for PE.MethodsWe reviewed PubMed, Embase, and the Cochrane Library for articles published until 15th September 2022. Original articles that investigated inflammatory biomarkers in PE and normal pregnancy were included. We selected healthy pregnant women as controls. The inflammatory biomarkers in the case and control groups were expressed as standardized mean differences and 95% confidence intervals using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale. Publication bias was assessed using Egger’s test.ResultsThirteen articles that investigated 2,549 participants were included in this meta-analysis. Patients with PE had significantly higher levels of C-reactive protein (CRP), interleukin (IL)-4, IL-6, IL-8, IL-10, and tumor necrosis factor (TNF) than the controls. CRP and pro-inflammatory cytokine levels were higher than those of anti-inflammatory cytokines. Patients with gestational age > 34 weeks had significantly higher IL-6 and TNF levels. Patients with higher systolic blood pressure had significantly higher IL-8, IL-10, and CRP levels.ConclusionInflammatory imbalance is an independent risk factor for PE development. Impairment of the anti-inflammatory system is a crucial initiating factor for PE development. Failed autoregulation, manifested as prolonged exposure to pro-inflammatory cytokines, leads to PE progression. Higher levels of inflammatory biomarkers suggest more severe symptoms, and pregnant women after 34 weeks of gestation are more susceptible to PE

The MERS-CoV N Protein Regulates Host Cytokinesis and Protein Translation via Interaction With EF1A

Middle East respiratory syndrome coronavirus (MERS-CoV), a pathogen causing severe respiratory disease in humans that emerged in June 2012, is a novel beta coronavirus similar to severe acute respiratory syndrome coronavirus (SARS-CoV). In this study, immunoprecipitation and proximity ligation assays revealed that the nucleocapsid (N) protein of MERS-CoV interacted with human translation elongation factor 1A (EF1A), an essential component of the translation system with important roles in protein translation, cytokinesis, and filamentous actin (F-actin) bundling. The C-terminal motif (residues 359–363) of the N protein was the crucial domain involved in this interaction. The interaction between the MERS-CoV N protein and EF1A resulted in cytokinesis inhibition due to the formation of inactive F-actin bundles, as observed in an in vitro actin polymerization assay and in MERS-CoV-infected cells. Furthermore, the translation of a CoV-like reporter mRNA carrying the MERS-CoV 5′UTR was significantly potentiated by the N protein, indicating that a similar process may contribute to EF1A-associated viral protein translation. This study highlights the crucial role of EF1A in MERS-CoV infection and provides new insights into the pathogenesis of coronavirus infections

Scheme construction with numerical flux residual correction (NFRC) and group velocity control (GVC)

For simulating multi-scale complex flow fields like turbulent flows, the high order accurate schemes are preferred. In this paper, a scheme construction with numerical flux residual correction (NFRC) is presented. Any order accurate difference approximation can be obtained with the NFRC. To improve the resolution of the shock, the constructed schemes are modified with group velocity control (GVC) and weighted group velocity control (WGVC). The method of scheme construction is simple, and it is used to solve practical problems