Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.

An Unexpected Decrease in Vibrational Entropy of Multicomponent Rutile Oxides.

High-entropy oxides (HEOs), featuring infinite chemical composition and exceptional physicochemical properties, are attracting much attention. The configurational entropy caused by a component disorder of HEOs is popularly believed to be the main driving force for thermal stability, while the role of vibrational entropy in the thermodynamic landscape has been neglected. In this study, we systematically investigated the vibrational entropy of multicomponent rutile oxides (including Fe0.5Ta0.5O2, Fe0.333Ti0.333Ta0.333O2, Fe0.25Ti0.25Ta0.25Sn0.25O2, and Fe0.21Ti0.21Ta0.21Sn0.21Ge0.16O2) by precise heat capacity measurements. It is found that vibrational entropy gradually decreases with increasing component disorder, beyond what one could expect from an equilibrium thermodynamics perspective. Moreover, all multicomponent rutile oxides exhibit a positive excess vibrational entropy at 298.15 K. Upon examinations of configuration disorder, size mismatch, phase transition, and polyhedral distortions, we demonstrate that the excess vibrational entropy plays a pivotal role in lowering the crystallization temperature of multicomponent rutile oxides. These findings represent the first experimental confirmation of the role of lattice vibrations in the thermodynamic landscape of rutile HEOs. In particular, vibrational entropy could serve as a novel descriptor to guide the predictive design of multicomponent oxide materials.

Graph-Based Method for Calibration of High-Resolution Mass Spectra of Natural Organic Matter.

Inaccuracies in ion detection and signal processing can undermine confidence in the molecular formula assignment of high-resolution mass spectrometry, which relies on precise matching of the mass-to-charge ratio (m/z). This study proposes a novel graph-based spectra calibration method, MSCMcalib, which implements coordinate transformation and pattern detection. MSCMcalib maps uncalibrated m/z data onto a modified 2D mass defect plot, facilitating the automatic calibration of detected lines, i.e., the calibration of uncalibrated peaks aligned with these lines. The "propagation" method is subsequently employed to accurately and automatically calibrate 605 m/z values across multiple lines, encompassing 98% of the m/z range. The calibrated m/z values divide the m/z range of the spectrum into multiple subintervals, with each subinterval undergoing a process of "scaling" calibration. The utilization of narrower partitions effectively mitigates divergence issues at both ends that arise from the polynomial fitting of errors against m/z. The effectiveness of MSCMcalib is validated through the calibration of SRFA data with m/z error ranges spanning from -10 to -6 ppm, resulting in an additional assignment of 11%-30% more molecular formulas compared to the quadratic fitting calibration.

Barbier Polymerization-Induced Emission towards Fully Substituted Polyethylene Analogues with Non-Traditional Intrinsic Luminescence.

The properties of polyethylene are highly dependent on the variety and quantity of substitutions. Generally, polyethylene can only be fully substituted with fluorine atoms, mainly e. g., polytetrafluoroethylene and nafion, because atomic radius of fluorine atom is small enough. The preparation of fully substituted polyethylene analogues (FSPEA) and their non-traditional intrinsic luminescence (NTIL) are attractive, especially for substitutions with relatively larger atomic radii than a fluorine atom. Here, Barbier polymerization-induced emission (PIE) is demonstrated as a universal method for the molecular design of NTIL type FSPEAs with intriguing aggregation-induced emission (AIE) behaviors. Through Barbier polymerization of diphenyldichloromethane and different peroxyesters in the presence of Mg in one pot, a series of FSPEAs, including polytriphenylethanol (PTPE), polydiphenylfurylethanol (PDPFE), polydiphenylthiophenylethanol (PDPTE) and polydiphenylnaphthylethanol (PDPNE) have been successfully prepared. Further potential applications for explosive detection, artificial light-harvesting system and white phosphor-converted light-emitting diode are investigated. Therefore, this work opens up a new approach for the molecular design of FSPEA with non-conjugated luminescence, which may cause inspirations to different research fields like polyolefin and luminescent materials.

Lithography-Free Nanofabrication on Various Semiconductors by the Codeposition Etching Technique.

Nano-/microstructures can be formed with the aid of small amounts of impurities during deposition with noble gas plasma irradiation, which is referred to as codeposition etching. This can be a new method for lithography-free semiconductor nanofabrication. Here, the codeposition etching method was employed with argon plasma and molybdenum (Mo) impurities on various semiconductors. Structures can be formed only on substrates that have a lower sputtering yield than the seed impurity. The density, area, and height of structures are related to both the impurity deposition rate and the substrate material. Moreover, two mechanisms of impurity nucleation are proposed according to time dependence results for the formation of the structures.

