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Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics. Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the “intelligent platelet aggregate classifier” or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and “learned” to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method’s accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient’s heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.

The diurnal variation and distribution of lunar surficial hydration (OH/H2O) is of great significance for understanding the solar wind implantation and water cycle on the Moon. Lunar south pole is an ideal place to study the diurnal variation of surficial hydration due to the large number of repeat observations of the same region, which is very limited in mid‐ or low‐latitudes. Here we showed clear 0.5‐hr interval diurnal variation of surficial hydration at lunar south pole. The variation of hydration band depth with local time is exactly the opposite to the variation of temperature, indicating that lunar surficial hydration changes sufficiently with temperature. This relationship indicates that both the diurnal variation and hydration content are latitude dependent. Our observations support the hypothesis that the diurnal variation of hydration on the Moon is due to the formation of metastable hydroxyl. Plain Language Summary Hydration (OH/H2O) has been found on the surface of the Moon due to the implantation of solar wind. Hydration contents in the morning and evening were observed to be higher than that at local noon. Lunar south pole is a very good place to study the diurnal variation of surficial hydration compared with other places of the Moon as there are a lot of repeat observations of the same area at different local times. We conducted a detailed investigation of surficial hydration at the lunar south pole based on repeat Moon Mineralogy Mapper near‐infrared data. We found surficial hydration at lunar south pole gradually decreases toward local noon, and then recovers to the morning level at evening. The variation trend is exactly the opposite to the temperature, indicating lunar surficial hydration changes sufficiently with instantaneous temperature. These observations provide clues for studies on the formation and evolution of volatiles on the Moon and other airless bodies. Key Points 0.5‐hr interval diurnal variation of lunar surficial hydration was revealed at lunar south pole for the first time Lunar surficial hydration changes sufficiently with instantaneous temperature Lunar surficial hydration did not change when the Moon enters the Earth's magnetotail

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang’E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth’s umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy.

In the near future, single-molecule surface-enhanced Raman spectroscopy (SERS) is expected to expand the family of popular analytical tools for single-molecule characterization. We provide a roadmap for achieving single molecule SERS through different enhancement strategies for diverse applications. We introduce some characteristic features related to single-molecule SERS, such as Raman enhancement factor, intensity fluctuation, and data analysis. We then review recent strategies for enhancing the Raman signal intensities of single molecules, including electromagnetic enhancement, chemical enhancement, and resonance enhancement strategies. To demonstrate the utility of single-molecule SERS in practical applications, we present several examples of its use in various fields, including catalysis, imaging, and nanoelectronics. Finally, we specify current challenges in the development of single-molecule SERS and propose corresponding solutions.

The double-helix point-spread function (DH-PSF) is one of the most used PSFs for large depth-of-field 3D single-molecule localisation microscopy. Due to its popularity, many algorithms have been developed to analyse experimental DH-PSF data, either based on dedicated DH-PSF fitting or on generalised PSF fitting, typically using cubic splines. We show here that the most popular implementations of both these approaches have limitations in terms of localisation performance, processing speed or user-friendliness. To overcome some of these limitations, we have developed a new analytical approach for DH-PSF fitting based on unmixing (DHPSFU) of fitted localisation data using distance pairing. We compare DHPSFU with two popular algorithms, SMAP and EasyDHPSF, using realistic simulated datasets based on experimental data, to show that our algorithm achieves the highest Jaccard index (DHPSFU: 0.98; SMAP: 0.91; EasyDHPSF: 0.85) and fastest CPU-based processing speed (DHPSFU: 6,800 locs/s; SMAP: 2,500 locs/s; EasyDHPSF: 63 locs/s). We also show that our algorithm achieves the best resolution when imaging the cellular plasma membrane of Jurkat T cells (DHPSFU: 140 nm, EasyDHPSF: 162 nm, SMAP: 165 nm). We have incorporated DHPSFU as a Fiji plugin and provide Matlab and Python scripts for user customisation.

Conventional methods for investigating heavy metal contamination in soil are time consuming and expensive. In this study, we (i) explored reflectance spectroscopy as an alternative method for assessing heavy metals, and (ii) further explored the physicochemical mechanism that allows estimation of heavy metals with the reflectance spectroscopy method. We first investigated the spectral response of changing concentrations of heavy metals in soils. The results indicated that only at very high concentration can transition elements exhibit their inherent absorption features. In spite of this observation, we successfully predicted low levels of heavy metals in agricultural soils. The best prediction accuracies were obtained for the siderophile elements Ni, Cr, and Co. The poorest prediction was for Cd. The order of prediction accuracy for metals was approximately the same as the order of their correlation coefficients with Fe. Complementary to some previous studies that found that the intercorrelation between heavy metals and active soil components (such as Fe oxides, organic matter, and clay) is the major predictive mechanism, in the present study we concluded that the correlation with total Fe (including active and residual Fe) is the major mechanism. This conclusion was further supported by both correlation analysis and chemical sequential extraction. Correlation analysis showed that all metals are negatively correlated with reflectance while positively correlated with the absorption depth at about 500 nm, a feature resulting from goethite. The chemical forms of heavy metals, which showed that besides the crystalline Fe oxide and organic matter fractions, heavy metals have significant amounts in the residual fraction, also strengthened the conclusion.

