RESUMO
Non-invasive prenatal testing (NIPT) is a quite popular approach for detecting fetal genomic aneuploidies. However, due to the limitations on sequencing read length and coverage, NIPT suffers a bottleneck on further improving performance and conducting earlier detection. The errors mainly come from reference biases and population polymorphism. To break this bottleneck, we proposed NIPT-PG, which enables the NIPT algorithm to learn from population data. A pan-genome model is introduced to incorporate variant and polymorphic loci information from tested population. Subsequently, we proposed a sequence-to-graph alignment method, which considers the read mis-match rates during the mapping process, and an indexing method using hash indexing and adjacency lists to accelerate the read alignment process. Finally, by integrating multi-source aligned read and polymorphic sites across the pan-genome, NIPT-PG obtains a more accurate z-score, thereby improving the accuracy of chromosomal aneuploidy detection. We tested NIPT-PG on two simulated datasets and 745 real-world cell-free DNA sequencing data sets from pregnant women. Results demonstrate that NIPT-PG outperforms the standard z-score test. Furthermore, combining experimental and theoretical analyses, we demonstrate the probably approximately correct learnability of NIPT-PG. In summary, NIPT-PG provides a new perspective for fetal chromosomal aneuploidies detection. NIPT-PG may have broad applications in clinical testing, and its detection results can serve as a reference for false positive samples approaching the critical threshold.
Assuntos
Aneuploidia , Teste Pré-Natal não Invasivo , Humanos , Feminino , Gravidez , Teste Pré-Natal não Invasivo/métodos , Algoritmos , Genômica/métodos , Diagnóstico Pré-Natal/métodos , Análise de Sequência de DNA/métodosRESUMO
Given the prevalence of heterocyclic scaffolds in drug-related molecules, converting these highly modular heterocyclic scaffolds into structural diversified and dearomatized analogs is an ideal strategy for improving their physicochemical and pharmacokinetic properties. Here, we described an efficient method for silver carbene-mediated dearomative N-N bond cleavage leading to skeletal hopping between indazole and 1,2-dihydroquinazoline via a highly selective single-carbon insertion procedure. Using this methodology, a series of dihydroquinazoline analogues with diarylmethylene-substituted quaternary carbon centers were constructed with excellent yields and good functional group compatibility, which was further illustrated by the late-stage diversification of important pharmaceutically active ingredients. DFT calculations indicated that the silver catalyst not only induces the formation of the silver carbene, but also activates the diazahexatriene intermediate, which plays a crucial role in the formation of the C-N bond.
RESUMO
The chemical constituents from the stems and leaves of Artocarpus tonkinensis in Artocarpus of Moraceae were systematically studied by means of silica gel, octadecylsilyl(ODS), and Sephadex LH-20 gel column chromatographies, as well as preparative high-performance liquid chromatography(Pre-HPLC) and a variety of chromatographic separation techniques. The spectral data and physicochemical properties of the compounds were obtained from separation and compared with those of the compounds reported in the literature. As a result, 11 compounds isolated from the 90% ethanol extract of the stems and leaves of A. tonkinensis were identified as artocatonkine(1), 5,6,7,4'-tetramethoxyflavone(2), apigenin-4'-O-ß-D-glucoside(3), rayalinol(4), psorachalcone A(5), 4-ketopinoresinol(6), ficusesquilignan B(7), pinnatifidanin AI(8), pinnatifidanin A(9), O-methylmellein(10), and trans-4-hydroxymellein(11). Among these compounds, compound 1 was a new prenylated flavone, and compounds 2-11 were isolated from the plants belonging to the genus Artocarpus for the first time. Furthermore, all compounds 1-11 were evaluated for their anti-rheumatoid arthritis activities, and the MTS method was used to measure their inhibitory effects on the proliferation of synovioblasts in vitro. The results of activity evaluation showed that flavonoid compounds 1-3, 5, and lignan compounds 8 and 9 displayed significant anti-rheumatoid arthritis activities, showing the IC_(50) values in inhibiting the proliferation of synovioblasts MH7A from(6.38±0.06) µmol·L~(-1) to(168.58±0.28)µmol·L~(-1).