Fecal calprotectin: A novel predictor of ulcerated esophageal injury after atrial fibrillation catheter ablation.

BACKGROUND: Atrial esophageal fistula (AEF) is a lethal complication that can occur post atrial fibrillation (AF) ablation. Esophageal injury (EI) is likely to be the initial lesion leading to AEF. Endoscopic examination is the gold standard for a diagnosis of EI but extensive endoscopic screening is invasive and costly. This study was conducted to determine whether fecal calprotectin (Fcal), a marker of inflammation throughout the intestinal tract, may be associated with the existence of esophageal injury. METHODS: This diagnostic study was conducted in a cohort of 166 patients with symptomatic AF undergoing radiofrequency catheter ablation from May 2020 to June 2021. Fcal tests were performed 1-7 days after ablation. All patients underwent endoscopic ultrasonography 1 or 2 days after ablation. RESULTS: The levels of Fcal were significantly different between the EI and non-EI groups (404.9 µg/g (IQR 129.6-723.6) vs. 40.4 µg/g (IQR 15.0-246.2), p < .001). Analysis of ROC curves revealed that a Fcal level of 125 µg/g might be the optimal cut-off value for a diagnosis of EI, giving a 78.8% sensitivity and a 65.4% specificity. The negative predictive value of Fcal was 100% for ulcerated EI. CONCLUSIONS: The level of Fcal is associated with EI post AF catheter ablation. 125 µg/g might be the optimal cut-off value for a diagnosis of EI. Negative Fcal could predict the absence of ulcerated EI, which could be considered a precursor to AEF.

Deciphering dissolved organic matter characteristics and its fate in a glacier-fed desert river-the Tarim river, China.

The bioavailable diverse dissolved organic matter (DOM) present in glacial meltwater significantly contributes to downstream carbon cycling in mountainous regions. However, the comprehension of molecular-level characteristics of riverine DOM, from tributary to downstream and their fate in glacier-fed desert rivers remains limited. Herein, we employed spectroscopic and high-resolution mass spectrometry techniques to study both optical and molecular-level characteristics of DOM in the Tarim River catchment, northwest China. The results revealed that the DOC values in the downstream were higher than those in the tributaries, yet they remained comparable to those found in other glacier-fed streams worldwide. Five distinct components were identified using EEM-PARAFAC analysis in both tributary and downstream samples. The dominance of three protein-like components in tributary samples, contrasting with a higher presence of humic-like components in downstream samples, which implied that the dilution and alterations of the glacier DOM signature and overprinting with terrestrial-derived DOM. Molecular composition revealed that thousands of compounds with higher molecular weight and increased aromaticity were transformed, generated and introduced from terrestrial inputs during downstream transportation. The twofold rise in polycyclic aromatic and polyphenolic compounds observed downstream compared to tributaries indicated a greater influx of terrestrial organic matter introduced into the downstream during water transportation. The study suggests that the glacier-sourced DOM experienced minimal photodegradations, with limited influence from human activities, while also being shaped by terrestrial inputs during its transit in the alpine-arid region. This unique scenario offers valuable insights into comprehending the fate of DOM originating from glacial meltwater in arid mountainous regions.

Metabolism of alcohol ethoxylates (AEs) in rat, hamster, and human hepatocytes and liver S9: a pilot study for metabolic stability, metabolic pathway, and metabolites identification in vitro and in silico.

Alcohol ethoxylates (AEs) are a well-known class of non-ionic surfactants widely used by the personal care market. The aim of this study was to evaluate and characterize the in vitro metabolism of AEs and identify metabolites. Five selected individual homologue AEs (C8EO4, C10EO5, C12EO4, C16EO8, and C18EO3) were incubated using human, rat, and hamster liver S9 fraction and cryopreserved hepatocytes. LC-MS was used to identify metabolites following the incubation of AEs by liver S9 and hepatocytes of all three species. All AEs were metabolized in these systems with a half-life ranging from 2 to 139 min. In general, incubation of AE with human liver S9 showed a shorter half-life compared to rat liver S9. While rat hepatocytes metabolized AEs faster than human hepatocytes. Both hydrophobic alkyl chain and hydrophilic EO head group groups of AEs were found to be target sites of metabolism. Metabolites were identified that show primary hydroxylation and dehydrogenation, followed by O-dealkylation (shortening of EO head groups) and glucuronidation. Additionally, the detection of whole EO groups indicates the cleavage of the ether bond between the alkyl chain and the EO groups as a minor metabolic pathway in the current testing system. Furthermore, no difference in metabolic patterns of each individual homologue AE investigated was observed, regardless of alkyl chain length or the number of EO groups. Moreover, there is an excellent agreement between the in vitro experimental data and the metabolite profile simulations using in silico approaches (OECD QSAR Toolbox). Altogether, these data indicate fast metabolism of all AEs with a qualitatively similar metabolic pathway with some quantitative differences observed in the metabolite profiles. These metabolic studies using different species can provide important reference values for further safety evaluation.