Ovarian cancer is a highly aggressive malignancy with limited therapeutic options and a poor prognosis. Deubiquitinating enzymes (DUBs) have emerged as critical regulators of protein ubiquitination and proteasomal degradation, influencing various cellular processes relevant to cancer pathogenesis. In this study, the research progress between ovarian cancer and DUBs was mapped and visualized using bibliometrics, and the expression patterns and biological roles of DUBs in ovarian cancer were summarized. Studies related to DUBs in ovarian cancer were extracted from the Web of Science Core Collection (WoSCC) database. VOSviewer 1.6.20, CiteSpace 6.3.R1, and R4.3.3 were used for bibliometric analysis and visualization. For analysis 243 articles were included in this study. The number of publications on DUBs in ovarian cancer has gradually increased each year. China, the United States, and the United Kingdom are at the center of this field of research. The Johns Hopkins University, Genentech, and Roche Holding are the main research institutions. David Komander, Zhihua Liu, and Richard Roden are the top authors in this field. The top five journals with the largest publication volumes in this field are , , , , and . Keyword burst analysis identified five research areas: \"deubiquitinating enzyme,\" \"expression,\" \"activation,\" \"degradation,\" and \"ubiquitin.\" In addition, we summarized the expression profiles and biological roles of DUBs in ovarian cancer, highlighting their roles in tumor initiation, growth, chemoresistance, and metastasis. An overview of the research progress is provided in this study on DUBs in ovarian cancer over the last three decades. It offers insight into the most cited papers and authors, core journals, and identified new trends.

Until recently, global high spatial resolution maps of FeO and TiO2 of the Moon were only derived from Clementine data. In this study, we show global maps of FeO and TiO2 using Chang'E‐1 Interference Imaging Spectrometer (IIM) at a spatial resolution of 200 m/pixel. With a newly developed calibration presented here, spectra obtained by IIM compare well with telescopic spectra. Spectral parameters previously shown to be sensitive to iron and titanium, derived from the calibrated IIM data are highly correlated with the measured elemental concentration with R2 = 0.96 for FeO and 0.95 for TiO2. The maps were developed using this calibration. Histograms of basalt FeO estimates have a negatively skewed distribution, while TiO2 distributions are unimodal. They also revealed that the lunar highland crust is relatively uniform on the quadrant scale (several hundred to thousand kilometers scale) but inhomogenous on the global scale. The area of highest elevation of the Moon has very low FeO and TiO2 raising the question about South Pole‐Aitken (SPA) (whether its ejecta deposits covered the highest elevation and when it was formed). Although the average FeO and TiO2 abundances for basalts are highly correlated, local areas of elevated iron can be associated with both high and low titanium. Key Points New high‐resolution iron and titanium maps are presented FeO of the highest elevations exclude a South Pole‐Aitken impact origin Refined calibrations of Chang E 1 IIM data are presented

Controlling the surface traps in metal halide perovskites (MHPs) is essential for device performance, stability, and commercialization. Here, a facile approach is introduced to passivate the methylammonium lead iodide (MAPbI3) perovskite single crystal (PSC) surface defects by benzylamine (BA) ligand treatment, and the natural crystallographic (100) facets surface of PSC is chosen as the research platform to provide a deeper understanding of the passivation process. The confocal photoluminescence (PL) results show that the pristine three-dimensional (3D) MAPbI3 PSC surface with a symmetric emission spectrum is normally converted to a pure two-dimensional (2D) BA2PbI4, and also forms a quasi-2D Ruddlesden–Popper perovskite (RPP) BA2MAn−1PbnI3n+1 (n = 2, 3, 4, … ∞) after BA exchange with cation defects. The blue shift in the PL peak, as well as the extended exciton lifetimes of time-resolved photoluminescence (TRPL), indicate the realization of surface defect passivation. Additionally, changes in surface morphology are also investigated. The reaction starts with the formation of small, layered crystallites over the surface; as time elapses, the layered crystallites spread and merge in contact with each other, eventually resulting in smooth features. Our findings present a simple approach for MAPbI3 PSC surface defect passivation, which aims to advance MHP optimization processes toward practical perovskite device applications.