Assuntos
Artocarpus , Proliferação de Células , Folhas de Planta , Caules de Planta , Artocarpus/química , Folhas de Planta/química , Caules de Planta/química , Proliferação de Células/efeitos dos fármacos , Humanos , Medicamentos de Ervas Chinesas/farmacologia , Medicamentos de Ervas Chinesas/química , Linhagem Celular , Estrutura Molecular , Cromatografia Líquida de Alta PressãoRESUMO
Layered or chain materials have received significant research attention owing to their interesting physical properties, which can dramatically change when the material is thinned from bulk (three-dimensional) to thin two-dimensional sheet or one-dimensional (1D) chain form. Materials with the stoichiometry AX2 with A = Si or Ge and X = S or Se form an especially intriguing semiconducting class. For example, bulk silicon dichalcogenides (SiX2) consist of 1D chains held together by van der Waals forces. Although this structural configuration has the potential to reveal interesting physical phenomena within the 1D limit, obtaining SiX2 single chains has been challenging. We here examine experimentally and theoretically SiX2 materials in the low chain number limit. Carbon nanotubes serve as growth templates and stabilize and protect the structures, and atomic-resolution scanning transmission electron microscopy directly identifies the atomic structure. Two distinct chain structures are observed for SiX2. SixGe1-xS2(1-y)Se2y quaternary alloy chains are also synthesized and characterized, demonstrating tunable semiconducting properties at the atomic-chain level. Density functional theory calculations reveal that the band gap of these alloy chains can be widely tuned through composition engineering. This work offers the possibilities for synthesizing and controlling semiconductor compositions at the single-chain limit to tailor material properties.
RESUMO
High aerosol loadings are observed not only in megacities on continents but also in oceanic regions like the Bohai Sea. This work provides a comprehensive analysis of the spatial and temporal variations in Aerosol Optical Depth (AOD) across different ocean regions worldwide over the past four decades, using remote sensing reanalysis data. The mean AOD value across all oceanic grids is approximately 0.112, with higher levels recorded in the Central Atlantic (~0.206), followed by the North Indian Ocean (~0.201), and the Western North Pacific (~0.197). A latitudinal analysis reveals that high AOD values are predominantly found in the Northern Hemisphere's oceanic regions, especially between latitudes 0° and 70° N. Except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Among which, the Bohai Sea stands out as the most polluted oceanic region with AOD value of 0.35. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5 % of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S-30° N). Investigation into inland seas shows that nearly two-thirds have experienced a declining AOD trend, while sharply upward trends in AOD are primarily found in Asia. The Bohai Sea shows the largest increase in AOD, with an annual growth rate of 1.4 %. The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. To improve air quality in inland seas like the Bohai Sea, adjusting industrial layouts, such as relocating heavy industries from the surrounding coastal cities' proximities to areas near open seas, could significantly benefit public health.
RESUMO
Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.
RESUMO
Epoxides, as a prominent small ring O-heterocyclic and the privileged pharmacophores for medicinal chemistry, have recently represented an ideal substrate for the development of single-atom replacements. The previous O-to-C replacement strategy for epoxides to date typically requires high temperatures to achieve low yields and lacks substrate range and functional group tolerance, so achieving this oxygen-carbon exchange remains a formidable challenge. Here, we report a silver-catalyzed direct conversion of epoxides into trifluoromethylcyclopropanes in a single step using trifluoromethyl N-triftosylhydrazones as carbene precursors, thereby achieving oxygen-carbon exchange via a tandem deoxygenation/[2 + 1] cycloaddition. The reaction shows broad tolerance of functional groups, allowing routine cheletropic olefin synthesis in a strategy for the net oxygen-carbon exchange reaction. The utility of this method is further showcased with the late-stage diversification of epoxides derived from bioactive natural products and drugs. Mechanistic experiments and DFT calculations elucidate the reaction mechanism and the origin of the chemo- and stereoselectivity.
RESUMO
In order to develop a highly efficient H2S gas sensor at low working temperature, in this work, a kind of novel Ce-doped ZnCo2O4 hollow microspheres (Ce/ZnCo2O4 HMSs) were successfully synthesized using a template-free one-pot method, showing a sensitive response toward H2S. The microstructure and morphology of the material were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The gas-sensing performance of the composite was investigated, showing that the ZnCo2O4 doped with 6 mol% Ce had the highest response to 20 ppm H2S at a low operating temperature of 160 °C with a response value of 67.42, which was about 2 times higher than that of original ZnCo2O4. The prepared Ce/ZnCo2O4 HMS sensor in response to H2S exhibited a linear range of 0.1-200 ppm with a low detection limit of 0.1 ppm under the conditions of ambient humidity of 45% and ambient temperature of 20 °C. Meanwhile, it also possessed good selectivity, repeatability and reproducibility. The response value of the sensor decreased by 5.32% after 7 months of continuous monitoring of H2S in an atmospheric environment of a pig farm, indicating that the sensor had a long-term stability and continuous service life with important application prospects.