A Joint State and Fault Estimation Scheme for State-Saturated System with Energy Harvesting Sensors.

In this article, the issue of joint state and fault estimation is ironed out for delayed state-saturated systems subject to energy harvesting sensors. Under the effect of energy harvesting, the sensors can harvest energy from the external environment and consume an amount of energy when transmitting measurements to the estimator. The occurrence probability of measurement loss is computed at each instant according to the probability distribution of the energy harvesting mechanism. The main objective of the addressed problem is to construct a joint state and fault estimator where the estimation error covariance is ensured in some certain sense and the estimator gain is determined to accommodate energy harvesting sensors, state saturation, as well as time delays. By virtue of a set of matrix difference equations, the derived upper bound is minimized by parameterizing the estimator gain. In addition, the performance evaluation of the designed joint estimator is conducted by analyzing the boundedness of the estimation error in the mean-squared sense. Finally, two experimental examples are employed to illustrate the feasibility of the proposed estimation scheme.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-µRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Spatiotemporal Utilization of Latent Heat in Erythritol-based Phase Change Materials as Solar Thermal Fuels.

Solar thermal fuels (STFs) have been particularly concerned as sustainable future energy due to their impressive ability to store solar energy in chemical bonds and controllably release thermal energy. However, currently studied STFs mainly focus on molecule-based materials with high photochemical activity, toxicity, and compromised features, which greatly restricts their applications in practical scenarios of solar energy utilization. Herein, we present a novel erythritol-based composite phase change material (PCM) as a new type of STFs with an outstanding capability to store solar energy as latent heat in its stable supercooling state and release thermal energy as needed. This composite PCM with stored thermal energy can be maintained stably at room temperature and subsequently release latent heat as high as 224.9 J/g during the crystallization process triggered by thermal stimuli. Remarkably, solar energy can be converted into latent heat stored in the composite PCM over months. Through mechanical stimulations, the released latent heat can increase the temperature of the composite up to 91 °C. This work presents a new concept of using spatiotemporal storage and release of latent heat in PCMs for solar energy utilization, making it a potential candidate as STFs for developing future clean energy techniques.

Intrinsically Flexible Phase Change Fibers for Intelligent Thermal Regulation.

Owing to the significant latent heat generated at constant temperatures, phase change fibers (PCFs) have recently received much attention in the field of wearable thermal management. However, the phase change materials involved in the existing PCFs still experience a solid-liquid transition process, severely restricting their practicality as wearable thermal management materials. Herein, we, for the first time, developed intrinsically flexible PCFs (polyethylene glycol/4,4'-methylenebis(cyclohexyl isocyanate) fibers, PMFs) through polycondensation and wet-spinning process, exhibiting an inherent solid-solid phase transition property, adjustable phase transition behaviors, and outstanding knittability. The PMFs also present superior mechanical strength (28 MPa), washability (> 100 cycles), thermal cycling stability (> 2000 cycles), facile dyeability, and heat-induced recoverability, all of which are highly significant for practical wearable applications. Additionally, the PMFs can be easily recycled by directly dissolving them in solvents for reprocessing, revealing promising applications as sustainable materials for thermal management. Most importantly, the applicability of the PMFs was demonstrated by knitting them into permeable fabrics, which exhibit considerably improved thermal management performance compared with the cotton fabric. The PMFs offer great potential for intelligent thermal regulation in smart textiles and wearable electronics.

The FASTQ+ format and PISA.

SUMMARY: The FASTQ+ format is designed for single-cell experiments. It extends various optional tags, including cell barcodes and unique molecular identifiers, to the sequence identifier and is fully compatible with the FASTQ format. In addition, PISA implements various utilities for processing sequences in the FASTQ format and alignments in the SAM/BAM/CRAM format from single-cell experiments, such as converting FASTQ format to FASTQ+, annotating alignments, PCR deduplication, feature counting and barcodes correction. The software is open-source and written in C language. AVAILABILITY AND IMPLEMENTATION: https://doi.org/10.5281/zenodo.7007056 or https://github.com/shiquan/PISA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Promotion of Humic Acid Transformation by Abiotic and Biotic Fe Redox Cycling in Nontronite.

The redox activity of Fe-bearing minerals is coupled with the transformation of organic matter (OM) in redox dynamic environments, but the underlying mechanism remains unclear. In this work, a Fe redox cycling experiment of nontronite (NAu-2), an Fe-rich smectite, was performed via combined abiotic and biotic methods, and the accompanying transformation of humic acid (HA) as a representative OM was investigated. Chemical reduction and subsequent abiotic reoxidation of NAu-2 produced abundant hydroxyl radicals (thereafter termed as ·OH) that effectively transformed the chemical and molecular composition of HA. More importantly, transformed HA served as a more premium electron donor/carbon source to couple with subsequent biological reduction of Fe(III) in reoxidized NAu-2 by Geobacter sulfurreducens, a model Fe-reducing bacterium. Destruction of aromatic structures and formation of carboxylates were mechanisms responsible for transforming HA into an energetically more bioavailable substrate. Relative to unaltered HA, transformed HA increased the extent of the bioreduction by 105%, and Fe(III) reduction was coupled with oxidation and even mineralization of transformed HA, resulting in bleached HA and formation of microbial products and cell debris. ·OH transformation slightly decreased the electron shuttling capacity of HA in bioreduction. Our results provide a mechanistic explanation for rapid OM mineralization driven by Fe redox cycling in redox-fluctuating environments.

Green Tides Significantly Alter the Molecular Composition and Properties of Coastal DOC and Perform Dissolved Carbon Sequestration.

Despite green tides (or macroalgal blooms) having multiple negative effects, it is thought that they have a positive effect on carbon sequestration, although this aspect is rarely studied. Here, during the world's largest green tide (caused by Ulva prolifera) in the Yellow Sea, the concentration of dissolved organic carbon (DOC) increased by 20-37% in intensive macroalgal areas, and thousands of new molecular formulas rich in CHNO and CHOS were introduced. The DOC molecular species derived from U. prolifera constituted ∼18% of the total DOC molecular species in the seawater of bloom area, indicating the profound effect that green tides have on shaping coastal DOC. In addition, 46% of the macroalgae-derived DOC was labile DOC (LDOC), which had only a short residence time due to rapid microbial utilization. The remaining 54% was recalcitrant DOC (RDOC) rich in humic-like substances, polycyclic aromatics, and highly aromatic compounds that resisted microbial degradation and therefore have the potential to play a role in long-term carbon sequestration. Notably, source analysis showed that in addition to the microbial carbon pump, macroalgae are also an important source of RDOC. The number of RDOC molecular species contributed by macroalgae even exceed (77 vs 23%) that contributed by microorganisms.

Quantifying Stochastic Processes in Shaping Dissolved Organic Matter Pool with High-Resolution Mass Spectrometry.

Natural dissolved organic matter (DOM) represents a ubiquitous molecular mixture, progressively characterized by spatiotemporal resolution. However, an inadequate comprehension of DOM molecular dynamics, especially the stochastic processes involved, hinders carbon cycling predictions. This study employs ecological principles to introduce a neutral theory to elucidate the fundamental processes involving molecular generation, degradation, and migration. A neutral model is thus formulated to assess the probability distribution of DOM molecules, whose frequencies and abundances follow a ß-distribution relationship. The neutral model is subsequently validated with high-resolution mass spectrometry (HRMS) data from various waterbodies, including lakes, rivers, and seas. The model fitting highlights the prevalence of molecular neutral distribution and quantifies the stochasticity within DOM molecular dynamics. Furthermore, the model identifies deviations of HRMS observations from neutral expectations in photochemical and microbial experiments, revealing nonrandom molecular transformations. The ecological null model further validates the neutral modeling results, demonstrating that photodegradation reduces molecular stochastic dynamics at the surface of an acidic pit lake, while random distribution intensifies at the river surface compared with the porewater. Taken together, the DOM molecular neutral model emphasizes the significance of stochastic processes in shaping a natural DOM pool, offering a potential theoretical framework for DOM molecular dynamics in aquatic and other ecosystems.

Unraveling the Linkages between Molecular Abundance and Stable Carbon Isotope Ratio in Dissolved Organic Matter Using Machine Learning.