RESUMO
Here, we report a silver carbene-enabled single-carbon insertion reaction of indoles via a one-pot, two-step sequence to deliver a dearomative quaternary center quinoline scaffold in a modular fashion. Specifically, we used N-triftosylhydrazones as masked donor-donor carbene precursors that facilitate the insertion of carbon atoms bearing various functional groups to the library of functionalized quinoline. Experimental and DFT evidence support the transient presence of a cyclopropane species and removal of protecting groups.
RESUMO
Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. The Bohai Sea, a semi-enclosed sea in northern China, is surrounded by thirteen industrial cities that have experienced significant improvements in air quality over the past decade. However, the changes in air pollution over the Bohai Sea and its impacts on surrounding cities remain poorly understood. To address this, this study investigated the evolution of air pollution and its chemical composition in the Bohai Sea over four decades, utilizing satellite remote sensing data, reanalysis datasets, emissions inventories, and statistical modeling. Historically, the region has suffered from severe air pollution, resulting from a combination of continental emissions and marine inputs (e.g., sea salt, ports and maritime vessel activities). The aerosol optical depth (AOD) over the sea was higher than the mean levels observed in its surrounding coastal cities. Statistically, 45% of the air masses reaching the Bohai Sea are associated with natural sources (dust- and marine-rich), while the remainder carry anthropogenic pollutants from continental regions. With the exception of Cangzhou city, these coastal cities suffer from air pollutants originating from the Bohai Sea. Cities in the northern region of the sea, spanning from Tianjin to Yingkou, are particularly impacted. The majority of the surrounding cities are affected by a large proportion of anthropogenic aerosol types transported through air masses from the Bohai Sea, including those from biomass burning and industrial activities. These findings emphasize the considerable influence of human-induced sources in the Bohai Sea on neighboring urban areas. Furthermore, being a maritime region, natural sources like sea salt and dust from the sea may also exert a discernible impact on the neighboring environment.
Assuntos
Poluentes Atmosféricos , Poluição do Ar , Humanos , Cidades , Poluentes Atmosféricos/análise , Poeira/análise , Poluição do Ar/análise , China , Aerossóis/análise , Monitoramento Ambiental/métodosRESUMO
Van der Waals (vdW) ferromagnetic materials have emerged as a promising platform for the development of 2D spintronic devices. However, studies to date are restricted to vdW ferromagnetic materials with low Curie temperature (Tc) and small magnetic anisotropy. Here, a chemical vapor transport method is developed to synthesize a high-quality room-temperature ferromagnet, Fe3GaTe2 (c-Fe3GaTe2), which boasts a high Tc = 356 K and large perpendicular magnetic anisotropy. Due to the planar symmetry breaking, an unconventional room-temperature antisymmetric magnetoresistance (MR) is first observed in c-Fe3GaTe2 devices with step features, manifesting as three distinctive states of high, intermediate, and low resistance with the sweeping magnetic field. Moreover, the modulation of the antisymmetric MR is demonstrated by controlling the height of the surface steps. This work provides new routes to achieve magnetic random storage and logic devices by utilizing the room-temperature thickness-controlled antisymmetric MR and further design room-temperature 2D spintronic devices based on the vdW ferromagnet c-Fe3GaTe2.
RESUMO
Gestational diabetes mellitus (GDM) presents varied manifestations throughout pregnancy and poses a complex clinical challenge. High-depth cell-free DNA (cfDNA) sequencing analysis holds promise in advancing our understanding of GDM pathogenesis and prediction. In 299 women with GDM and 299 matched healthy pregnant women, distinct cfDNA fragment characteristics associated with GDM are identified throughout pregnancy. Integrating cfDNA profiles with lipidomic and single-cell transcriptomic data elucidates functional changes linked to altered lipid metabolism processes in GDM. Transcription start site (TSS) scores in 50 feature genes are used as the cfDNA signature to distinguish GDM cases from controls effectively. Notably, differential coverage of the islet acinar marker gene PRSS1 emerges as a valuable biomarker for GDM. A specialized neural network model is developed, predicting GDM occurrence and validated across two independent cohorts. This research underscores the high-depth cfDNA early prediction and characterization of GDM, offering insights into its molecular underpinnings and potential clinical applications.