Dissolved organic matter (DOM) is a complex mixture of molecules that constitutes one of the largest reservoirs of organic matter on Earth. While stable carbon isotope values (δ13C) provide valuable insights into DOM transformations from land to ocean, it remains unclear how individual molecules respond to changes in DOM properties such as δ13C. To address this, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the molecular composition of DOM in 510 samples from the China Coastal Environments, with 320 samples having δ13C measurements. Utilizing a machine learning model based on 5199 molecular formulas, we predicted δ13C values with a mean absolute error (MAE) of 0.30‰ on the training data set, surpassing traditional linear regression methods (MAE 0.85‰). Our findings suggest that degradation processes, microbial activities, and primary production regulate DOM from rivers to the ocean continuum. Additionally, the machine learning model accurately predicted δ13C values in samples without known δ13C values and in other published data sets, reflecting the δ13C trend along the land to ocean continuum. This study demonstrates the potential of machine learning to capture the complex relationships between DOM composition and bulk parameters, particularly with larger learning data sets and increasing molecular research in the future.

Exploring the Complexities of Dissolved Organic Matter Photochemistry from the Molecular Level by Using Machine Learning Approaches.

Dissolved organic matter (DOM) sustains a substantial part of the organic matter transported seaward, where photochemical reactions significantly affect its transformation and fate. The irradiation experiments can provide valuable information on the photochemical reactivity (photolabile, photoresistant, and photoproduct) of molecules. However, the inconsistency of the fate of irradiated molecules among different experiments curtailed our understanding of the roles the photochemical reactions have played, which cannot be properly addressed by traditional approaches. Here, we conducted irradiation experiments for samples from two large estuaries in China. Molecules that occurred in irradiation experiments were characterized by the Fourier transform ion cyclotron resonance mass spectrometry and assigned probabilistic labels to define their photochemical reactivity. These molecules with probabilistic labels were used to construct a learning database for establishing a suitable machine learning (ML) model. We further applied our well-trained ML model to "un-matched" (i.e., not detected in our irradiation experiments) molecules from five estuaries worldwide, to predict their photochemical reactivity. Results showed that numerous molecules with strong photolability can be captured solely by the ML model. Moreover, comparing DOM photochemical reactivity in five estuaries revealed that the riverine DOM chemistry largely determines their subsequent photochemical transformation. We offer an expandable and renewable approach based on ML to compatibly integrate existing irradiation experiments and shed insight into DOM transformation and degradation processes.

Temperature Thresholds of Pyrogenic Dissolved Organic Matter in Heating Experiments Simulating Forest Fires.

Heating temperature (HT) during forest fires is a critical factor in regulating the quantity and quality of pyrogenic dissolved organic matter (DOM). However, the temperature thresholds at which maximum amounts of DOM are produced (TTmax) and at which the DOC gain turns into net DOC loss (TT0) remain unidentified on a component-specific basis. Here, based on solid-state 13C nuclear magnetic resonance, absorbance and fluorescence spectroscopies, and Fourier transform ion cyclotron resonance mass spectrometry, we analyzed variations in DOM composition in detritus and soil with HT (150-500 °C) and identified temperature thresholds for components on structural, fluorophoric, and molecular formula levels. TTmax was similar for detritus and soil and ranged between 225 and 250 °C for bulk dissolved organic carbon (DOC) and most DOM components. TT0 was consistently lower in detritus than in soil. Moreover, temperature thresholds differed across the DOM components. As the HT increased, net loss was observed initially in molecular formulas tentatively associated with carbohydrates and aliphatics, then proteins, peptides, and polyphenolics, and ultimately condensed aromatics. Notably, at temperatures lower than TT0, particularly at TTmax, burning increased the DOC quantity and thus might increase labile substrates to fuel soil microbial community. These composition-specific variations of DOM with temperature imply nonlinear and multiple temperature-dependent wildfire impacts on soil organic matter properties.

Speciation and molecular characterization of thiophenic and sulfide compounds in petroleum by sulfonation and methylation followed by electrospray mass spectrometry.

Thiophenes and sulfides are the dominant sulfur-containing compounds in petroleum and have been widely of concern in the fields of petroleum refining and geochemistry. In this study, a novel approach was developed for selective separation and characterization of petroleum-derived thiophenic and sulfide compounds. Thiophenic compounds were selectively converted to sulfonates in the presence of vitriolic acid and can be characterized by negative ion electrospray mass spectrometry. Thiophenic sulfonates were further separated from the oil by silica chromatography and enabled the molecular characterization of sulfides in the residual oil. Various model sulfur compounds and a vacuum gas oil were used to validate the method; thiophenic and sulfide biomarker compounds in a well-documented crude oil were selectively characterized. The results indicate that the approach is feasible for molecular characterization of thiophenic and sulfide compounds, which is complementary to recently developed methods for separation and/or ionization of sulfur compounds in petroleum.